Beyond the Lab: fNIRS as a Transformative Tool for Naturalistic Neuroimaging in Clinical Research and Drug Development

Abstract

This article explores the growing application of functional near-infrared spectroscopy (fNIRS) in naturalistic research settings, a paradigm shift from traditional, constrained neuroimaging. Aimed at researchers and drug development professionals, it details how fNIRS's portability, motion tolerance, and ecological validity are unlocking new possibilities for studying brain function during real-world behaviors, from social interactions to cognitive tasks performed at home. We cover the foundational principles enabling this transition, present cutting-edge methodological applications and case studies, address key technical and analytical challenges, and validate fNIRS through comparisons with established modalities like fMRI. The synthesis provides a roadmap for leveraging fNIRS to generate more clinically relevant neural biomarkers and advance precision mental health.

The Rise of Naturalistic Neuroscience: Why fNIRS is a Game-Changer

Functional near-infrared spectroscopy (fNIRS) is revolutionizing neuroimaging by enabling brain activity measurement in real-world settings, effectively bridging the gap between highly controlled laboratory environments and ecologically valid naturalistic contexts. This optical brain monitoring technique uses near-infrared light to estimate cortical hemodynamic activity by measuring changes in oxygenated and deoxygenated hemoglobin concentrations, providing an indirect measure of neural activity [1]. The fundamental shift toward naturalistic design represents a methodological transformation in neuroscience research, moving from restrictive, artificial laboratory setups toward studying brain function during authentic behaviors and social interactions.

The technological advantages of fNIRS make it uniquely suited for naturalistic paradigms. Unlike functional magnetic resonance imaging (fMRI), which requires subjects to remain motionless in a confined scanner, fNIRS is highly portable, wearable, and less sensitive to motion artifacts [2] [3]. This allows researchers to study brain activity during walking, speaking, and social interactions—behaviors that were previously difficult or impossible to investigate with traditional neuroimaging methods. Furthermore, fNIRS provides a superior temporal resolution compared to fMRI, often achieving millisecond-level precision for capturing rapid neural dynamics [3]. These characteristics have positioned fNIRS as the preferred modality for studying the brain in its natural context, opening new frontiers in cognitive neuroscience, developmental psychology, clinical rehabilitation, and social interaction research.

Comparative Advantages of fNIRS for Naturalistic Paradigms

Methodological Strengths and Limitations

The transition to naturalistic design requires understanding the comparative advantages and limitations of different neuroimaging modalities. The table below provides a systematic comparison of fNIRS against other common brain imaging techniques:

Table 1: Comparative analysis of neuroimaging modalities for naturalistic research

Feature	fNIRS	fMRI	EEG
Spatial Resolution	1-3 cm [3]	1-5 mm [3]	1-10 cm
Temporal Resolution	~100 ms [3]	2-4 seconds [3]	<100 ms
Portability	High [4] [2]	None	High
Tolerance to Movement	High [2] [5]	Very Low	Medium
Natural Environment Compatibility	High [4] [2]	None	Medium
Measurement Depth	Superficial cortex (2-3 cm) [3]	Whole brain	Cortical surface
Key Naturalistic Applications	Social interaction, motor execution, speech, developmental studies [4]	Restricted naturalistic stimuli	Limited movement studies

Quantitative Evidence Supporting Naturalistic Applications

The evidence base supporting fNIRS in naturalistic settings has expanded significantly across multiple domains. The following table summarizes key application areas with corresponding experimental findings:

Table 2: Evidence for fNIRS in naturalistic applications across research domains

Research Domain	Experimental Evidence	Practical Advantages
Social Interaction & Hyperscanning	Enabled brain-to-brain synchrony measurement in dyads during cooperation/competition [5]	Measures multiple interacting subjects simultaneously; reveals neural correlates of social behaviors [4] [5]
Speech & Language	41 subjects measured during reading aloud and silent reading tasks [6]	Robust to muscle movements during speech; validates correction algorithms for articulation artifacts [4] [6]
Developmental Science	Successful measurements in infants, toddlers, and children during developmentally appropriate tasks [2]	Tolerant of natural movements; comfortable for sensitive populations; short set-up time [4] [2]
Clinical & Elderly Populations	Applications in Parkinson's, Alzheimer's, stroke rehabilitation, and elderly motor-cognitive assessment [2] [3]	Bedside monitoring capability; suitable for patients unable to tolerate scanner environments [2] [3]
Motor Execution	Study of gait, coordination, and complex motor tasks during actual movement [4]	Compatibility with movement and bioelectric measures; ideal for rehabilitation research [4]

Experimental Protocols for Naturalistic fNIRS Research

Hyperscanning Protocol for Social Interactions

The hyperscanning approach represents one of the most significant advances in naturalistic fNIRS research, enabling the study of brain-to-brain synchrony during social interactions. The following protocol, adapted from parent-child dyad research [5], provides a framework for hyperscanning experiments:

Equipment Preparation:

• Select fNIRS caps appropriate for head circumference (often slightly larger than measured size)
• Create a 3×5 optode grid with 30mm spacing in the forehead area, aligned with the International 10-20 system (Fpz point)
• Mount probe holder grids with soft foam material for comfort and minimal pressure marks
• Prepare two synchronized fNIRS systems with separate probe sets for each participant
• Allow laser diodes to warm up for 30 minutes before measurement for stable operation

Experimental Setup:

• Position participants side-by-side in front of a computer screen with adjustable chin rests
• Measure and mark the Fpz point (10% of nasion-inion distance) on each participant's head
• Place caps ensuring the middle probe of the bottom row is positioned on Fpz
• Secure fiber strings on holder arms to prevent pulling on caps
• Advance probes until scalp contact is indicated by the spring mechanism
• Test signal quality using the Auto Gain function and adjust problematic channels by repositioning hair or adjusting probe depth

Task Implementation:

• Adapt cooperative/competitive computer tasks originally developed by Cui et al. [5]
• For cooperation: Participants respond jointly via synchronized button presses
• For competition: Participants attempt to respond faster than their partner
• Use a towel to obscure hand movements from view
• Implement practice trials before data collection

Data Collection Parameters:

• Set device to event-related measurement mode
• Activate RS232 serial input for receiving triggers from the experimental paradigm
• Acquire data at sampling frequencies ≥10 Hz to adequately capture hemodynamic responses [5]

Protocol for Speech Production Studies

Speech production research with fNIRS requires specialized methodologies to address the unique challenges of articulation artifacts. The following protocol is validated across 50 healthy subjects [6]:

Experimental Design:

• Implement randomized block designs with multiple conditions:
  • Jaw movement (JM) tasks: 5-second JM followed by 5-second rest (isolates articulation artifacts)
  • Reading aloud (RA): 10-second reading followed by 10-second rest
  • Silent reading (SR): 10-second silent reading followed by 10-second rest
• Use standardized texts at third- or fourth-grade reading level to minimize cognitive load variations
• Include sufficient repetitions (typically 10) for statistical power

fNIRS Configuration:

• Employ a cap system with 16 sources (760nm and 850nm LEDs) and 16 detectors
• Configure 32 source-detector combinations at 3cm distance for cortical measurement
• Include 2 source-detector pairs at 0.8cm distance as short channels for superficial signal regression
• Position optodes to cover frontal, temporal, and parietal lobes relevant for language processing
• Digitize optode positions using a commercial digitizer for precise localization

Motion Artifact Correction:

• Apply a hybrid motion-correction algorithm sequentially using:
  • Spline interpolation for baseline drift correction
  • Wavelet filtering for high-frequency artifact removal
• Validate algorithm performance by comparing RA and SR conditions after correction
• Regress out extracortical contributions using short-separation channels

Signal Quality Control:

• Compute signal-to-noise ratio (SNR) as mean intensity divided by standard deviation
• Discard channels with SNR <8 as excessively noisy
• Convert light intensity to optical density before motion correction
• Apply modified Beer-Lambert law with differential pathlength factor of 6 for hemoglobin concentration calculation

Technical Implementation and Signal Processing

fNIRS Operating Principles and Equipment

Understanding the technical fundamentals of fNIRS operation is essential for designing effective naturalistic experiments. The following research toolkit outlines key components:

Table 3: Research toolkit for naturalistic fNIRS experiments

Component	Function	Technical Specifications
fNIRS Console	Light source control and signal detection	Continuous wave (CW), frequency domain (FD), or time domain (TD) systems [1]
Optodes	Light emission and detection on scalp	Sources: LEDs or lasers (760nm, 850nm); Detectors: photodiodes [6]
Head Caps/Bands	Secure optode placement and positioning	Neoprene material for light shielding; ergonomic fit; various sizes [2]
Short-Separation Detectors	Measure extracortical signals for regression	Typically 0.8cm source-detector distance [6]
Auxiliary Equipment	Enable synchronization and multimodal recording	Response buttons, eye-trackers, motion capture, physiological monitors

fNIRS operation relies on the principle that biological tissues are relatively transparent to near-infrared light (650-950nm), while hemoglobin compounds are strong absorbers in this spectrum [1]. The technique utilizes the modified Beer-Lambert law to relate light attenuation changes to hemoglobin concentration variations:

OD = ln(I₀/I) = ε·[X]·l·DPF + G

Where OD represents optical density, I₀ and I are incident and detected light intensities, ε is the extinction coefficient, [X] is chromophore concentration, l is source-detector distance, DPF is the differential pathlength factor, and G is a geometry-dependent factor [1]. Using multiple wavelengths allows calculation of both oxygenated hemoglobin (HbO) and deoxygenated hemoglobin (HbR) concentration changes.

Workflow for Naturalistic fNIRS Data Acquisition and Processing

The following diagram illustrates the comprehensive workflow for naturalistic fNIRS experiments, from experimental design to data interpretation:

Diagram 1: Comprehensive fNIRS naturalistic research workflow

Advanced Signal Processing for Naturalistic Environments

Naturalistic environments present unique signal processing challenges, particularly regarding motion artifacts and physiological confounds. The hybrid motion correction algorithm validated for speech protocols has demonstrated 94% effectiveness in removing jaw movement artifacts [6]. This approach combines:

• Spline interpolation for identifying and correcting motion artifacts based on outlier detection
• Wavelet filtering for removing high-frequency noise while preserving hemodynamic signals

For hyperscanning applications, advanced analytical methods include:

• Wavelet coherence analysis for measuring brain-to-brain synchrony between dyads
• Random pair analysis for validating observed synchrony against chance levels
• Inter-subject correlation for measuring shared neural responses during naturalistic stimuli

The integration of short-separation channels (typically 0.8cm source-detector distance) enables regression of superficial physiological artifacts, significantly improving signal quality by removing components originating from scalp blood flow [6].

Future Directions and Implementation Guidelines

Emerging Trends in Naturalistic fNIRS

The field of naturalistic fNIRS research continues to evolve with several promising directions:

Multimodal Integration: Combining fNIRS with other imaging modalities creates powerful synergistic approaches. Simultaneous fMRI-fNIRS measurement leverages fMRI's high spatial resolution for deep brain structures alongside fNIRS's temporal resolution and portability for naturalistic paradigms [3]. This integration helps validate fNIRS signals against the established fMRI BOLD response and provides more comprehensive brain activity mapping.

Wearable Technology Advances: Ongoing miniaturization of fNIRS systems enables increasingly unobtrusive monitoring. Current wireless systems weigh under 300g, allowing extended measurement during truly natural behaviors [2]. Future developments will likely include dry optode technologies that further reduce setup time and participant discomfort.

Standardized Protocols: The establishment of standardized processing pipelines, such as the hybrid motion correction for speech protocols, will enhance reproducibility and cross-study comparisons [6]. Community-wide efforts to create shared analysis frameworks and reporting standards are critical for advancing the field.

Practical Implementation Recommendations

Successful implementation of naturalistic fNIRS research requires attention to several practical considerations:

Population-Specific Adaptations:

• For infant studies: Use specialized systems like BabyBrite with flat optode tips and soft, biocompatible holders [2]
• For elderly populations: Consider potential mobility limitations in paradigm design
• For clinical populations: Adapt protocols to accommodate specific symptoms or limitations

Experimental Design Considerations:

• Balance ecological validity with experimental control through careful task design
• Include appropriate control conditions (e.g., silent reading vs. reading aloud)
• Implement adequate practice trials to minimize novelty effects
• Consider block designs versus event-related designs based on research questions

Signal Quality Assurance:

• Conduct thorough signal quality checks before data collection
• Implement real-time quality monitoring during acquisition
• Establish predetermined criteria for channel exclusion
• Document signal quality metrics for inclusion in methodological reporting

The transition to naturalistic design represents not merely a technical shift but a conceptual transformation in how we study the human brain. By embracing the unique capabilities of fNIRS to measure brain function during real-world behaviors and social interactions, researchers can address fundamental questions about human cognition, communication, and behavior with unprecedented ecological validity.

Functional Near-Infrared Spectroscopy (fNIRS) is a non-invasive neuroimaging technique that measures cerebral hemodynamics and oxygenation changes related to brain activity. The fundamental principle enabling fNIRS is neurovascular coupling, the physiological mechanism where increased neuronal activity triggers localized changes in cerebral blood flow, blood volume, and oxygen metabolism [7]. When a brain region becomes active, a complex cascade of vascular events occurs: oxygen consumption initially increases, followed by a substantial increase in cerebral blood flow (CBF) that overshoots the metabolic demand. This hemodynamic response results in characteristic changes in oxygenated and deoxygenated hemoglobin concentrations—specifically, an increase in oxygenated hemoglobin ([O₂Hb]) and a decrease in deoxygenated hemoglobin ([HHb]) in the active area [7]. fNIRS leverages these predictable hemodynamic changes to indirectly monitor neural activation patterns through optical measurements, providing a unique window into brain function with advantages including portability, motion tolerance, and applicability in naturalistic settings [8] [9].

Biophysical and Technical Foundations

The Optical Window and Light-Tissue Interaction

fNIRS technology is possible because biological tissues exhibit relative transparency to light in the near-infrared spectrum (approximately 650-950 nm), creating what is known as the "optical window" [7]. Within this range, light can penetrate biological tissues, including the scalp, skull, and brain, to depths of several centimeters. The two primary chromophores in neural tissue that absorb near-infrared light are oxyhemoglobin (Oâ‚‚Hb) and deoxyhemoglobin (HHb), which have distinct absorption spectra [7]. By measuring how much light is absorbed at different wavelengths, fNIRS can quantify changes in the concentration of these hemoglobin species.

The depth of light penetration into brain tissue is determined by the separation distance between the light source and detector, with a general rule of thumb being maximum depth (zₘₐₓ) equals half the source-detector distance (d/2) [7]. Typical source-detector separations of 3-4 cm enable sampling of cortical brain regions, making fNIRS particularly suitable for investigating outer layers of the cerebral cortex.

fNIRS Measurement Modalities

Three primary technical implementations of fNIRS exist, each with distinct advantages and limitations:

Table 1: fNIRS Measurement Modalities

Method	Technical Approach	Key Capabilities	Limitations
Continuous Wave (CW-fNIRS)	Measures light attenuation using constant intensity light sources	High sampling rate, cost-effective, simple implementation	Measures relative absorption changes only; cannot separate absorption from scattering
Frequency Domain (FD-fNIRS)	Uses amplitude-modulated light to measure phase shift and amplitude attenuation	Can separate absorption and scattering properties; provides better depth resolution	More complex implementation than CW-fNIRS
Time Domain (TD-fNIRS)	Emplies short light pulses to measure temporal point spread function	Best depth resolution; can separate absorption and scattering; can resolve deeper tissue layers	Technically complex; expensive; lower sampling rate

Continuous-wave systems are most commonly used in cognitive neuroscience applications due to their practical implementation, cost-effectiveness, and sufficient capability for measuring task-related hemodynamic changes [7]. The hemodynamic parameters derived from fNIRS measurements include concentration changes of oxygenated hemoglobin (Δ[O₂Hb]), deoxygenated hemoglobin (Δ[HHb]), total hemoglobin (Δ[tHb] = Δ[O₂Hb] + Δ[HHb]), and tissue oxygen saturation (StO₂ = 100 × [O₂Hb]/[tHb]) [7].

Experimental Design and Methodological Considerations

Research Paradigms and Naturalistic Approaches

fNIRS enables diverse experimental designs, from traditional block and event-related paradigms to innovative naturalistic approaches. The technology's portability and motion tolerance make it particularly valuable for studying brain function in ecologically valid settings that closely resemble real-world environments [8]. Recent research has demonstrated fNIRS applications in various naturalistic contexts, including:

• Social interactions and hyperscanning: Simultaneously measuring brain activity from multiple individuals during social exchanges, cooperation, or competition [5]
• Classroom and educational settings: Investigating neural correlates of learning and engagement in authentic educational environments [8]
• Daily life activities: Monitoring prefrontal cortex activity during technology use, including social media consumption [9]
• Motor tasks in realistic conditions: Studying complex motor coordination outside laboratory constraints [8]

These naturalistic approaches address limitations of traditional neuroimaging methods that require strict physical constraints and highly controlled environments, potentially limiting the generalizability of findings to real-world contexts [8].

fNIRS Hyperscanning Protocol

Hyperscanning—simultaneous brain recording from multiple interacting individuals—has emerged as a powerful approach for studying social interactions [5]. The following protocol outlines key steps for conducting fNIRS hyperscanning experiments:

• Cap Preparation and Optode Placement

  • Select appropriate cap sizes based on head circumference
  • Arrange optodes in a standardized configuration (e.g., 3×5 grid on forehead for prefrontal measurements)
  • Ensure proper optode-scalp coupling through visual inspection and signal quality checks
  • Align the middle probe of the bottom row with the Fpz position (10-20 system)

• Signal Quality Optimization

  • Power on the fNIRS system 30 minutes before measurements to ensure stable operation
  • Use the system's Auto Gain function to assess signal quality
  • Adjust optode positioning or hair displacement to improve problematic channels
  • Modify signal intensity settings for channels with insufficient or excessive signal

• Experimental Task Implementation

  • Implement appropriate dyadic tasks (e.g., cooperative games, competitive tasks)
  • Ensure proper synchronization between fNIRS systems and task paradigms
  • Control for potential confounds (e.g., visual obstructions between participants)
  • Record trigger signals synchronized with task events

• Data Acquisition and Export

  • Acquire data simultaneously from all participants
  • Monitor signal quality throughout the experiment
  • Export raw light intensity data for subsequent processing and analysis [5]

This protocol can be adapted for various dyadic constellations (parent-child, romantic partners, strangers) and different experimental tasks to address diverse research questions in social neuroscience.

Data Processing and Analysis Workflows

Standard fNIRS Preprocessing Pipeline

Proper data processing is essential for extracting meaningful neural signals from fNIRS data. The following workflow outlines standard preprocessing steps:

Diagram 1: fNIRS Data Preprocessing Workflow

• Raw Intensity to Optical Density: Convert raw light intensity measurements to optical density, which is more linearly related to chromophore concentration [10]
• Signal Quality Assessment: Evaluate signal quality using metrics like the Scalp Coupling Index (SCI) to identify channels with poor optode-scalp contact
• Channel Selection: Remove channels with source-detector distances too short to penetrate cortical tissue (typically <1 cm) [10]
• Hemoglobin Conversion: Apply the Modified Beer-Lambert Law to convert optical density changes to relative concentrations of oxygenated (HbO) and deoxygenated hemoglobin (HbR) [10]
• Physiological Noise Filtering: Use bandpass filtering (typically 0.05-0.7 Hz) to remove cardiac pulsation (∼1 Hz) and very low-frequency drifts [10]
• Epoching: Segment data into trials time-locked to experimental events for subsequent analysis [10]

Advanced Analysis Approaches

For more sophisticated analysis, particularly in single-trial classification or brain-computer interface applications, the General Linear Model (GLM) approach provides enhanced performance [11]. The GLM simultaneously estimates the task-evoked hemodynamic response while removing confounding signals through nuisance regressors:

Table 2: Components in General Linear Model for fNIRS Analysis

Component	Description	Purpose
Task Regressor	Model of expected hemodynamic response (e.g., canonical HRF)	Capture brain activity related to experimental tasks
Short-Separation Regressors	Signals from short source-detector distance channels	Remove systemic physiological noise originating from superficial layers
Physiological Regressors	Measurements of heart rate, respiration, blood pressure	Account for cardiac, respiratory, and Mayer wave influences
Motion Regressors	Parameters derived from motion artifact detection algorithms	Mitigate effects of head movement on signal quality

The GLM approach significantly improves contrast-to-noise ratio and provides more accurate single-trial estimates of brain activity compared to conventional preprocessing pipelines [11]. Correct implementation within cross-validation frameworks is essential to avoid overfitting when used for classification tasks.

The Scientist's Toolkit: Essential Research Reagents and Materials

Table 3: Essential Materials for fNIRS Research

Item	Specification	Function/Purpose
fNIRS System	Continuous-wave, frequency-domain, or time-domain system	Main acquisition unit for measuring light attenuation through tissue
Optodes	Light source and detector pairs (typically LEDs and photodiodes)	Emit near-infrared light into tissue and detect back-scattered light
Headgear	EEG caps with modified holder grids or custom-designed fNIRS caps	Hold optodes in stable positions on scalp with consistent source-detector distances
Calibration Phantoms	Tissue-simulating materials with known optical properties	System validation and performance characterization before human measurements
Short-Separation Detectors	Additional detectors at short distances (∼0.5 cm) from sources	Measure superficial signals for regressing out systemic physiological noise
Auxiliary Physiological Monitors	ECG, respiration belt, blood pressure monitor	Record physiological parameters for noise regression in GLM analysis
Stimulus Presentation Software	PsychToolbox, Presentation, E-Prime	Deliver controlled experimental paradigms with precise timing
Data Analysis Software	Homer2/3, NIRS Toolbox, nirsLAB, MNE-Python, custom scripts	Process raw fNIRS data and perform statistical analysis
7-Bromo-6-methylindoline-2,3-dione	7-Bromo-6-methylindoline-2,3-dione|CAS 882679-16-5	7-Bromo-6-methylindoline-2,3-dione (CAS 882679-16-5). A high-purity isatin-based building block for research. For Research Use Only. Not for human or veterinary use.
2-[(4-Bromobut-2-en-1-yl)oxy]oxane	2-[(4-Bromobut-2-en-1-yl)oxy]oxane|C9H15BrO2

Application in Naturalistic Research: Case Study

A recent naturalistic fNIRS study examined the immediate impact of social media use on executive functioning in college students [9]. Researchers used a wearable fNIRS system to monitor prefrontal cortex activity while participants engaged with social media in an environment resembling typical daily use conditions. The study revealed that brief social media exposure led to:

• Reduced accuracy in executive function tasks (n-back and Go/No-Go paradigms)
• Altered prefrontal activation patterns, including increased medial PFC activity (suggesting greater cognitive effort) and decreased dorsolateral and ventrolateral PFC activation (reflecting working memory and inhibition impairments)
• Behavioral correlates demonstrating the cognitive costs of social media use

This case study illustrates how wearable fNIRS technology enables investigation of neurocognitive processes in naturalistic settings, providing ecological validity that complements controlled laboratory studies [9].

fNIRS represents a powerful neuroimaging modality that leverages neurovascular coupling and the optical properties of biological tissues to investigate brain function. Its unique combination of portability, relatively high temporal resolution, and tolerance for movement makes it particularly valuable for studying cognitive processes in naturalistic contexts. As technological advances continue to improve wearable fNIRS systems and analytical methods become more sophisticated, this approach promises to bridge the gap between highly controlled laboratory studies and real-world brain function, offering new insights into the neural bases of human behavior in ecologically valid settings.

Functional Near-Infrared Spectroscopy (fNIRS) has emerged as a powerful neuroimaging tool that effectively bridges the gap between laboratory research and real-world brain monitoring. Its unique combination of portability, cost-effectiveness, and significant tolerance to motion artifacts enables researchers to study brain function in naturalistic settings and across diverse populations. This application note details the technical foundations, experimental protocols, and practical implementations of fNIRS that facilitate its growing application in cognitive neuroscience, clinical diagnostics, and pharmaceutical development outside traditional laboratory confines. By providing structured protocols and empirical data comparisons, we establish a framework for leveraging fNIRS in ecologically valid research designs central to advancing naturalistic research paradigms.

Functional Near-Infrared Spectroscopy (fNIRS) is a non-invasive optical neuroimaging technique that measures cerebral hemodynamic activity by quantifying changes in oxygenated (HbO) and deoxygenated hemoglobin (HbR) concentrations [12]. The technology exploits the differential absorption properties of biological chromophores in the near-infrared spectrum (650-950 nm), which readily penetrates biological tissues [13]. As a hemodynamic-based modality, fNIRS shares a common physiological basis with functional magnetic resonance imaging (fMRI) but offers distinct practical advantages that make it particularly suitable for real-world settings [14] [15]. The core value proposition of fNIRS for naturalistic design research rests on three foundational pillars: its inherent portability, substantially lower operational costs compared to fMRI, and robust tolerance to motion artifacts—each of which will be systematically explored in this application note with supporting experimental evidence and implementation protocols.

Core Technical Advantages

Portability and Operational Flexibility

The portability of fNIRS systems stems from their fundamental technical architecture, which utilizes compact light sources (LEDs or lasers) and detectors that can be mounted on flexible headgear rather than requiring a fixed, massive infrastructure [16]. This enables neuroimaging across diverse environments previously inaccessible to traditional brain imaging methods.

Table 1: fNIRS Portability-Enabled Research Applications

Application Domain	Research Setting	fNIRS System Advantage
Pediatric Neuroscience	Home, nursery, or school environments	Enables developmental studies in natural contexts; well-tolerated by infants and children [13]
Psychiatric Monitoring	Clinical wards, outpatient settings, patient homes	Allows for longitudinal assessment of treatment response in real-world conditions [16]
Neuroergonomics	Workplace environments, simulated operational settings	Assesses cognitive workload during actual task performance [17]
Motor Rehabilitation	Therapy clinics, home-based training environments	Monitors brain activity during physical therapy and motor learning [12]

Modern wearable fNIRS platforms exemplify this portability advantage. Recent technological advances have yielded wireless, self-applied fNIRS systems that participants can use with minimal supervision after brief training [16]. These systems incorporate augmented reality guidance for proper device placement and cloud-based data management, enabling large-scale studies in participants' natural environments. This capability for dense sampling—collecting substantial data across multiple sessions—is crucial for establishing reliable individual-level brain activity patterns rather than relying solely on group averages [16].

Cost-Effectiveness

The economic advantage of fNIRS relative to established neuroimaging modalities like fMRI represents a significant factor in its adoption for large-scale or resource-constrained research programs.

Table 2: Comparative Cost Analysis of Neuroimaging Modalities

Imaging Modality	System Cost	Operational Expenses	Infrastructure Requirements	Accessibility
fNIRS	Low to moderate	Low	Standard lab/clinical space	High
fMRI	Very high	Very high	Specialized shielded room, magnetic containment	Limited
EEG	Low	Low	Standard lab/clinical space	High
PET	Very high	Very high	Cyclotron facility, radiochemistry lab	Very limited

fNIRS systems are relatively cost-effective compared to other neuroimaging technologies, particularly fMRI [12] [17]. The affordability of equipment and lower associated operational costs make fNIRS an accessible option for researchers and clinicians, especially in resource-limited settings [12]. This cost profile enables higher participant throughput and facilitates longitudinal study designs with multiple measurement sessions, which is particularly valuable for tracking intervention effects or developmental trajectories in both basic research and clinical trials [16].

Motion Tolerance and Artifact Correction

A critical advantage of fNIRS in naturalistic settings is its relative tolerance to motion artifacts compared to fMRI [14] [13]. While motion can affect signal quality, several algorithmic approaches have been developed to effectively identify and correct for motion artifacts, preserving data integrity without requiring complete trial rejection.

Table 3: Motion Artifact Correction Techniques in fNIRS

Correction Method	Underlying Principle	Performance Efficacy	Implementation Complexity
Wavelet-Based Filtering	Decomposes signal using wavelet transforms and zeros coefficients representing artifacts	Highest performance; corrects 93% of artifacts in cognitive tasks [18]	Moderate
Correlation-Based Signal Improvement (CBSI)	Leverages negative correlation between HbO and HbR during neural activity	Effective for large spikes and baseline shifts; fully automatable [19]	Low
WCBSI (Combined Approach)	Integrates wavelet and correlation-based methods	Superior performance across all metrics; highest probability as best-ranked algorithm (78.8%) [19]	Moderate to High
Spline Interpolation	Identifies artifact segments and fits spline functions for subtraction	Effective but depends on accurate artifact detection [19]	Moderate
Principal Component Analysis (PCA)	Decomposes signal into components and removes those representing motion	Can over-correct signal; performance depends on measurement availability [19]	Moderate

The WCBSI algorithm (wavelet and correlation-based signal improvement) represents a particularly advanced motion correction approach, combining the strengths of multiple techniques. In comparative studies, WCBSI was the only method exceeding average performance across all evaluation metrics and had the highest probability (78.8%) of being the best-ranked algorithm [19]. This robust motion tolerance enables research with populations that typically present significant challenges for fMRI, including infants, children, and individuals with neurological or psychiatric conditions that may involve involuntary movements [13] [16].

Experimental Protocols for Naturalistic Settings

Protocol 1: At-Home Prefrontal Cortex Monitoring

Application Context: This protocol enables longitudinal assessment of prefrontal cortex (PFC) function in participants' homes, suitable for pharmaceutical trials evaluating cognitive effects of neuropsychiatric medications or monitoring disease progression in neurodegenerative disorders.

Materials and Equipment:

• Wireless, multichannel fNIRS system (e.g., NIRSport2, Cortivision Photon)
• Tablet with augmented reality guidance application for optode placement
• HIPAA-compliant cloud storage infrastructure
• Cognitive task battery (e.g., N-back, Flanker, Go/No-Go tasks)

Procedure:

• Device Configuration: Utilize a headband configuration with sources and detectors covering dorsolateral, ventrolateral, and frontopolar prefrontal regions, with optional short-distance detectors for extracerebral signal correction.
• Participant Training: Conduct initial supervised session to train participants on self-applying the fNIRS headband using AR guidance for reproducible placement.
• Data Acquisition: Implement block-design tasks (e.g., 7-minute N-back tasks) with simultaneous fNIRS recording at sampling rates ≥5 Hz.
• Quality Control: Apply automated signal quality checks (SNR >15 dB) before full task initiation.
• Data Processing: Implement WCBSI motion correction, bandpass filtering (0.01-0.2 Hz), and conversion to hemoglobin concentration changes using the modified Beer-Lambert law.

Validation Metrics: Test-retest reliability assessed via intraclass correlation coefficients (ICCs); within-participant consistency across sessions; differentiation from group-level patterns [16].

Protocol 2: Drug Craving Assessment in Substance Use Disorders

Application Context: Investigation of orbitofrontal cortex (OFC) activation patterns in response to drug cues across different substance abuse populations (e.g., methamphetamine, heroin, mixed drugs).

Materials and Equipment:

• High-density fNIRS system (e.g., NIRSIT with 204 channels)
• Stimulus presentation system for craving induction paradigm
• Clinical assessment scales for craving quantification

Procedure:

• Experimental Design: Implement block design with resting state and drug cue exposure conditions.
• Optode Placement: Focus on comprehensive frontal coverage with specific attention to OFC regions (channels 14-16, 29-32, 46-48).
• Data Acquisition: Collect continuous hemodynamic data during 5-minute baseline and 5-minute cue exposure blocks.
• Signal Processing: Apply wavelet-based motion correction, extract HbO and HbR concentration changes from OFC regions.
• Data Analysis: Employ machine learning classifiers (LDA, SVM) to differentiate substance abuse patterns based on hemodynamic responses [20].

Validation Metrics: Classification accuracy of drug abuse types; statistical significance of OFC activation differences between groups; correlation between HbO activation and clinical craving measures [20].

Technical Specifications and Research Reagent Solutions

Table 4: Essential fNIRS Research Components and Their Functions

Component Category	Specific Examples	Technical Function	Research Application
Light Sources	VCSEL lasers (780 nm, 850 nm), LEDs	Emit near-infrared light at specific wavelengths	Determines penetration depth and chromophore discrimination [20]
Detectors	Silicon photodiodes (SiPD), Avalanche photodiodes	Measure attenuated light intensity after tissue passage	Determines signal-to-noise ratio and sensitivity [15]
Optode Configurations	Standard caps (3 cm separation), short-distance detectors (8 mm)	Define measurement channels and spatial resolution	Short-distance detectors enable superficial signal regression [15]
Signal Processing Algorithms	WCBSI, Spline interpolation, tPCA	Correct motion artifacts and enhance signal quality	Enable data retention in movement-rich naturalistic settings [19]
Wearable Platforms	Wireless headbands, Portable amplifiers	Enable untethered data collection in real-world environments	Facilitate ecological momentary assessment [16]
Core Analytical Metrics	HbO/HbR concentration changes, Hemodynamic response function	Quantify neurovascular coupling related to neural activity	Provide biomarkers for cognitive state and clinical status [12] [13]

Discussion and Implementation Considerations

Spatial Resolution and Depth Limitations

While fNIRS offers significant advantages for naturalistic research, researchers must consider its technical constraints. The spatial resolution of fNIRS is fundamentally limited by optode separation distance, typically providing resolution on the scale of centimeters rather than millimeters [12]. Additionally, the penetration depth of near-infrared light restricts measurement to the cerebral cortex, with limited access to subcortical structures [12]. These limitations necessitate careful experimental design and interpretation of results within the context of fNIRS capabilities.

Signal Contamination and Individual Variability

fNIRS signals can be contaminated by systemic physiological noise (e.g., cardiac pulsation, respiration, blood pressure changes) and extracerebral hemodynamics [12]. Advanced analysis techniques, including short-distance channel regression and principal component analysis, can mitigate these confounding factors. Furthermore, individual differences in scalp and skull anatomy affect optical pathlength and signal strength, requiring appropriate normalization procedures for group-level analyses [12].

Multimodal Integration Strategies

The combination of fNIRS with complementary neuroimaging modalities represents a powerful approach for naturalistic research. Simultaneous fNIRS-EEG recording capitalizes on the high temporal resolution of EEG and the superior spatial localization of fNIRS [13]. Integration with eye-tracking and physiological monitoring provides comprehensive assessment of cognitive state and autonomic function during real-world tasks [21]. These multimodal approaches enhance the interpretative power of fNIRS data in complex, ecologically valid environments.

fNIRS technology has matured into a robust neuroimaging platform that effectively addresses the critical challenges of naturalistic research settings. Its core advantages—unmatched portability, compelling cost-effectiveness, and resilient motion tolerance—enable neuroscientific investigation and clinical assessment in real-world contexts previously inaccessible to conventional brain imaging methods. The experimental protocols and technical specifications detailed in this application note provide researchers with practical frameworks for implementing fNIRS across diverse settings, from at-home cognitive monitoring to clinical assessment of neurological disorders. As the field advances, ongoing developments in wearable technology, motion correction algorithms, and multimodal integration will further expand the frontiers of fNIRS applications, ultimately enhancing our understanding of brain function in ecologically valid contexts and accelerating the development of novel therapeutic interventions.

Functional Near-Infrared Spectroscopy (fNIRS) is a neuroimaging technique that leverages near-infrared light to non-invasively measure cortical hemodynamic activity associated with neural firing. The technology operates on the principle that biological tissues are relatively transparent to light in the 700-900 nm range, while hemoglobin compounds are strong absorbers within this spectrum [1]. By measuring light attenuation after it passes through cerebral tissue, fNIRS can estimate changes in oxyhemoglobin (HbO) and deoxyhemoglobin (HbR) concentrations, providing an indirect measure of brain activity [22]. The field has evolved substantially since Jobsis's pioneering work in 1977, with three primary spectroscopic techniques emerging: Continuous Wave (CW), Frequency Domain (FD), and Time Domain (TD) fNIRS [23] [1].

Each technique employs distinct methods for illuminating tissue and interpreting transmitted light, creating a technical spectrum with significant trade-offs between information content, system complexity, cost, and practical applicability. CW-fNIRS, the most widespread approach, utilizes constant amplitude light sources and measures attenuation to calculate relative hemoglobin concentration changes [23]. FD-fNIRS employs intensity-modulated light sources to measure both attenuation and phase shift, offering deeper insight into photon path length and tissue properties [23]. TD-fNIRS, the most technically complex approach, uses short photon pulses and time-resolved detection to directly measure photon time-of-flight, providing the highest depth sensitivity and potential for absolute quantification [23] [24].

For naturalistic research settings—which encompass studies conducted in realistic environments with moving participants, such as educational settings, clinical contexts, or sports performance—the choice between these techniques carries profound implications. Naturalistic paradigms prioritize ecological validity but introduce significant methodological challenges including motion artifacts, ambient light interference, and complex physiological noise [22] [25]. This application note delineates the technical specifications, comparative performance, and implementation protocols for CW, FD, and TD fNIRS within naturalistic research contexts.

Technical Specifications and Comparative Analysis

The selection of an appropriate fNIRS technique requires careful consideration of technical specifications against research requirements. The table below provides a quantitative comparison of the three primary fNIRS modalities across critical parameters.

Table 1: Technical Comparison of CW, FD, and TD fNIRS Modalities

Parameter	Continuous Wave (CW)	Frequency Domain (FD)	Time Domain (TD)
Basic Principle	Constant amplitude light source; measures intensity attenuation [23]	Intensity-modulated light (~100 MHz); measures attenuation, phase shift, and modulation depth [23]	Short light pulses (~100 ps); measures temporal distribution of transmitted photons [23] [24]
Information Content	Relative concentration changes of HbO and HbR [23]	Absolute quantification possible; distinguishes scattering and absorption [23]	Absolute quantification possible; depth resolution via time-gating [24]
Sensitivity to Layers	Mixed sensitivity (superficial and deep) [24]	Improved depth sensitivity via phase information [23]	High depth selectivity; sensitive to brain layers [24]
Temporal Resolution	High (equivalent to sampling rate) [23]	High (equivalent to sampling rate) [23]	Moderate (limited by time-gating acquisition) [24]
Spatial Resolution	~2-3 cm (depth penetration ~1.5 cm) [23]	~2-3 cm (depth penetration ~1.5 cm) [23]	~1-2 cm (better depth localization) [24]
Cost	Low [23]	High [23]	Very High [23]
System Complexity	Low [23]	Moderate to High [23]	Very High [23]
Portability	High (wearable systems available) [22]	Moderate (typically benchtop systems) [23]	Low (typically bulky systems) [23]
Brain Specificity	Low without short-separation channels [24]	Moderate [23]	High [24]
Motion Artifact Robustness	Moderate [25]	Moderate [23]	Lower (sensitive to optode displacement) [23]
Commercial Availability	Widely available [23]	Limited [23]	Very limited [23]

CW-fNIRS dominates the commercial landscape due to its favorable balance of cost, simplicity, and performance [23]. Its limitations in distinguishing absorption from scattering effects and inability to provide absolute quantification are offset by robustness, high channel count capabilities, and relatively straightforward data analysis pipelines. The technique's susceptibility to superficial hemodynamic changes can be mitigated through implementation of short-separation channels (typically 8mm source-detector pairs) that regress out scalp contributions [26] [24].

FD-fNIRS offers enhanced discrimination between absorption and scattering coefficients through phase shift measurements, potentially detecting fast optical signals related to neuronal swelling (Event-Related Optical Signals) [23]. However, this comes with increased instrumental complexity, higher cost, and reduced portability—significant constraints in naturalistic settings.

TD-fNIRS represents the premium approach for depth-resolved brain imaging, with recent advancements producing more compact, high-channel-count systems [24]. Temporal moment analysis (particularly higher moments like variance) demonstrates enhanced sensitivity to cerebral layers compared to intensity measurements alone [24]. One study reported that TD moment analysis improved HRF recovery correlations by 98% for HbO and 48% for HbR compared to CW-GLM approaches [24]. Despite these advantages, TD systems remain prohibitively expensive for many research settings and present substantial analytical challenges.

Table 2: Performance Metrics in Naturalistic Settings

Metric	CW-fNIRS	FD-fNIRS	TD-fNIRS
Typical HRF Recovery Correlation (HbO)	Baseline	~20-40% improvement over CW [23]	~98% improvement over CW [24]
Typical HRF Recovery Correlation (HbR)	Baseline	~10-30% improvement over CW [23]	~48% improvement over CW [24]
Recovery RMSE	Baseline	~20-30% reduction [23]	~56% reduction (HbO), ~52% reduction (HbR) [24]
Tolerance to Movement	High [22] [25]	Moderate [23]	Low to Moderate [23]
Setup Time	Short [22]	Moderate to Long [23]	Long [23]
Analysis Complexity	Low to Moderate [23]	Moderate to High [23]	High [24]

Experimental Protocols for Naturalistic Research

Protocol for CW-fNIRS in Naturalistic Settings

CW-fNIRS offers the most practical implementation path for naturalistic study designs, particularly those involving participant movement, child populations, or real-world environments [22].

Apparatus and Reagents:

• CW-fNIRS System: A continuous wave fNIRS instrument with minimum 2 wavelengths (typically 690nm and 830nm) [23] [1].
• Optode Caps: Flexible headgear with source-detector distance of 30mm for adult cortical measurements; 15-20mm for infant studies [22].
• Short-Separation Channels: Additional detectors at 8mm from sources for superficial signal regression [26] [24].
• Auxiliary Monitoring: Physiological sensors (heart rate, respiration) for confounding regression [27].
• Stimulus Presentation System: Portable computing device for task delivery in naturalistic contexts [22].
• Data Acquisition Software: Configured for appropriate sampling rate (typically 10-50Hz) [27].

Procedure:

• System Setup and Calibration: Power on the CW-fNIRS system and allow 15 minutes for lamp stabilization. Perform reference measurements using calibration phantoms with known optical properties if available.
• Participant Preparation: Measure head circumference and select appropriate cap size. Mark vertex (Cz) according to the International 10-20 system for reproducible placement [1].
• Optode Placement: Position optodes on the scalp according to predetermined montage targeting regions of interest. Ensure good optical contact through hair separation and verify coupling quality via real-time signal inspection.
• Signal Quality Verification: Check for cardiac pulsations (~1Hz) in the raw intensity signals indicating adequate optode-scalp coupling [27].
• Experimental Task Administration: Implement block, event-related, or naturalistic interactive paradigm. For naturalistic designs, incorporate tasks that allow movement within constraints of signal reliability.
• Data Acquisition: Record continuous fNIRS data throughout task performance with synchronization triggers marking experimental events.
• Data Export: Convert raw data to optical density (if not automated) for subsequent processing.

Naturalistic Adaptation Notes:

• For mobile paradigms, utilize wearable CW systems with wireless data transmission [22].
• In child studies, employ brief task periods with engaging stimuli to maintain participation [22].
• For clinical populations with movement disorders, incorporate more flexible optode mounting and motion-tolerant analysis approaches [22].

Protocol for TD-fNIRS in Controlled Naturalistic Settings

TD-fNIRS protocols offer superior depth resolution but require more constrained implementation, suitable for stationary naturalistic tasks such as immersive virtual reality or complex cognitive activities without significant head movement.

Apparatus and Reagents:

• TD-fNIRS System: Time-domain system with picosecond pulsed lasers and time-correlated single photon counting detection [24].
• High-Density Optode Arrays: Arrangements with multiple source-detector separations (e.g., 20mm, 30mm, 40mm) for depth profiling [24].
• Time-Gating Equipment: Hardware/software for temporal moment analysis (zeroth, first, and second moments) [24].
• Auxiliary fNIRS Module: Short-separation channel array (8mm) for systemic physiological noise regression [24].
• Computational Resources: High-performance computing for Monte Carlo simulations and temporal data processing [24].

Procedure:

• System Initialization: Power on TD-fNIRS system and allow laser sources to stabilize (typically 30 minutes). Perform instrumental response function (IRF) measurement using a reflective phantom.
• Participant Preparation and Positioning: Position participant comfortably in a setting that mimics natural context (e.g., viewing realistic stimuli, handling objects). Ensure minimal movement capacity while maintaining ecological validity of tasks.
• Optode Montage Application: Apply high-density optode arrays using stereotaxic positioning systems for precise localization. Verify coupling quality through initial time-of-flight distribution assessment.
• Baseline DTOF Acquisition: Collect distribution of time-of-flight (DTOF) data during resting state for system characterization and noise floor estimation.
• Experimental Paradigm Execution: Administer naturalistic cognitive tasks (e.g., problem-solving, social interaction simulations) while collecting continuous TD-fNIRS data.
• Time-Gating Implementation: Apply temporal windows to isolate late-arriving photons (which have penetrated deeper tissue layers) for enhanced depth sensitivity [24].
• Moment Calculation: Compute temporal moments (M0, M1, M2) from DTOFs for each channel and time point [24].

Analytical Processing:

• Moment Time-Series Construction: Convert DTOFs into moment sequences (M0 = integrated intensity; M1 = mean time-of-flight; M2 = variance) [24].
• GLM Implementation: Apply General Linear Modeling with moment data incorporating short-separation regressors: Δμ_a = (X^T Z^{-1} X)^{-1} X^T Z^{-1} ΔM where X contains sensitivity factors, Z is covariance matrix, and ΔM represents moment changes [24].
• Statistical Mapping: Generate statistical parametric maps of brain activation using moment-based HRF estimates.

Protocol for Hybrid fNIRS-EEG in Naturalistic Settings

Combining fNIRS with electrophysiological measures provides complementary information about hemodynamic and electrical neural activity, particularly valuable in naturalistic research where different aspects of brain function can be captured simultaneously.

Apparatus and Reagents:

• fNIRS System: CW-fNIRS preferred for compatibility and minimal electrical interference.
• EEG System: DC-capable amplifier with high input impedance for motion-tolerant acquisition.
• Integrated Caps: Specialized headgear incorporating both fNIRS optodes and EEG electrodes in complementary arrangements.
• Artifact Rejection Tools: Accelerometers for motion artifact detection and registration.
• Synchronization Hardware: TTL pulse generator or shared clock for temporal alignment of multimodal data streams.

Procedure:

• Hardware Integration: Connect fNIRS and EEG systems ensuring electrical isolation to prevent interference. Implement shared trigger mechanism for synchronous data acquisition.
• Participant Preparation: Apply conductive EEG gel following standard protocols, then position fNIRS optodes around electrode sites. Use optically transparent EEG electrodes when available.
• Signal Quality Optimization: Verify both fNIRS signal quality (cardiac pulsations) and EEG impedance levels (<50 kΩ for motion-tolerant acquisition).
• Naturalistic Task Administration: Implement paradigms that capture both slow hemodynamic changes (via fNIRS) and fast neural oscillations (via EEG) during ecologically valid tasks.
• Multimodal Data Collection: Acquire synchronized data streams with common trigger events marking experimental epochs.

Visualization of Experimental Workflows

CW-fNIRS Naturalistic Study Workflow

TD-fNIRS Moment Analysis Workflow

The Scientist's Toolkit: Essential Research Reagents and Materials

Successful implementation of fNIRS in naturalistic settings requires specialized materials and analytical tools. The following table details essential components of the fNIRS research toolkit.

Table 3: Essential fNIRS Research Materials and Analytical Tools

Category	Item	Specifications	Function in Naturalistic Research
Hardware Components	CW-fNIRS System	2+ wavelengths (690nm, 830nm), 10-50Hz sampling, wireless capability [22]	Mobile brain imaging in realistic environments; suitable for moving participants
	Short-Separation Detectors	8mm source-detector distance [26] [24]	Regression of superficial physiological noises; enhances brain specificity
	TD-fNIRS System	Picosecond pulsed lasers, time-correlated single photon counting [24]	Depth-resolved brain imaging; absolute quantification of hemoglobin
	Hybrid Caps	Integrated fNIRS optodes and EEG electrodes [25]	Multimodal brain imaging combining hemodynamic and electrical activity
Software Tools	HOMER3	MATLAB-based analysis pipeline [1]	Comprehensive fNIRS data processing from raw data to statistical maps
	NIRS Toolbox	Object-oriented MATLAB framework [1]	Advanced statistical analysis and signal processing for fNIRS data
	AtlasViewer	Brain visualization software [1]	Probe design and data visualization on anatomical brain models
Analytical Methods	General Linear Model (GLM)	HRF modeling with physiological regressors [27]	Statistical inference for task-evoked brain activity
	Temporal Moment Analysis	Calculation of M₀, M₁, M₂ from DTOFs [24]	Enhanced depth sensitivity in TD-fNIRS; improved HRF recovery
	Short-Separation Regression	Signal processing with 8mm channels as regressors [26] [24]	Removal of systemic physiological noises from brain signals
Experimental Materials	Naturalistic Paradigms	Task designs mimicking real-world activities [22]	Enhanced ecological validity while maintaining experimental control
	Portable Stimulus Systems	Mobile devices for task presentation [22]	Implementation of experiments outside laboratory settings
	Motion Tracking Systems	Accelerometers, optical tracking [25]	Motion artifact detection and correction in mobile paradigms
2,5-Dioxopyrrolidin-1-yl nonanoate	2,5-Dioxopyrrolidin-1-yl nonanoate, CAS:104943-23-9, MF:C13H21NO4, MW:255.31 g/mol	Chemical Reagent	Bench Chemicals
3,5-Dichloro-2-methylthioanisole	3,5-Dichloro-2-methylthioanisole, CAS:1803780-70-2, MF:C8H8Cl2S, MW:207.12 g/mol	Chemical Reagent	Bench Chemicals

The selection of appropriate fNIRS technology for naturalistic research involves careful consideration of the trade-offs between information content, practical constraints, and research objectives. CW-fNIRS offers the most practical solution for truly mobile naturalistic paradigms, with recent advancements in short-separation regression and motion-tolerant analysis methods significantly enhancing its brain specificity [26] [24]. FD-fNIRS occupies a middle ground, providing additional information content without the extreme complexity of TD systems, though commercial options remain limited. TD-fNIRS delivers superior depth resolution and sensitivity to cerebral hemodynamics, making it ideal for stationary naturalistic settings where depth discrimination is critical and technical complexity can be managed [24].

For researchers embarking on naturalistic fNIRS studies, the following evidence-based recommendations are provided:

• Prioritize CW-fNIRS with short-separation channels for studies involving significant participant movement or requiring maximum ecological validity [22] [26].
• Implement TD-fNIRS when depth resolution and absolute quantification are scientifically necessary and technical resources permit [24].
• Adopt rigorous preprocessing pipelines including band-pass filtering (0.01-0.5Hz) and GLM frameworks with physiological regressors regardless of modality [27].
• Consider hybrid imaging approaches combining fNIRS with EEG when both hemodynamic and electrophysiological measures are needed to address research questions [25].
• Validate naturalistic paradigms with controlled laboratory comparisons to establish the relationship between measured signals and neural activity in complex environments.

The ongoing development of wearable technologies, improved analytical methods, and multi-modal integration approaches continues to expand the possibilities for fNIRS in naturalistic research settings. By carefully matching technical capabilities to research requirements, scientists can leverage the unique strengths of each fNIRS modality to advance our understanding of brain function in real-world contexts.

fNIRS in Action: Protocols and Breakthrough Applications in Natural Environments

Functional near-infrared spectroscopy (fNIRS) is undergoing a transformative shift from lab-constrained systems to fully wearable technologies that enable functional neuroimaging in naturalistic environments [28]. This evolution is powered by remarkable progress in optoelectronics and miniaturization, making wearable high-density fNIRS and diffuse optical tomography (DOT) technologies possible for the first time [28]. These systems are unlocking unprecedented opportunities for precision mental health by allowing researchers to collect dense-sampled brain data in real-world settings, including unsupervised monitoring at home [29]. The capacity to measure cortical hemodynamics related to neural activation outside traditional laboratory confines represents a paradigm shift in neuroscience research, particularly for drug development where ecological validity and longitudinal monitoring are crucial [30] [29].

Wearable fNIRS systems are defined as technologies that are "sufficiently miniaturized to permit all optoelectronic components to be worn by the subject, typically on the head, with either minimal tethering cabling or, in the case of wireless devices, no tethering at all" [28]. When combined with high-density configurations (typically requiring optode densities of ~0.5/cm² or more with multiple source-detector separations), these systems can achieve spatial resolution comparable to fMRI while operating in virtually any environment [28]. This technological advancement opens new frontiers for investigating brain function in contexts previously inaccessible to neuroimaging, from monitoring cognitive decline in aging populations to assessing therapeutic responses in neurological disorders [30].

Core Technical Specifications

Modern wearable fNIRS platforms represent a significant departure from traditional systems through their emphasis on user-friendly design, wireless operation, and robust data quality. The technical evolution has been driven by modular system architectures that employ "self-contained optoelectronic modules which typically incorporate one or more source-detector pairs and allow channels to be formed both within and across modules" [28]. This approach enables conformal scalp attachment while maintaining dense sampling capabilities essential for quality neuroimaging.

Table 1: Key Technical Specifications of Advanced Wearable fNIRS Platforms

Parameter	Research-Grade Systems	Clinical/Home-Use Systems	DIY/Low-Cost Systems
Channels	32+ sources/detectors [28]	8-16 sources/detectors [29]	1-8 sources/detectors [31]
Source-Detector Distances	Short (<15 mm) and long (≥30 mm) separations [28]	15-33 mm [28]	Variable, typically 25-35 mm [31]
Weight	Varies; increasingly lightweight [28]	Ultralightweight designs [28]	Lightweight headbands [31]
Connectivity	Wired or wireless [28]	Fully wireless [29]	Wireless or standalone [31]
Power Management	Tethered or battery-operated	Rechargeable batteries (2+ hours) [29]	Standard batteries [31]
Data Quality	High dynamic range detectors [28]	Motion-resistant, good dynamic range [29]	Basic signal quality [31]

Enabling Technologies for Home Deployment

Successful deployment of wearable fNIRS for unsupervised home monitoring relies on several key technological innovations beyond the core measurement system. Augmented reality (AR) guidance for device placement ensures reproducible positioning across multiple sessions without technical supervision [29]. This approach utilizes tablet cameras to guide users through proper headset alignment, addressing one of the most significant challenges in unsupervised data collection. Cloud-based data management enables secure transmission and storage of acquired data, allowing researchers remote access while maintaining compliance with healthcare regulations like HIPAA [29]. Additionally, integrated cognitive testing platforms provide synchronized behavioral and brain activity measurements during standardized tasks administered through tablet applications [29].

Figure 1: End-to-End Workflow for Unsupervised fNIRS Data Collection at Home

Experimental Design & Protocols

Dense-Sampling Paradigms for Longitudinal Monitoring

Dense-sampling refers to "collecting a large amount of data over multiple sessions, providing a more comprehensive and reliable view of brain data" [29]. This approach is particularly valuable for establishing individual-level brain signatures and tracking subtle changes over time, making it ideal for clinical trials and therapeutic monitoring. In practice, dense-sampling involves repeated measurements across multiple days or weeks, with each session typically lasting 30-60 minutes and incorporating various cognitive tasks and resting-state measurements [29].

A proof-of-concept study demonstrated the feasibility of this approach, where participants completed ten measurement sessions across three weeks, each including self-guided preparation, device placement, and cognitive testing [29]. This intensive sampling strategy significantly improves the reliability and specificity of functional connectivity measures, with reported high test-retest reliability and within-participant consistency in functional connectivity and activation patterns [29]. For drug development applications, this enables researchers to establish robust individual baselines and detect intervention effects with greater precision than single-session designs.

Event-Related Designs for Naturalistic Assessment

Event-related experimental designs are particularly suitable for naturalistic settings as they "focus on the presentation of single stimuli/events" and can be implemented with varying inter-stimulus intervals (ISI) [32]. This flexibility allows for more ecologically valid paradigms that better resemble real-world cognitive demands. For example, in driving simulation studies, stimuli might include "red lights, or pedestrians crossing the street, and rather passive events, such as disturbances like kids playing on the pedestrian way" [32].

Key considerations for event-related designs in home settings include:

• Stimulus repetition: To achieve sufficient statistical power, researchers should "repeat stimuli of the same condition and average over stimuli multiple times" [32]
• ISI optimization: To prevent overlapping hemodynamic responses, ISI should be carefully jittered, typically between 3-10 seconds, balancing temporal efficiency with signal quality [32]
• Task randomization: Presenting stimuli in randomized order "can help avoid participants anticipating the order of the presented stimuli" [32], reducing habituation effects in longitudinal studies

Core Cognitive Tasks for fNIRS Assessment

Table 2: Standardized Cognitive Tasks for Unsupervised fNIRS Monitoring

Task	Cognitive Domain	Protocol	Analysis Approach
N-Back [29]	Working Memory	Participants monitor sequences of stimuli and indicate when the current stimulus matches one presented 'n' items back. Typical sessions: 7 minutes with 0-back and 2-back conditions.	Block averaging of oxy-Hb concentration changes during task periods versus baseline.
Flanker Task [29]	Executive Function, Inhibitory Control	Participants identify central target arrows flanked by congruent or distracting stimuli. Measures conflict resolution ability.	Event-related analysis comparing congruent and incongruent trials, focusing on prefrontal activation.
Go/No-Go [29]	Response Inhibition	Participants respond frequently to "Go" stimuli but withhold response to rare "No-Go" stimuli. Assesses impulse control.	Contrasting successful inhibitions on No-Go trials versus Go trials, examining right prefrontal regions.
Verbal Fluency [30]	Executive Function, Language	Participants generate words belonging to a specific category within time constraints.	Analysis of prefrontal and temporal lobe activation during word generation versus rest.
Resting-State [29]	Functional Connectivity	Participants rest with eyes open or closed for 5-10 minutes without engaging in structured tasks.	Calculation of correlation-based functional connectivity maps between brain regions.

Data Processing & Analytical Frameworks

Pre-processing Pipelines for Real-World Data

fNIRS signals acquired in home environments require careful processing to isolate neuronal activity from various noise sources. Unprocessed fNIRS data contain "noise from different sources including physiological, instrumental, and motion that may conceal the task-related functional cortical signal" [33]. The most frequently used pre-processing techniques identified in motor control research (which translates well to naturalistic settings) include:

• Frequency filters: Bandpass filters (typically 0.01-0.2 Hz) to remove cardiac pulsation (~1 Hz), respiration (~0.2-0.3 Hz), and Mayer waves (~0.1 Hz) [33]
• Wavelet filters: Effective for removing motion artifacts and systemic interference without distorting the hemodynamic response [33]
• Short-separation regression: Using channels with short source-detector distances (<15 mm) to regress out superficial scalp hemodynamics [28]

Recent community initiatives like the fNIRS Reproducibility Study Hub (FRESH) have revealed that "the main sources of variability across teams are linked to pruning choices, hemodynamic response function models, and the analysis space used for statistical inference" [34]. This underscores the importance of standardized processing protocols, particularly for multi-site clinical trials.

Statistical Analysis and Inference

For statistical analysis, the general linear model (GLM) represents the most frequently used processing technique in fNIRS research [33]. The GLM approach models the fNIRS signal as a combination of predicted hemodynamic responses to experimental conditions plus error terms. More sophisticated approaches include:

• Linear mixed effects models: Particularly useful for nested data structures in longitudinal studies [33]
• Multiple comparison corrections: False discovery rate (FDR) procedures to control type I errors in channel-wise analyses [35]
• Functional connectivity analysis: Calculating correlation matrices between channels to assess network properties [29]

Figure 2: Standard fNIRS Data Processing Pipeline

Application Protocols for Specific Use Cases

Protocol 1: Longitudinal Therapeutic Monitoring in Depression

Purpose: To track neurophysiological changes associated with antidepressant treatment response through unsupervised home monitoring.

Equipment: Wireless high-density fNIRS system (minimum 16 channels), tablet with cognitive testing application, cloud connectivity [29].

Session Structure:

• Pre-treatment baseline assessment (3 sessions over 1 week)
• Weekly monitoring during treatment (8-12 weeks)
• Post-treatment assessment (3 sessions over 1 week)

Cognitive Battery (each session):

• Emotional face processing task (10 minutes)
• Verbal fluency task (7 minutes) [30]
• Resting-state recording (5 minutes)
• Go/No-Go task (7 minutes) [29]

Key Outcome Measures:

• Prefrontal cortex activation during emotional processing
• Functional connectivity within cognitive control networks
• Hemodynamic response latency and amplitude

Analysis Approach: Linear mixed effects models with time as fixed effect and participant as random effect, controlling for potential practice effects.

Protocol 2: Cognitive Function Assessment in ADHD

Purpose: To evaluate medication effects on prefrontal cortex function in attention-deficit/hyperactivity disorder through dense-sampling.

Equipment: Wearable fNIRS headband with prefrontal coverage, AR guidance for placement, integrated task administration [29] [31].

Session Timing: Assessments during medication ON and OFF states (counterbalanced)

Task Protocol:

• N-back task (working memory) [29]
• Flanker task (attention and cognitive control) [29]
• Resting-state with eye-open fixation (5 minutes)

Data Quality Considerations: Given challenges with motion in ADHD populations, implement:

• Real-time motion quality metrics
• Automated artifact rejection algorithms
• Additional short-separation channels for superficial signal regression [28]

Statistical Analysis: Within-subject contrasts between medication conditions, focusing on dorsolateral and ventrolateral prefrontal cortex activation.

The Scientist's Toolkit

Table 3: Essential Research Reagents and Solutions for Wearable fNIRS Studies

Item	Specifications	Function/Purpose	Representative Examples
Wearable fNIRS Systems	High-density (≥0.5 optodes/cm²), multiple source-detector distances (including short-separation <15 mm) [28]	Measures cortical hemodynamics through near-infrared light absorption	NIRx NIRSport2, Artinis Brite, Custom DIY systems [28] [31]
Optode Placement Guidance	Augmented reality systems using tablet/smartphone cameras	Ensures consistent device placement across unsupervised sessions	AR guidance software [29]
Cognitive Testing Platforms	Tablet-based applications synchronized with fNIRS recording	Administers standardized cognitive tasks while measuring brain activity	Integrated N-back, Flanker, Go/No-Go tasks [29]
Data Processing Software	Pipeline tools for filtering, artifact removal, and statistical analysis	Processes raw fNIRS signals to extract neural activity markers	Homer2, NIRS-KIT, SPM-based tools [33] [35]
Quality Control Metrics	Signal-to-noise ratio calculations, motion artifact quantification	Ensures data quality and identifies problematic channels/sessions	CV, SVN, tCOD metrics [34]
Cloud Data Management	HIPAA/GDPR-compliant secure data transmission and storage	Enables remote data access while maintaining privacy and security	Custom cloud solutions [29]
Methyl 4-amino-2-isopropoxybenzoate	Methyl 4-amino-2-isopropoxybenzoate|909563-22-0	Methyl 4-amino-2-isopropoxybenzoate (CAS 909563-22-0) is a chemical building block for research. This product is for Research Use Only and is not intended for personal use.	Bench Chemicals
3-Chloro-6-nitroisoquinolin-1-ol	3-Chloro-6-nitroisoquinolin-1-ol, CAS:1416714-03-8, MF:C9H5ClN2O3, MW:224.6 g/mol	Chemical Reagent	Bench Chemicals

Methodological Considerations and Limitations

Ensuring Data Quality and Reproducibility

Data quality remains a significant consideration in unsupervised fNIRS studies. The FRESH initiative revealed that "fNIRS reproducibility varies with data quality, analysis pipelines, and researcher experience" [34]. Teams with higher self-reported analysis confidence, which correlated with years of fNIRS experience, showed greater agreement in analyzing the same datasets [34]. To enhance reproducibility:

• Implement rigorous quality control metrics before analysis
• Pre-register analysis pipelines to reduce researcher degrees of freedom
• Provide comprehensive training materials for participants conducting self-administered recordings
• Use automated quality checks to flag potentially problematic sessions in real-time

Technical Limitations and Solutions

Current wearable fNIRS systems face several technical constraints that researchers must consider:

• Depth sensitivity: fNIRS primarily measures cortical activity, with limited sensitivity to subcortical regions [28]
• Spatial resolution: Even high-density systems have resolution limitations compared to fMRI [28]
• Hair interference: Dark thick hair can impede signal quality, requiring careful optode placement and additional channels [34]
• Battery life: Wireless systems typically offer 2-4 hours of continuous recording, which may limit assessment duration [29]

Potential solutions include combining fNIRS with other portable neuroimaging methods like EEG for comprehensive assessment, developing more efficient power management systems, and implementing adaptive sampling strategies that focus recording periods on tasks of greatest interest.

Wearable fNIRS systems represent a transformative technology for neuroscience research and drug development, enabling dense-sampling approaches in naturalistic environments. The capacity to collect functional brain data unsupervised at home addresses critical limitations of traditional neuroimaging, particularly for longitudinal studies and clinical trials requiring ecologically valid assessment. As these technologies continue to evolve toward greater miniaturization, improved signal quality, and enhanced user-friendly design, they hold substantial promise for advancing precision mental health and personalized therapeutic interventions.

The integration of wearable neuroimaging into real-world settings marks a transformative advancement for precision mental health, enabling the capture of brain function in ecologically valid conditions [16]. This case study details the application of a wearable functional near-infrared spectroscopy (fNIRS) platform to investigate the immediate cognitive and neural impacts of social media use—a domain of high relevance given that 69% of U.S. adults and 81% of teens engage with these platforms regularly [36]. Traditional neuroimaging methods like fMRI are confined to laboratory settings, limiting their ability to study brain function in naturalistic environments [16]. In contrast, fNIRS provides a portable, cost-effective, and motion-tolerant alternative, facilitating the collection of dense-sampled data during daily activities [16] [36]. Framed within a broader thesis on naturalistic fNIRS applications, this document provides detailed application notes and experimental protocols from a seminal study, offering researchers a template for conducting similar investigations into digital behavior's impact on cognition [36].

Background and Scientific Rationale

The Promise of fNIRS for Naturalistic Research

Functional near-infrared spectroscopy (fNIRS) is a non-invasive optical neuroimaging method that measures cortical hemodynamic activity by detecting changes in oxygenated (Oxy-Hb) and deoxygenated hemoglobin (Deoxy-Hb) concentrations [35] [37]. Its advantages over other neuroimaging modalities include portability, tolerance to motion artifacts, and lower operating costs, making it exceptionally suitable for studying brain function in real-world scenarios such as offices, homes, or during social interactions [16] [36]. The development of wireless, wearable fNIRS systems, sometimes integrated with augmented reality for guided device placement, has further democratized access to functional brain imaging outside the laboratory [16]. This technological progress supports the precision mental health paradigm, which aims to tailor interventions based on individual neurobiological phenotypes, moving beyond one-size-fits-all treatment approaches [16].

The Cognitive Cost of Social Media

Contemporary research indicates that social media consumption can negatively impact executive functioning (EF), which encompasses working memory, inhibitory control, and cognitive flexibility [36] [38]. One proposed mechanism is attentional overload, where the constant notifications and infinite scroll features of digital platforms exceed the brain's finite attentional resources, leading to a state of continuous partial attention and reduced cognitive performance [38]. A recent study leveraging wearable fNIRS provided the first direct neural evidence of these cognitive costs in a naturalistic setting, demonstrating altered prefrontal cortex activation following social media use [36]. This case study expands on those findings and methodologies.

The following section synthesizes the experimental design and core results from the foundational study, "Naturalistic fNIRS assessment reveals decline in executive function and altered prefrontal activation following social media use in college students" [36].

• Participants: Twenty college students (average age not specified in search results, but typically ~20 years). Fifty-five percent (55%) met criteria for social media addiction, with an average Instagram usage of five hours per week [36].
• Design: A within-subjects design where participants completed EF assessments and self-report questionnaires both before and after a brief, guided social media exposure.
• fNIRS Setup: A wearable, multichannel fNIRS system was used to measure hemodynamic activity in the prefrontal cortex (PFC), a brain region critical for executive functions [36].
• Cognitive Tasks: The assessment battery included the N-back task (working memory) and Go/No-Go task (response inhibition), administered via a tablet interface synchronized with the fNIRS recordings [16] [36].

The study yielded significant behavioral and neural changes following social media exposure, summarized in the table below.

Table 1: Key Behavioral and Neural Findings Following Social Media Exposure

Domain	Metric	Pre-Social Media Performance	Post-Social Media Performance	Significance
Behavioral (EF)	N-back Accuracy	Higher	Reduced	Impaired working memory [36]
	Go/No-Go Accuracy	Higher	Reduced	Impaired inhibitory control [36]
Neural (PFC Activation)	Medial PFC (mPFC)	Baseline	Increased	Suggests greater cognitive effort/performance monitoring [36]
	Dorsolateral PFC (dlPFC)	Baseline	Decreased	Reflects impairment in working memory [36]
	Ventrolateral PFC (vlPFC)	Baseline	Decreased	Reflects impairment in inhibition [36]
	Inferior Frontal Gyrus (IFG)	Baseline	Decreased	Linked to difficulties suppressing motor responses [36]

These findings demonstrate a tangible cognitive cost associated with social media use, behaviorally manifested as reduced accuracy in classic EF tasks and neurally reflected in a shift of prefrontal activation patterns indicative of compensatory effort and impaired core executive processes [36].

Detailed Experimental Protocol

This section provides a step-by-step methodology replicating the cited study, offering a ready-to-use protocol for researchers.

Pre-Experimental Preparation

• Ethics and Consent: Obtain approval from the Institutional Review Board (IRB). Secure written informed consent from all participants prior to the experiment.
• Participant Screening: Recruit adults from relevant populations (e.g., college students). Screen for history of psychiatric or neurological disorders. Use questionnaires to assess baseline social media usage habits and potential addiction [36].
• Device Preparation: Calibrate the wireless, multichannel fNIRS system according to manufacturer specifications. Ensure batteries are fully charged.

fNIRS Device Configuration and Placement

• Equipment: Use a commercial wearable fNIRS system (e.g., similar to NIRSport [39]) capable of measuring both Oxy-Hb and Deoxy-Hb concentration changes.
• Optode Montage: Design a montage covering key prefrontal subregions: dorsolateral PFC (dlPFC), ventrolateral PFC (vlPFC), medial PFC (mPFC), and inferior frontal gyrus (IFG) [36]. Optodes should be positioned according to the international 10-20 system for EEG placement to ensure reproducibility [39].
• Placement Guidance: For unsupervised or reproducible studies, an augmented reality (AR) guidance system using a tablet camera can be implemented to ensure consistent device placement across sessions [16].

Experimental Procedure Workflow

The following diagram outlines the sequential workflow of the experimental protocol.

Cognitive Task Parameters

The cognitive tasks are critical for probing specific executive functions. The protocols below should be implemented on a tablet computer synchronized with the fNIRS data acquisition system.

Table 2: Detailed Parameters for Executive Function Tasks

Task	Function Measured	Protocol	Duration	Metrics Recorded
N-back	Working Memory	Participants view a sequence of stimuli and indicate when the current stimulus matches the one presented 'n' steps back. Use 1-back and 2-back conditions. [16] [40]	6 blocks per condition. Each block: 16s rest, 20s task. [40]	Accuracy (%), Reaction Time (ms)
Go/No-Go	Response Inhibition	Participants respond rapidly to frequent "Go" stimuli and withhold responses to infrequent "No-Go" stimuli. [16]	~7 minutes total per session. [16]	Accuracy on Go/No-Go trials (%), Commission Errors (False Alarms)
Resting-State	Functional Connectivity	Participants remain at rest, typically fixating on a cross, without engaging in any structured task. [16]	Multiple periods interleaved with tasks, e.g., 30-60s. [16]	Hemodynamic fluctuations for connectivity analysis

Data Processing and Statistical Analysis

• Data Preprocessing: Process raw fNIRS signals to remove noise and artifacts. This includes:
  • Motion Artifact Correction: Apply algorithms like CBSI (Correlation-Based Signal Improvement) or TDDR to mitigate movement-induced noise [37].
  • Physiological Noise Filtering: Use band-pass filters to isolate the hemodynamic response (typically ~0.01-0.2 Hz) from cardiac and respiratory cycles [35].
• Hemodynamic Conversion: Convert optical density data to concentration changes in Oxy-Hb and Deoxy-Hb using the Modified Beer-Lambert Law [35].
• Statistical Analysis:
  • General Linear Model (GLM): Apply a GLM to task-dependent data to estimate beta weights for brain activation in response to specific cognitive tasks [35].
  • Functional Connectivity: Calculate correlations (e.g., Pearson's Correlation, Cross-Correlation) between time series from different brain regions to construct functional brain networks [37].
  • Graph Theory Metrics: Analyze connectivity networks using graph theory measures (e.g., clustering coefficient, global efficiency) to quantify brain network organization [37].
  • Group-Level Inference: Use paired t-tests to compare pre- and post-social media activation and connectivity. Correct for multiple comparisons using False Discovery Rate (FDR) [35].

Visualization of Neural Findings

The study revealed a distinct pattern of neural reallocation in the prefrontal cortex following social media use. The following diagram illustrates this key finding.

The Scientist's Toolkit: Essential Research Reagents and Materials

For researchers seeking to replicate this study, the following table details the essential materials and their functions.

Table 3: Essential Research Materials and Equipment for Wearable fNIRS Studies

Item	Specification / Example	Primary Function	Notes for Selection
Wearable fNIRS System	Multi-channel, wireless system (e.g., NIRSport [39])	Measures cortical hemodynamic responses (Oxy-Hb, Deoxy-Hb) in naturalistic settings.	Prioritize systems with good portability, battery life, and compatibility with synchronization tools.
fNIRS Optodes	Sources & detectors for 760nm & 850nm wavelengths [40]	Emits near-infrared light and detects its attenuation after passing through brain tissue.	Ensure the number of channels is sufficient to cover the target PFC subregions.
AR Placement Guide	Tablet application with camera-based AR [16]	Guides reproducible device placement on the head according to the 10-20 system.	Critical for ensuring data consistency across sessions and participants, especially in unsupervised setups.
Stimulus Presentation Software	PsychoPy [40], E-Prime, or custom tablet apps	Prescribes cognitive tasks (N-back, Go/No-Go) and records behavioral responses.	Software must allow for synchronization with fNIRS data via event markers (e.g., via Lab Streaming Layer).
Data Processing Pipeline	CBSI/TDDR for noise removal; PC/CC for connectivity [37]	Removes artifacts and extracts meaningful hemodynamic and connectivity features from raw fNIRS data.	Open-source toolboxes (e.g., Homer2, NIRS-KIT) can implement these algorithms.
Cloud Data Platform	HIPAA-compliant cloud storage [16]	Enables secure remote data upload, storage, and access for collaborative research.	Ensures data integrity and facilitates large-scale, multi-site studies.
2,2-Dimethoxycyclobutan-1-amine	2,2-Dimethoxycyclobutan-1-amine, CAS:2059967-05-2, MF:C6H13NO2, MW:131.17 g/mol	Chemical Reagent	Bench Chemicals
3-(Pentan-2-YL)-1,2-oxazol-5-amine	3-(Pentan-2-yl)-1,2-oxazol-5-amine|CAS 1092298-90-2		Bench Chemicals

This application note demonstrates the potent combination of wearable fNIRS technology and naturalistic experimental design to uncover the tangible cognitive costs of everyday activities like social media use [36]. The detailed protocols and findings provide a validated framework for researchers in neuroscience and drug development to objectively quantify the impact of digital behaviors and other real-world stimuli on brain function. The ability to collect dense-sampled, ecologically valid neuroimaging data opens new frontiers for developing biomarkers, assessing therapeutic interventions, and advancing the core principles of precision mental health [16]. Future work should focus on larger, more diverse cohorts, including clinical populations, to explore disorder-specific applications and further validate the potential of this innovative approach.

The advent of wearable, self-administered functional near-infrared spectroscopy (fNIRS) platforms represents a paradigm shift in neuroimaging, moving beyond traditional lab-based settings to enable precision functional mapping in naturalistic environments [16]. This approach aligns with the emerging framework of precision mental health, which aims to tailor interventions based on individual neurobiological features rather than relying solely on symptom-based categorizations [16]. Unlike traditional neuroimaging tools like functional magnetic resonance imaging (fMRI), which are costly, confine participants to restricted positions, and are sensitive to motion artifacts, fNIRS offers a portable, cost-effective alternative that is more tolerant of movement [41]. This case study examines a specific self-administered fNIRS platform, evaluating its components, reliability, and application in capturing individualized brain dynamics.

The integrated platform described in recent literature comprises several key components [16]:

• A wireless, portable multichannel fNIRS device: Designed for easy and comfortable use at home, facilitating the collection of dense-sampled prefrontal cortex (PFC) data.
• An intuitive tablet application: Utilizes augmented reality (AR) guidance, enabled by the tablet camera, to guide users through proper and reproducible device placement. This feature ensures consistency in data collection across unsupervised sessions.
• App-integrated cognitive tests: Includes a set of standardized cognitive tasks (e.g., N-back, Flanker, Go/No-Go) specifically designed for tablet-based administration.
• A cloud-based data management system: Enables synchronized recording of cortical brain activity and behavioral responses, with secure, remote data access for clinicians and researchers.

This platform is engineered to overcome the significant technical and administrative gaps in existing systems, which are often cumbersome, wired, and require technician administration, thereby hindering widespread adoption for remote monitoring and large-scale studies [16].

Experimental Protocols & Methodologies

Protocol for Dense-Sampling and Reliability Assessment

A proof-of-concept study demonstrates the application of this platform for dense-sampling functional connectivity analysis [16].

• Participants: Eight healthy young adults (5 females, 3 males; mean age 26.13 ± 5.99 years).
• Study Design: Each participant completed ten measurement sessions over three weeks.
• Session Protocol: Each session lasted approximately 45 minutes and included:
  • Self-guided preparation and device placement: Participants used the tablet application for guidance.
  • Task explanation and practice: Participants practiced each cognitive task with feedback.
  • Cognitive testing with synchronized fNIRS recording: Brain data (oxy-hemoglobin [Oxy-Hb] and deoxy-hemoglobin [deoxy-Hb]) was recorded during four seven-minute conditions: resting-state, N-back task (working memory), Flanker task (inhibitory control), and Go/No-Go task (response inhibition) [16].
• Data Analysis: Focused on analyzing the test-retest reliability and within-participant consistency of functional brain connectivity across the ten sessions.

Protocol for Naturalistic Assessment of Social Media Impact

Another study exemplifies the use of wearable fNIRS in a naturalistic setting to investigate the immediate impact of social media consumption on executive function [9].

• Participants: Twenty college students.
• Study Design: Participants were divided into a social media use group and a control group. Both groups completed assessments before and after a brief intervention.
• Intervention: The social media group engaged in passive scrolling on their preferred platform, while the control group did not.
• Session Protocol:
  • Pre-test: Participants completed EF tasks (N-back, Go/No-Go) and self-report questionnaires while fNIRS measured prefrontal cortex activity.
  • Intervention: Social media use or control period.
  • Post-test: Participants repeated the EF tasks and questionnaires with concurrent fNIRS monitoring.
• Environment: Data were collected in a quiet, private room in a student residence building to enhance ecological validity [9].
• Data Analysis: Examined behavioral performance (task accuracy) and neural correlates (changes in PFC activation) pre- and post-intervention.

Quantitative Results from Dense-Sampling Study

Table 1: Summary of Key Findings from the Dense-Sampling fNIRS Study [16]

Aspect Investigated	Key Finding	Implication
Test-Retest Reliability	High reliability and within-participant consistency in functional connectivity and activation patterns across ten sessions.	Supports the feasibility of the platform for capturing stable, individualized neural signatures.
Individual Specificity	An individual's brain data demonstrated deviations from group-level averages.	Highlights the importance of individualized neuroimaging for precise mapping of brain activity, crucial for precision health.
Data Quantity	Total of seventy minutes of fNIRS data collected for each of the four cognitive tasks per participant.	Dense-sampling (large amount of data over multiple sessions) is achievable outside the lab and provides a more comprehensive view of brain function.

Quantitative Results from Social Media Impact Study

Table 2: Summary of Key Findings from the Naturalistic fNIRS Study on Social Media Use [9]

Domain	Key Finding	Neural Correlate (fNIRS Measurement)
Behavioral Performance	Reduced accuracy in executive function tasks (N-back, Go/No-Go) following social media exposure.	-
Prefrontal Activation	Increased medial PFC (mPFC) activation.	Suggests greater cognitive effort and performance monitoring after social media use.
Prefrontal Activation	Decreased activation in dorsolateral (dlPFC) and ventrolateral (vlPFC) prefrontal cortex.	Reflects impairments in working memory and inhibitory control.
Motor Inhibition	Reduced inferior frontal gyrus (IFG) activity.	Linked to difficulties in suppressing motor responses.

The Scientist's Toolkit: Research Reagent Solutions

Table 3: Essential Materials and Reagents for Self-Administered fNIRS Research

Item / Solution	Function / Description
Wireless Multichannel fNIRS Device	Core imaging hardware; uses near-infrared light (typically at 730 nm and 850 nm) to measure relative changes in oxygenated and deoxygenated hemoglobin in the cortical tissue [41].
AR-Guided Placement System	Software component (e.g., tablet app) that uses augmented reality to ensure consistent and reproducible placement of the fNIRS headband according to the standard 10-20 system [16].
Tablet-Based Cognitive Battery	Standardized set of behavioral tasks (e.g., N-back, Flanker, Go/No-Go) integrated with the fNIRS system for synchronized stimulus presentation and data acquisition [16].
Cloud-Based Data Repository	A secure, HIPAA-compliant cloud solution for remote data storage, access, and analysis, enabling collaborative research and remote patient monitoring [16].
BIOPAC AcqKnowledge Software	Example of data acquisition and analysis software that can interface with fNIRS systems to provide tools for event-related potentials and ensemble averaging [41].
2-Isopropoxy-5-nitrobenzylamine	2-Isopropoxy-5-nitrobenzylamine|Research Chemical
2,6-Dicyclopropylpyrimidin-4-amine	2,6-Dicyclopropylpyrimidin-4-amine|Research Chemical

Signaling Pathways and Workflow Diagrams

The evidence presented demonstrates that self-administered fNIRS platforms are technologically feasible and capable of producing highly reliable and individualized functional maps. The high test-retest reliability achieved through dense-sampling in naturalistic settings underscores the potential of this technology to move psychiatric and neurological research beyond group-level averages to patient-specific biomarkers [16]. The application in studying the cognitive impact of social media highlights its ecological validity, capturing neural correlates of executive dysfunction as it unfolds in real-world scenarios [9].

For researchers and drug development professionals, these platforms offer a transformative toolset. They enable the longitudinal monitoring of treatment response in patients' daily lives, facilitate the identification of neurobiological subtypes within heterogeneous disorder categories, and provide a means to conduct large-scale, decentralized clinical trials. Future work should focus on validating these platforms in larger, more diverse, and clinical populations to fully realize their potential in advancing precision mental health.

Functional near-infrared spectroscopy (fNIRS) hyperscanning represents a transformative approach in social neuroscience, enabling the simultaneous recording of brain activity from two or more individuals during interactive tasks. This paradigm moves beyond studying isolated brains to investigate the dynamic brain activities that underlie real-world social behaviors [42]. The technique is particularly valuable for naturalistic design settings because it is relatively motion-tolerant, portable, and allows for more ecological testing environments compared to traditional neuroimaging methods [43]. These attributes make fNIRS hyperscanning ideally suited for exploring the neural mechanisms of social interaction in community-based research contexts, from examining parent-child relationships to investigating peer interactions in educational settings.

Key Experimental Protocols

fNIRS Hyperscanning Setup for Dyadic Research

The following protocol outlines a standardized approach for conducting fNIRS hyperscanning experiments with dyads, adaptable for various types of social relationships (parent-child, adult strangers, romantic partners) [43].

• Pre-Experimental Preparation

  • Equipment Setup: Prepare fNIRS caps by selecting sizes slightly larger than participants' head circumferences. Modify standard EEG caps by creating holes (approximately 15mm diameter) arranged in a 3×5 grid over forehead regions, ensuring 30mm spacing between holes [43].
  • Optode Configuration: Arrange optodes in holder grids to create measurement channels covering prefrontal regions critical for social cognition [43].
  • System Calibration: Verify signal quality and calibrate the fNIRS system according to manufacturer specifications prior to participant arrival [43].

• Participant Preparation and Positioning

  • Obtain informed consent following ethical approval procedures [43].
  • Position dyad members facing each other at a comfortable distance (approximately 1.5 meters) to facilitate natural interaction while allowing for optode placement and cable management [43].
  • Secure fNIRS caps on both participants, ensuring proper optode-scalp contact through visual inspection of signal quality.

• Experimental Paradigm Design

  • Implement a block design alternating between baseline rest periods (e.g., 30 seconds) and interactive task conditions [43].
  • Cooperative Task Example: Design trials requiring simultaneous behavioral responses from both participants to earn points, emphasizing coordination and joint attention [43].
  • Include multiple trials per condition (e.g., 20 trials per task block) to ensure adequate statistical power for detecting inter-brain synchrony [43].

• Data Acquisition Parameters

  • Record concentration changes of oxygenated hemoglobin (oxy-Hb) and deoxygenated hemoglobin (deoxy-Hb) simultaneously from both participants [43].
  • Maintain consistent sampling rates across both measurement systems (typically 10-50 Hz depending on the fNIRS device) [43].
  • Synchronize behavioral and neural data streams using precise timing markers embedded in the experimental presentation software.

Data Analysis Pipeline for Brain-to-Brain Synchrony

• Preprocessing Steps

  • Apply bandpass filtering to remove physiological noise (e.g., cardiac pulsation around 1 Hz, respiratory rhythms around 0.2-0.3 Hz, and very low-frequency drift) [43].
  • Convert raw light intensity measurements to optical density changes, then to oxy-Hb and deoxy-Hb concentration changes using the modified Beer-Lambert law [43].
  • Identify and correct motion artifacts using validated algorithms (e.g., wavelet-based filtering, spline interpolation) [43].

• Analysis of Inter-Brain Synchrony

  • Calculate wavelet transform coherence (WTC) between corresponding brain regions of interacting participants to quantify inter-brain synchrony across time and frequency domains [43].
  • Employ statistical validation through surrogate data tests, comparing actual synchrony values against those generated from artificially paired (non-interacting) datasets [43].
  • Conduct connectivity analyses between different brain regions within and between brains to map neural networks supporting social interaction [43].

Quantitative Parameters and Experimental Specifications

Table 1: Key fNIRS Hyperscanning Parameters for Social Interaction Studies

Parameter Category	Specification	Experimental Purpose
Optode Configuration	3×5 grid, 30mm spacing [43]	Prefrontal cortex coverage for social cognition
Sampling Rate	10-50 Hz (device-dependent) [43]	Capturing hemodynamic responses
Block Design	30s rest, 20 trials/task block [43]	Task-baseline alternation for signal comparison
Dyad Positioning	~1.5 meters apart, facing each other [43]	Enabling naturalistic social interaction
Measurement Type	Oxy-Hb and deoxy-Hb concentration changes [43]	Hemodynamic response quantification
Analysis Method	Wavelet transform coherence (WTC) [43]	Inter-brain synchrony quantification

Table 2: fNIRS System Components and Research Reagent Solutions

Component/Reagent	Manufacturer/Example	Function/Purpose
NIRS Measurement System	Hitachi Medical Corporation ETG-4000 [43]	Primary data acquisition hardware
Probe Holder Grids	Custom 3×5 configuration [43]	Secure optode positioning on scalp
Raw EEG Caps	EASYCAP GmbH [43]	Headgear foundation for optode placement
Computing Software	MATLAB R2014a or later [43]	Data analysis and experimental control
Stimulus Presentation	Psychophysics Toolbox [43]	Experimental paradigm delivery
fNIRS Analysis Toolkit	SPM for fNIRS toolbox [43]	Data preprocessing and statistical analysis

Visualization of Experimental Workflow

Figure 1: Comprehensive workflow for fNIRS hyperscanning experiments, illustrating the sequential phases from preparation through results interpretation. The diagram outlines key procedural steps including equipment setup, participant preparation, data acquisition with simultaneous brain recording, analysis of inter-brain synchrony, and final results interpretation.

Applications in Naturalistic Social Neuroscience Research

The hyperscanning approach has been successfully applied to investigate various forms of social interaction, demonstrating its versatility across different relationship dynamics and experimental contexts. Research has examined neural representation during cooperative tasks, revealing synchronized activity patterns in prefrontal regions when participants engage in coordinated activities [43]. These neural mechanisms supporting social understanding are particularly relevant for studying developmental populations, including parent-infant bonding and peer interactions among children [44] [43]. Furthermore, the approach shows significant promise for clinical applications, especially for understanding and treating conditions characterized by social difficulties such as autism spectrum disorder and social anxiety disorder [44]. By capturing the dynamic brain activities of multiple individuals simultaneously in naturalistic contexts, fNIRS hyperscanning provides unprecedented insights into the neural basis of human sociality that cannot be derived from studying isolated brains.

Functional near-infrared spectroscopy (fNIRS) is a portable, non-invasive neuroimaging technique that measures cortical hemodynamic activity by detecting changes in oxygenated and deoxygenated hemoglobin concentrations [11]. Its relative tolerance to motion artifacts and ecological validity makes it particularly well-suited for naturalistic research designs, allowing for the investigation of cognitive and emotional processes in real-world environments [36] [11]. This document outlines application notes and experimental protocols for using fNIRS to assess executive function, reward processing, and emotional regulation within naturalistic settings, providing a framework for researchers and drug development professionals.

Core Cognitive Domains and Neural Correlates

The prefrontal cortex (PFC) serves as the neural substrate for a range of higher-order cognitive and emotional functions. The table below summarizes the key domains, their neural correlates, and representative fNIRS findings.

Table 1: Core Cognitive Domains Assessable with fNIRS

Cognitive Domain	Key Brain Regions	Measured Constructs	Example fNIRS Findings in Naturalistic Settings
Executive Function	Dorsolateral PFC (dlPFC), Ventrolateral PFC (vlPFC) [36]	Working memory, cognitive flexibility, inhibition, planning [45]	Reduced dlPFC/vlPFC activation post-social media use linked to decreased accuracy on n-back and Go/No-Go tasks [36].
Emotional Regulation	Medial PFC (mPFC), Inferior Frontal Gyrus (IFG) [46]	Emotion modulation, impulse control, performance monitoring [45] [46]	Increased mPFC activation suggests greater cognitive effort; reduced IFG activity linked to response suppression difficulties [36]. Secure attachment correlates with better PFC-mediated emotion regulation [46].
Reward Processing & Decision-Making	Orbitofrontal Cortex, Medial PFC	Reward valuation, risk assessment, emotional vs. cognitive routes [45]	Decision-making involves interplay between emotional (System 1) and cognitive (System 2) processes, reflected in distinct fNIRS activation patterns [45].

Experimental Protocols for Naturalistic fNIRS Assessment

Protocol: Assessing Executive Function Decline Following Social Media Exposure

This protocol is adapted from a 2025 study investigating the immediate impact of social media on executive functioning [36].

1. Objective: To behaviorally and neurally quantify the effect of brief, naturalistic social media consumption on core executive functions. 2. Participants: Adult populations (e.g., college students). 3. Equipment: - fNIRS System: A wearable, multi-channel fNIRS system covering dlPFC, vlPFC, mPFC, and IFG. - Stimulus Presentation: A computer or tablet for task administration and social media exposure. 4. Procedure: - Pre-Exposure Baseline (T0): - Participants complete self-report questionnaires (e.g., on trait mindfulness [46] or social media usage [36]). - Participants perform baseline EF tasks (e.g., n-back for working memory, Go/No-Go for inhibition). - fNIRS data is collected during EF task performance. - Experimental Intervention (10-15 minutes): - Participants freely browse their personal social media feeds on a provided device. - Post-Exposure Assessment (T1): - Participants repeat the EF tasks while fNIRS data is collected. - Participants complete a brief self-report on their emotional state [36]. 5. Data Analysis: - Compare pre- and post-exposure task accuracy and reaction times. - Process fNIRS data to compute HbO and HbR concentration changes. - Contrast cortical activation maps between T0 and T1, looking for reductions in dlPFC/vlPFC activation and increased mPFC activation correlated with performance decline [36].

Protocol: Evaluating Emotional Regulation in Social Contexts

This protocol leverages fNIRS to study emotional regulation in contexts approaching real-life social interactions.

1. Objective: To investigate the role of PFC functions and trait mindfulness in emotional regulation during socially evocative tasks. 2. Participants: Adult populations. 3. Equipment: - fNIRS System: Covering mPFC and other prefrontal regions associated with socioemotional processing [46]. - Behavioral Tasks: Use of standardized emotionally evocative stimuli (e.g., video clips, images, or guided imagery of social situations). 4. Procedure: - Baseline Questionnaires: Administer scales assessing attachment style, prefrontal cortex functions, trait mindfulness, and emotion regulation strategies [46]. - fNIRS Recording: Participants are exposed to the emotionally evocative stimuli while fNIRS data is collected. A baseline rest period is recorded beforehand. - Self-Report: After each stimulus, participants rate their emotional state. 5. Data Analysis: - Use a General Linear Model (GLM) to estimate the hemodynamic response to each stimulus type [11]. - Perform path analysis to model relationships between neural activation (mPFC), self-reported attachment, trait mindfulness, and emotional regulation scores [46].

The following workflow diagram illustrates the general sequence of an fNIRS experiment, from setup to data interpretation:

Diagram 1: General Workflow of a Naturalistic fNIRS Study.

Data Processing and Analysis Workflow

Robust preprocessing is critical for extracting meaningful neural signals from fNIRS data. The following steps, implemented in tools like MNE-Python or Homer2, constitute a standard pipeline [10] [11].

Table 2: Essential fNIRS Data Processing Steps

Processing Step	Description	Purpose	Key Parameters/Notes
1. Optical Density Conversion	Converts raw light intensity signals to optical density (OD).	Reduces instrumental noise and normalizes data [10].	-
2. Signal Quality Check	Assesses data quality, e.g., via Scalp Coupling Index (SCI).	Identifies and marks channels with poor optode-scalp contact [10].	Channels with SCI < 0.5 can be excluded [10].
3. Conversion to Hemoglobin	Applies Modified Beer-Lambert Law (MBLL) to OD data.	Calculates relative concentrations of HbO and HbR [10].	Requires a pathlength factor (PPF), often ~0.1 [10].
4. Physiological Noise Removal	Uses bandpass filtering (e.g., 0.01-0.5 Hz) and/or General Linear Model (GLM) with Short-Separation (SS) regression.	Removes cardiac, respiratory, and other systemic physiological signals not of neural origin [10] [11].	GLM with SS is superior for separating brain activity from systemic noise in single-trial analysis [11].
5. Epoching	Segments data into trials time-locked to stimulus events.	Isolates the hemodynamic response for each experimental condition.	Typical epoch: -5 to +15 s around stimulus [10].
6. Feature Extraction	Calculates features from HbO/HbR epochs for analysis/classification.	Quantifies the hemodynamic response for statistical testing.	Common features: mean amplitude, slope, peak value, area under the curve (AUC).

The diagram below details the signal processing pathway from raw data to analyzed neural signals:

Diagram 2: fNIRS Data Processing Pipeline.

The Scientist's Toolkit: Research Reagent Solutions

This section lists key software and analytical tools essential for conducting fNIRS research.

Table 3: Essential Tools for fNIRS Research

Tool Name	Type	Primary Function	Application Note
MNE-Python [10]	Software Library	Full-suite analysis for M/EEG and fNIRS.	Provides end-to-end processing, from raw data to statistics. Includes tutorial for fNIRS motor data [10].
Homer2 / Homer3 [47]	Software Package	Widely used fNIRS data processing environment.	Supports processing stream re-configuration; allows user-defined algorithm integration [47].
NIRS Toolbox [47]	Software Toolbox (MATLAB)	Advanced signal processing, visualization, and statistical analysis.	Enables GLM, functional connectivity, and multi-modal analysis. NIRx data imports with 3-D probe info [47].
General Linear Model (GLM) [11]	Statistical Method	Estimating task-evoked hemodynamic responses while filtering confounds.	Crucial for improving single-trial analysis accuracy. Use within cross-validation to avoid overfitting [11].
Short-Separation Channels [11]	Hardware/Regression Technique	Measuring systemic physiological fluctuations at the scalp surface.	Used as nuisance regressors in GLM to separate non-neural from neural signals, enhancing contrast-to-noise [11].
Turbo-Satori [47]	Software	Real-time fNIRS analysis for neurofeedback and BCI.	Optimized for user-friendly real-time research with NIRx systems [47].
3-(1H-pyrazol-4-yl)prop-2-yn-1-ol	3-(1H-Pyrazol-4-yl)prop-2-yn-1-ol|Building Block	3-(1H-Pyrazol-4-yl)prop-2-yn-1-ol is a pyrazole-propargyl scaffold for drug discovery research. For Research Use Only. Not for human or veterinary use.	Bench Chemicals
2-(5-Phenylthiazol-2-yl)aniline	2-(5-Phenylthiazol-2-yl)aniline|CAS 96303-85-4		Bench Chemicals

Navigating Practical Challenges: A Guide to Robust Naturalistic fNIRS Research

Mitigating Motion Artifacts and Environmental Noise in Uncontrolled Settings

Functional near-infrared spectroscopy (fNIRS) has emerged as a powerful neuroimaging tool for studying brain function in real-world and naturalistic settings. Unlike other neuroimaging modalities, fNIRS offers a portable, non-invasive solution for monitoring cortical activation while subjects are relatively unconstrained [48]. This capability makes it particularly valuable for research scenarios that involve movement, social interaction, or ecological validity, such as studies on sports performance, face-to-face communication, or clinical assessments in natural environments [48] [49].

However, the very advantage that makes fNIRS suitable for uncontrolled settings—its tolerance for participant movement—also presents significant methodological challenges. Motion artifacts remain a predominant source of signal contamination in fNIRS data, often manifesting as spikes, baseline shifts, or slow drifts caused by changes in the coupling between optodes and the scalp [50] [19]. These artifacts can severely hamper data interpretation, as their magnitude often exceeds the hemodynamic response function (HRF) of interest [50]. In concurrent functional magnetic resonance imaging (fNIRS-fMRI) studies, additional noise sources from the MRI environment further complicate signal acquisition [51].

This application note provides a comprehensive framework for mitigating motion artifacts and environmental noise in fNIRS studies conducted in uncontrolled settings. We synthesize the latest methodological advances, including novel algorithmic approaches and hardware-based solutions, with specific protocols for implementation. By addressing these critical noise sources, researchers can enhance data quality and reliability in naturalistic research paradigms.

Motion Artifact Characteristics and Classification

Motion artifacts in fNIRS signals originate from various sources, including head movements (nodding, shaking, tilting), facial muscle movements (raising eyebrows), and body movements that cause optode displacement [49]. These movements result in imperfect contact between optodes and the scalp, leading to signal contamination through displacement, non-orthogonal contact, or oscillation of the optodes [49].

The table below classifies common motion artifact types and their characteristics:

Table 1: Classification of Motion Artifacts in fNIRS Signals

Artifact Type	Characteristics	Common Causes	Typical Duration
Spikes	Sharp, high-amplitude transients	Quick optode movement and return to original position	Short (seconds)
Baseline Shifts	Sudden change in DC signal level	Change in optode location or pressure	Persistent until correction
Slow Drifts	Gradual signal change over time	Slow optode movement or poor attachment	Long (minutes)
Physiological Noise	Periodic components correlated with physiology	Cardiac pulsation, respiration, blood pressure	Continuous

The impact of these artifacts is particularly pronounced in mobile scenarios enabled by wearable fNIRS devices, where movements are inherent to the experimental protocol [50]. Understanding these artifact characteristics is essential for selecting appropriate correction strategies.

Motion Artifact Removal Techniques

Algorithmic Approaches

Various algorithmic approaches have been developed to address motion artifacts in fNIRS data. These methods can be broadly categorized into blind source separation techniques, regression-based methods, and adaptive filtering approaches.

Table 2: Algorithmic Motion Artifact Removal Techniques

Method	Underlying Principle	Key Parameters	Advantages	Limitations
Wavelet Filtering [18] [19]	Decomposition of signal using wavelet basis; outlier coefficients set to zero	Probability threshold (alpha)	No MA detection needed; effective for spikes and drifts; fully automatable	May require parameter tuning
Correlation-Based Signal Improvement (CBSI) [19]	Exploits negative correlation between HbO and HbR in neural signals	Standard deviation ratio between HbO and HbR	Removes large spikes and baseline shifts; fully automated	Assumes strict negative correlation between HbO/HbR
Targeted Principal Component Analysis (tPCA) [19]	Applies PCA only to previously detected motion artifact intervals	Motion detection parameters; number of components to remove	Reduces over-correction compared to standard PCA	Complex to use; multiple user parameters
Spline Interpolation [50]	Models artifact segments using spline interpolation	Interpolation degree; motion detection parameters	Effective for well-defined artifact segments	Performance depends on accurate motion detection
Kalman Filtering [50]	Recursive algorithm predicting signal state based on previous measurements	State transition parameters; measurement noise	Adapts to changing signal conditions	Build-up errors may occur over time
WCBSI (Combined Wavelet-CBSI) [19]	Hybrid approach combining wavelet decomposition and CBSI principles	Wavelet and CBSI parameters	Superior performance across multiple metrics; handles various artifact types	Newer method with less extensive validation

Emerging Deep Learning Approaches

Recent advances in deep learning have introduced novel approaches for motion artifact removal. Denoising autoencoders (DAE) represent a particularly promising development, offering assumption-free artifact removal without requiring parameter tuning by experts [52] [50].

The DAE architecture typically consists of stacked convolutional layers that learn to map noisy input signals to clean outputs. Training these networks requires specialized approaches, including the generation of synthetic fNIRS data that mimics real signal properties and the design of specific loss functions tailored to fNIRS characteristics [50]. Studies have demonstrated that DAE models outperform conventional methods in lowering residual motion artifacts, decreasing mean squared error, and increasing computational efficiency [52] [50].

Hardware-Based Solutions

Hardware-based approaches incorporate additional sensors to detect and correct motion artifacts. Accelerometer-based methods are among the most common, including:

• Active Noise Cancelation (ANC): Uses adaptive filtering with accelerometer data as a reference signal [49]
• Accelerometer-Based Motion Artifact Removal (ABAMAR): Identifies motion artifacts using accelerometer signals [49]
• Acceleration-Based Movement Artifact Reduction Algorithm (ABMARA): Combines motion detection with artifact removal [49]
• AMARA (Acceleration-based Movement Artifact Reduction Algorithm): Automatically identifies motion artifacts using accelerometers and combines MARA and ABAMAR approaches [51]

These methods face challenges in certain environments, particularly in concurrent fNIRS-fMRI studies where most accelerometers are not MR-compatible [51]. Innovative solutions have been proposed, such as deriving acceleration data from fMRI motion parameters by considering individual slice stack acquisition times of simultaneous multislice acquisition [51].

Experimental Protocols and Implementation

Protocol for Combined Wavelet-CBSI (WCBSI) Method

The WCBSI approach has demonstrated superior performance in comparative studies [19]. The following protocol details its implementation:

Materials and Equipment

• fNIRS system with multiple source-detector pairs
• Computer with MATLAB or Python and appropriate toolboxes (HOMER3, NinPy)
• Head cap or holder ensuring stable optode placement

Procedure

• Data Acquisition: Collect fNIRS data at appropriate sampling rate (typically 1-10 Hz) with source-detector separations of 2-4 cm.
• Signal Preprocessing: Convert raw intensity signals to optical density changes.
• Motion Artifact Detection: Apply automated motion artifact detection algorithm to identify contaminated segments.
• Wavelet Decomposition: Decompose the signal using a wavelet basis (e.g., Daubechies wavelet).
• Thresholding: Identify and zero wavelet coefficients exceeding statistically determined thresholds.
• Signal Reconstruction: Reconstruct the signal from the modified wavelet coefficients.
• CBSI Application: Apply correlation-based signal improvement using the formula:
  • HbO' = HbO - α · HbR
  • HbR' = -(1/α · HbO') where α = std(HbO)/std(HbR)
• Validation: Compare processed signals with ground truth data if available.

Validation Metrics

• Calculate R, RMSE, MAPE, and ΔAUC values against ground truth
• Compare with results from other correction methods

Protocol for Deep Learning-Based Artifact Removal

For implementing deep learning-based artifact removal using denoising autoencoders [50]:

Materials and Equipment

• fNIRS system with capability for continuous data recording
• High-performance computing workstation with GPU acceleration
• Python with deep learning frameworks (TensorFlow, PyTorch)

Procedure

• Training Data Generation:

  • Simulate clean HRF using gamma functions with amplitudes between 30-80 μM·mm
  • Generate motion artifacts (spike and shift types) using Laplace distribution functions
  • Create resting-state fNIRS components using autoregressive models
  • Combine components to create synthetic training dataset

• Network Architecture Design:

  • Implement a 9-layer convolutional autoencoder
  • Include appropriate pooling and upsampling layers
  • Design custom loss function incorporating fNIRS signal characteristics

• Model Training:

  • Train network on synthetic dataset
  • Validate performance on experimental data with known artifacts
  • Optimize hyperparameters using validation set performance

• Application to Experimental Data:

  • Preprocess raw fNIRS signals
  • Apply trained model to contaminated signals
  • Extract cleaned output for further analysis

• Performance Validation:

  • Compare with conventional methods using MSE and computational efficiency metrics
  • Assess on open-access experimental datasets

Protocol for Hardware-Based Motion Correction

For implementing accelerometer-based motion correction in uncontrolled settings [49] [51]:

Materials and Equipment

• fNIRS system with auxiliary input capabilities
• MR-compatible accelerometers (for fMRI environments) or standard accelerometers
• Data acquisition system for synchronizing fNIRS and accelerometer data

Procedure

• Sensor Placement: Attach accelerometers to fNIRS probe holders to detect head movement.
• Data Synchronization: Ensure precise temporal alignment between fNIRS and accelerometer signals.
• Motion Parameter Extraction: Calculate displacement, velocity, and acceleration from raw accelerometer data.
• Adaptive Filtering: Apply filter algorithms using accelerometer data as reference signal.
• Artifact Correction: Use AMARA or similar approaches to identify and correct contaminated segments.
• Validation: Compare results with video recordings or other motion tracking when available.

For concurrent fNIRS-fMRI studies where accelerometers are not feasible, implement the slice-based motion tracing method [51]:

• Extract motion parameters from fMRI data using FSL MCFLIRT or AFNI 3dvolReg.
• Reconstruct high-resolution motion traces using slice acquisition times.
• Apply these motion traces as reference signals in artifact correction algorithms.

The Scientist's Toolkit: Essential Research Reagents and Materials

Table 3: Essential Materials for fNIRS Motion Artifact Research

Item	Function/Purpose	Example Specifications
Continuous Wave fNIRS System	Measures changes in light absorption to compute HbO and HbR concentrations	Multiple wavelengths (e.g., 760 and 850 nm); sampling rate 1-100 Hz
Accelerometers	Detects head movements for motion artifact reference signals	Tri-axial; MR-compatible versions for concurrent fMRI studies
3D Motion Capture System	Provides precise tracking of head and optode movement	Infrared cameras with reflective markers; sub-millimeter accuracy
Head Caps with Stable Mounting	Minimizes optode movement relative to scalp	Dense foam structures; customized for individual head shapes
HOMER3 Software Toolkit	Comprehensive fNIRS data processing and artifact correction	MATLAB-based; includes implementation of major correction algorithms
Synthetic Data Generation Tools	Creates training data for deep learning approaches	Implements autoregressive models for resting-state fNIRS simulation
N,N-diallyl-2-chloropyridin-4-amine	N,N-diallyl-2-chloropyridin-4-amine, CAS:2098117-22-5, MF:C11H13ClN2, MW:208.69 g/mol	Chemical Reagent
2-azido-N-(4-ethylphenyl)acetamide	2-Azido-N-(4-ethylphenyl)acetamide|CAS 1160748-31-1	2-Azido-N-(4-ethylphenyl)acetamide (CAS 1160748-31-1) is a high-purity chemical for research use. This compound is for laboratory research only and not for human or animal use.

Workflow Visualization

Comprehensive Motion Artifact Correction Workflow

Diagram 1: Comprehensive workflow for motion artifact correction in fNIRS studies

Deep Learning-Based Artifact Removal Process

Diagram 2: Deep learning-based artifact removal process using denoising autoencoders

Mitigating motion artifacts and environmental noise is essential for advancing fNIRS applications in naturalistic research settings. This application note has detailed the most effective current methodologies, from established algorithmic approaches to emerging deep learning techniques. The comparative performance data and implementation protocols provide researchers with practical tools for enhancing data quality in uncontrolled environments.

Future developments in this field will likely focus on improving the automation of artifact correction, enhancing real-time processing capabilities, and developing more sophisticated multimodal integration approaches. As fNIRS continues to evolve as a mobile neuroimaging technology, robust motion artifact handling will remain crucial for expanding its applications in real-world cognitive neuroscience, clinical assessment, and pharmaceutical development.

Reproducibility is a cornerstone of scientific progress, yet functional Near-Infrared Spectroscopy (fNIRS) research faces significant challenges in achieving consistent results across studies and laboratories. The portability of fNIRS that makes it ideal for naturalistic design settings also introduces variability through differences in optode placement, data processing choices, and experimental implementation [34]. Recent community initiatives have revealed that while fNIRS can produce reliable group-level results, individual-level analyses show considerably lower agreement between research teams, primarily due to variability in how poor-quality data are handled, how hemodynamic responses are modeled, and how statistical analyses are conducted [34]. This application note addresses these challenges by presenting standardized protocols and Augmented Reality (AR)-guided solutions to enhance methodological rigor, with particular relevance for researchers and drug development professionals requiring consistent brain activity measures in ecologically valid settings.

The Reproducibility Challenge in fNIRS

The fNIRS Reproducibility Study Hub (FRESH) initiative, which involved 38 independent research teams analyzing identical datasets, quantified the extent of analytical variability in the field. While approximately 80% of teams agreed on group-level results for hypotheses strongly supported by existing literature, agreement was substantially lower for individual-level analyses [34]. This reproducibility gap stems from multiple sources:

• Analytical Flexibility: The absence of standardized processing pipelines allows researchers to make justifiable but divergent choices at multiple analysis stages [34]
• Spatial Uncertainty: Traditional optode placement using the 10-20 system does not account for individual neuroanatomical variations, reducing measurement consistency [53]
• Physiological Confounds: Systemic physiological signals (cardiac, respiratory, blood pressure variations) contaminate fNIRS data with non-neural components [54]
• Data Quality Variability: Signal quality is influenced by scalp-coupling, motion artifacts, and individual differences in skin pigmentation and skull thickness [54] [34]

The implications for naturalistic research are particularly significant, as studies conducted in real-world settings typically involve smaller sample sizes, greater environmental variability, and fewer control mechanisms than laboratory-based experiments.

AR-Guided Optode Placement: Principles and Implementation

Spatial Information as a Reproducibility Factor

Conventional fNIRS optode positioning using the 10-20 international system is subject to measurement errors and does not account for inter-subject anatomical differences in cortical folding [53]. This spatial uncertainty contributes significantly to within-subject variability in longitudinal studies. Research has demonstrated that incorporating real-time neuronavigation to guide optode placement significantly increases intra-subject reproducibility, particularly in the region of interest [53]. Unlike traditional approaches that yield poor intra-subject reproducibility, neuronavigation protocols enable consistent and robust activation patterns across multiple sessions [53].

AR Implementation Platforms

Recent technological advances have made AR-guided fNIRS placement accessible for naturalistic research settings:

• Wearable fNIRS Platforms: Next-generation systems incorporate AR guidance via tablet cameras to direct users through proper and reproducible device placement without specialized technical expertise [29]
• Real-Time Neuronavigation: Research systems use individual neuroanatomical information to provide accurate cortical localization of optodes, ensuring consistent targeting of regions of interest across sessions [53]
• Self-Administered Protocols: AR-enabled systems allow for unsupervised data collection while maintaining positioning consistency, enabling longitudinal studies in real-world settings [29]

Table 1: Comparison of Optode Placement Methods

Placement Method	Positioning Accuracy	Required Expertise	Suitability for Naturalistic Settings	Intra-Subject Reproducibility
Manual 10-20 System	Low	Moderate	Low	Poor [53]
AR-Guided Placement	Medium	Low	High	Good [29]
Real-Time Neuronavigation	High	High	Medium	High [53]

Figure 1: AR-guided optode placement workflow for reproducible fNIRS setup

Standardized Experimental Protocols

Protocol Design Considerations

Naturalistic fNIRS research employs three primary experimental designs, each with distinct reproducibility considerations:

• Block Designs: Characterized by alternating periods of task and rest, providing robust signal-to-noise ratio but lower ecological validity [55]
• Event-Related Designs: Present discrete, randomized trials better suited for cognitive process isolation but requiring more trials for adequate power [55]
• Resting-State Designs: Measure intrinsic brain activity without tasks, requiring careful control of environmental conditions and participant state [55]

Resting-State Protocol Standardization

Resting-state designs present particular reproducibility challenges due to their sensitivity to uncontrolled environmental and physiological factors. The following protocol enhancements improve reliability:

• Environmental Controls: Measurements should be performed in quiet, light-dimmed settings to minimize sensory distractions that introduce unwanted neural activity [55]
• Participant Positioning: Comfortable sitting or lying positions minimize motion artifacts while maintaining alertness [55]
• Duration Optimization: Adequate recording length (typically 8-10 minutes) ensures stable functional connectivity metrics without participant fatigue [55]
• State Monitoring: Consistent instruction regarding eyes open (with fixation) versus closed conditions prevents visual network contamination [55]

Table 2: Minimum Reporting Standards for fNIRS Reproducibility

Methodological Aspect	Essential Reporting Elements	Impact on Reproducibility
Optode Placement	Method (AR, 10-20, neuronavigation), cap type, inter-optode distances	High [56]
Signal Processing	Motion artifact correction, filter parameters, physiological noise handling	High [34] [56]
Data Quality	Channel rejection criteria, quality metrics, excluded participants	High [34] [56]
Experimental Design	Timing parameters, participant instructions, environment description	Medium [56]
Statistical Analysis	HRF modeling, multiple comparisons correction, effect sizes	High [34] [56]

Standardized Data Processing Pipeline

Community initiatives have identified substantial variability in fNIRS analysis pipelines as a primary threat to reproducibility [34]. The following standardized processing workflow addresses key sources of analytical variability:

Figure 2: Standardized fNIRS data processing pipeline for reproducible analysis

Critical Processing Steps for Reproducibility

• Signal Quality Assessment: Implement the Scalp Coupling Index (SCI) to identify channels with poor optode-scalp contact, establishing consistent exclusion criteria (e.g., SCI < 0.5) across studies [10]
• Physiological Noise Removal: Apply appropriate filtering methods to remove cardiac (∼1 Hz) and respiratory (∼0.3 Hz) oscillations while preserving neural hemodynamic signals (0.01-0.5 Hz) [55] [10]
• Motion Artifact Correction: Utilize validated algorithms (e.g., wavelet-based, PCA, correlation-based) with consistent parameter settings across studies [34] [10]
• Hemodynamic Response Modeling: Standardize the choice of hemodynamic response function (HRF) models and fitting parameters, particularly for event-related designs [34]

The Scientist's Toolkit: Essential Research Reagents

Table 3: Essential Materials and Solutions for Reproducible fNIRS Research

Item	Function	Implementation Example
AR-Guided Placement System	Ensures consistent optode positioning across sessions	Tablet-based AR using camera to guide placement [29]
Neuronavigation System	Provides precise cortical localization of optodes	Real-time tracking integrated with individual neuroanatomy [53]
Multidistance Optodes	Enhances depth sensitivity and confound rejection	Short-separation channels (0.8 cm) combined with standard channels (3 cm) [53]
Physiological Monitors	Records systemic physiological confounds	Heart rate, blood pressure, respiration monitoring [53]
Standardized Processing Pipeline	Ensures consistent data analysis	Preprocessing, quality metrics, artifact correction [34] [10]
Quality Assessment Metrics	Quantifies signal quality for inclusion/exclusion	Scalp Coupling Index, motion artifact detection [10]

Enhancing reproducibility in naturalistic fNIRS research requires a multifaceted approach addressing both methodological and analytical variability. AR-guided optode placement and standardized experimental protocols significantly reduce spatial uncertainty and implementation inconsistencies, while community-developed processing standards mitigate analytical flexibility. For researchers in drug development and clinical applications, these protocols provide a framework for collecting reliable, consistent brain activity measures in ecologically valid settings. Continued development of open-source tools, standardized reporting guidelines, and community-wide validation efforts will further strengthen the role of fNIRS as a reproducible neuroimaging modality for naturalistic research.

Functional near-infrared spectroscopy (fNIRS) has emerged as a particularly valuable neuroimaging tool for naturalistic research designs, allowing for the investigation of brain function in real-world settings and during authentic behaviors [57] [9]. Its portability, non-invasiveness, and relative tolerance to movement enable brain monitoring under conditions that are infeasible for more restrictive modalities like fMRI. The core principle of fNIRS involves using near-infrared light (650-950 nm) to track cortical hemodynamic changes, which serve as a proxy for neural activity via neurovascular coupling [57]. The journey from raw, acquired light intensity signals to interpretable hemodynamic features is a critical process, the fidelity of which directly determines the validity and reliability of the ensuing scientific findings. This application note details the established and emerging protocols for fNIRS data processing, with a specific emphasis on pipelines that support the unique challenges and opportunities of naturalistic design research.

Fundamental Principles of fNIRS Signal Acquisition

fNIRS is an optics-based method where tissue is irradiated with constant-amplitude near-infrared light. The back-scattered light, captured by detectors at a distance from the sources, carries information on the absorption and scattering properties of the underlying tissue. The primary absorbers within the NIR range are oxygenated and deoxygenated hemoglobin (HbO and HbR, respectively). Consequently, changes in the attenuation of the detected light can be used to infer changes in the concentration of these chromophores, providing an indirect measure of regional cortical activity [57]. A typical continuous-wave fNIRS system, which is common in naturalistic studies, measures changes in light intensity over time at multiple wavelengths (typically 2 or more), but does not directly provide absolute concentration values.

Core Processing Pipeline: From Intensity to Hemodynamics

The transformation of raw light intensity into estimates of hemodynamic activity involves a series of critical processing steps, each designed to mitigate specific artifacts and confounds.

Initial Preprocessing and Motion Artifact Correction

The initial stages of the pipeline focus on preparing the signal for conversion and removing non-physiological noise, particularly motion artifacts, which are a significant challenge in naturalistic studies.

Protocol: Signal Quality Assessment and Initial Preprocessing

• Signal-to-Noise Ratio (SNR) Check: Discard channels with insufficient SNR. A common threshold is an SNR less than 8, as channels below this value are often too noisy for reliable analysis [6].
• Conversion to Optical Density (OD): Convert the raw light intensity measurements, ( I ), for each wavelength and channel, to optical density, ( OD ), using the equation: ( OD = -\log(I/I0) ), where ( I0 ) is a reference intensity (often the mean or initial value of the time series).
• Motion Artifact Correction (MAC): Apply a motion artifact correction algorithm to the OD time series. A hybrid spline interpolation and wavelet filtering approach has been validated as particularly effective for tasks involving speech or movement, correcting up to 94% of artifacts without introducing spurious responses [6].
  • Rationale: Motion can cause sudden, high-amplitude spikes or baseline shifts in the signal that are not of cerebral origin. Effective correction is paramount for data integrity.

The following workflow diagram illustrates the comprehensive data processing pipeline, from raw intensity to final analysis.

Hemodynamic Conversion Using the Modified Beer-Lambert Law (MBLL)

The core conversion from optical density to hemoglobin concentration changes is predominantly achieved using the Modified Beer-Lambert Law (MBLL). This step requires careful selection of several parameters, as variations can influence the absolute values of the derived hemodynamic signals [57].

Protocol: MBLL Application

• Apply the MBLL to the cleaned OD data for each wavelength. The fundamental equation is: ( \Delta C = \frac{1}{d \cdot DPF \cdot \epsilon} \cdot \Delta OD ) where ( \Delta C ) is the change in chromophore concentration, ( d ) is the source-detector distance, ( DPF ) is the differential pathlength factor, and ( \epsilon ) is the molar extinction coefficient.
• Use a linear combination of the changes at different wavelengths to solve for the relative concentrations of HbO and HbR.

Table 1: Critical Conversion Parameters in the Modified Beer-Lambert Law

Parameter	Description	Common Values & Sources of Variation
Molar Extinction Coefficient (ε)	A wavelength-specific constant describing how strongly a chromophore (HbO, HbR) absorbs light.	Published values vary slightly (e.g., Prahl [57], Cope [57], Ziljstra [57]). The choice of dataset can cause linear scaling differences in calculated [HbO] and [HbR].
Differential Pathlength Factor (DPF)	A unitless scaling factor that accounts for the increased pathlength of scattered light versus the physical source-detector distance.	Constant: Often DPF=6 is used for all subjects/wavelengths [6]. Age & Wavelength Dependent: Formulas exist that make DPF a function of age and wavelength, improving accuracy, especially in pediatric populations [57].
Source-Detector Distance (d)	The physical distance between the light source and detector on the scalp.	Typically 3 cm for adult cortical measurements. Shorter distances (~0.8-1.0 cm) are used for "short-separation channels" to measure superficial, extracortical signals [6] [11].

Advanced Processing: Physiological Noise and Systemic Confound Removal

After hemodynamic conversion, the signals still contain physiological noise of systemic origin (e.g., cardiac pulsation, respiration, Mayer waves). Mayer waves (~0.1 Hz), which are blood pressure-related oscillations, pose a particular challenge as their frequency content overlaps with the task-evoked hemodynamic response [57].

Protocol: General Linear Model (GLM) with Short-Separation Regression This advanced technique is highly recommended for single-trial analysis and naturalistic designs, as it simultaneously models the brain activity of interest and the confounding signals [11].

• Construct the Design Matrix (X): This matrix contains:
  • Task Regressor(s): A canonical hemodynamic response function (HRF) convolved with the task timing. For block designs in naturalistic settings, this can be a boxcar function.
  • Nuisance Regressors: This is the critical step for denoising. Include:
    • The preprocessed signal from a short-separation channel to account for systemic hemodynamic fluctuations in the scalp [6] [11].
    • Other physiological measures if available (e.g., heart rate, respiration).
    • Linear and quadratic drift terms.
• Model Fitting: Solve the linear model ( Y = X \beta + \epsilon ) for each channel, where ( Y ) is the measured HbO or HbR time series, ( \beta ) are the model weights (estimates of the HRF amplitude and confound contributions), and ( \epsilon ) is the error term.
• Signal Extraction: The cleaned, task-related brain signal is derived from the part of the signal explained by the task regressor. The weight (beta value) of the HRF regressor itself can also serve as a highly informative feature for single-trial classification [11].

Table 2: Comparison of Physiological Noise Removal Techniques

Method	Mechanism	Advantages	Limitations
Frequency Filtering	Applies low-pass (e.g., 0.14 Hz) and/or high-pass filters to remove out-of-band noise (heart rate, respiration).	Simple and computationally efficient.	Less effective for Mayer waves due to frequency overlap with HRF. Can distort the shape of the hemodynamic response.
Short-Separation Regression	Uses a separate channel sensitive to scalp hemodynamics as a nuisance regressor in a GLM.	Physiologically interpretable; effectively removes systemic confounds shared between scalp and cortex.	Requires a specific hardware/probe setup with short-separation channels.
Blind Source Separation (PCA, ICA)	Decomposes the signal from multiple channels into statistical components, allowing for the identification and removal of noise-related components.	Data-driven; does not require additional hardware.	Requires manual component classification; risk of removing neural signal; can be computationally intensive.

Feature Extraction for Naturalistic Designs

In block-designed naturalistic studies (e.g., alternating between single-task walking and dual-task walking, or pre- and post-social media use), the most common features are the mean values of the HbO and HbR signals during each task block [57] [9]. This averaging helps mitigate the impact of residual Mayer waves and other noise. For more complex designs or single-trial analyses, the beta weights from the GLM's HRF regressor provide a robust feature that quantifies the strength of the task-evoked response [11]. Furthermore, graph theory measures from connectivity analysis, such as global and local efficiency of brain networks, are emerging as powerful features for characterizing brain states in developmental and clinical populations [37] [58].

The Scientist's Toolkit: Essential Reagents and Materials

Table 3: Key Research Reagent Solutions for fNIRS Naturalistic Research

Item	Function/Application
Continuous-Wave fNIRS System (e.g., NIRScout)	The core hardware for emitting near-infrared light and detecting back-scattered intensity. Essential for portable, naturalistic data acquisition [6].
Customizable Probe Caps	Hold sources and detectors in predetermined geometries over brain regions of interest (e.g., Prefrontal Cortex). Allows for subject-specific fitting and replication of setups [6].
Short-Separation Detectors	Specialized detectors placed close (~0.8-1.5 cm) to light sources. Their signal is predominantly sensitive to extracerebral layers and is used as a nuisance regressor to remove systemic physiological noise [6] [11].
Digitization System (e.g., Polhemus Fastrak)	Precisely records the 3D locations of optodes relative to anatomical landmarks. Cruicial for co-registering fNIRS channels to standard brain atlases and improving spatial accuracy [6].
Motion Artifact Correction Algorithms	Software implementations (e.g., hybrid spline-wavelet, CBSI, TDDR) are critical "reagents" for cleaning data from moving subjects, making naturalistic studies feasible [6] [37].
General Linear Model (GLM) Software	Statistical framework (available in tools like Homer2, SPM, NIRS-KIT) used for robust HRF estimation and denoising with nuisance regressors, enhancing single-trial analysis confidence [11].

The following diagram illustrates the application of the General Linear Model, a key advanced technique for isolating brain activity.

Experimental Protocol: A Sample Workflow for a Naturalistic fNIRS Study

Aim: To investigate the impact of a brief social media intervention on prefrontal cortex activity during a cognitive task.

Methodology:

• Setup and Calibration: Fit the participant with a wearable fNIRS headcap targeting the prefrontal cortex (dlPFC, vlPFC, mPFC), including short-separation detectors. Calibrate the system and check all channels for good SNR [9].
• Baseline Block (Pre-Intervention): The participant performs a cognitive task (e.g., n-back for working memory or Go/No-Go for inhibition) for a predetermined number of trials or duration while fNIRS data is collected [9].
• Intervention Block: The participant engages in a passive social media activity (e.g., scrolling through a feed for 10 minutes) [9].
• Post-Intervention Block: The participant repeats the cognitive task from step 2.
• Data Processing:
  • Preprocessing: Convert raw intensity to optical density. Apply a hybrid motion artifact correction algorithm.
  • Conversion: Apply MBLL using age-appropriate DPF values and a consistent molar extinction coefficient dataset to calculate HbO/HbR time series.
  • Denoising: For each subject and channel, run a GLM on the HbO signal with the following regressors: a task HRF (for the cognitive blocks), the short-separation channel signal, and drift terms.
  • Feature Extraction: Extract the mean beta value for the task HRF regressor during the baseline and post-intervention cognitive blocks. Alternatively, calculate the mean HbO amplitude during these blocks from the cleaned signal.
• Statistical Analysis: Perform a repeated-measures ANOVA or linear mixed-effects model to test for a significant interaction between the factors "Group" (if a control group exists) and "Session" (Pre vs. Post) on the extracted HbO features [57] [9].

A rigorous and well-considered data processing pipeline is the foundation of robust fNIRS research, especially in naturalistic designs where controllability is reduced and artifacts are more prevalent. While the core steps of MBLL conversion and filtering are essential, the adoption of more advanced techniques like GLM-based denoising with short-separation regressors significantly enhances the ability to isolate true cortical hemodynamic activity from confounding systemic physiology. The protocols and tables provided herein offer a structured guide for researchers to implement these methods, thereby improving the validity, reproducibility, and overall impact of their fNIRS investigations in real-world settings.

Functional Near-Infrared Spectroscopy (fNIRS) has emerged as a powerful neuroimaging tool that offers portability, cost-effectiveness, and significant tolerance to motion artifacts, making it particularly suitable for naturalistic research settings [48] [29]. However, a fundamental challenge persists: the fNIRS signal is highly sensitive to hemodynamic changes occurring in the scalp and skull tissues, which can significantly contaminate the cerebral functional response of interest [59]. This superficial contamination remains one of the most significant limitations in fNIRS technology, as these extracerebral hemodynamic fluctuations arise from physiological processes including cardiac activity, respiration, blood pressure changes, and vasomotion [60] [59]. Critically, this superficial "noise" shares the same frequency spectrum as the functional brain signal and may even be temporally correlated with functional tasks due to systemic cardiovascular responses to cognitive challenges [59]. Short-separation channels (SSCs) have consequently emerged as an essential hardware and methodological solution to explicitly measure and subsequently remove these confounding superficial signals [60] [61].

Theoretical Foundation and Technical Specifications of Short-Separation Channels

Photon Migration Principles and Depth Sensitivity

The operational principle of fNIRS channels depends fundamentally on the source-detector separation distance. Conventional "long channels" typically utilize interoptode distances of 30-40 mm in adults to achieve sufficient sensitivity to the cerebral cortex, with maximum sampling depth approximating half the source-detector distance [60]. In contrast, short-separation channels employ substantially reduced source-detector distances specifically designed to probe only extracerebral tissues while maintaining minimal sensitivity to brain activity [59]. The optimum short-separation distance represents a critical balance between completely eliminating brain sensitivity (impossible due to the continuous nature of photon migration) and maximizing the spatial overlap between the sensitivity distributions of the short-separation and standard channels in the scalp and skull layers [59].

Table 1: Optimum Short-Separation Distances for Different Populations

Population	Standard Channel Distance	Optimum Short-Separation Distance	Maximum Brain Sensitivity Ratio	Key Reference
Adults	30 mm	8.4 mm	5%	Brigadoi & Cooper (2015) [59]
Term-Age Infants	20-25 mm	2.15 mm	5%	Brigadoi & Cooper (2015) [59]
Neonates	15-20 mm	<2.15 mm	Not specified	Brigadoi & Cooper (2015) [59]

Spatial Configuration Strategies for Short-Separation Channels

The integration of short-separation channels into fNIRS probe designs requires careful consideration of the spatial arrangement relative to standard channels. Different configuration strategies offer trade-offs between coverage, hardware requirements, and regression efficacy.

Table 2: Spatial Configuration Strategies for Short-Separation Channels

Configuration Strategy	Description	Number of SSCs Required	Use Cases
Local	One SSC for each long channel	Equal to number of long channels	High-precision studies with sufficient hardware capacity [60] [61]
Symmetrical-local	One SSC for each symmetrical channel	~50% of long channels	Bilateral designs with symmetrical probe arrangements [60] [61]
Symmetrical-region	One SSC for each symmetrical region	Fewer than symmetrical-local	Large-area imaging with regional specificity [60] [61]
Global-ipsilateral	One SSC for each hemisphere	2	Whole-head imaging with limited hardware availability [60] [61]
Global-contralateral	One SSC for the entire head	1	Minimal hardware scenarios with assumed global superficial homogeneity [60] [61]

Advanced Algorithms for Superficial Signal Regression

General Linear Model Framework with Short-Separation Regressors

The General Linear Model (GLM) provides a robust statistical framework for integrating short-separation measurements into fNIRS analysis pipelines, particularly for single-trial analysis and brain-computer interface applications [11]. In this approach, the short-separation channel signals are incorporated as nuisance regressors alongside the task-related hemodynamic response function (HRF) regressors, enabling simultaneous estimation of brain activity while regressing out superficial interference [11]. This method significantly enhances the contrast-to-noise ratio of the brain signal and reduces the risk of falsely classifying task-evoked systemic physiology instead of genuine brain activity [11]. Correct implementation within cross-validation schemes is crucial to avoid overfitting, particularly in single-trial classification contexts [11].

Comprehensive Algorithmic Approaches for Signal Processing

Beyond the GLM framework, researchers have developed multiple algorithmic strategies to leverage short-separation measurements for superficial signal regression, ranging from simple subtraction techniques to advanced adaptive filtering approaches.

Table 3: Algorithmic Methods for Short-Separation Channel Signal Processing

Algorithm	Methodological Approach	Advantages	Implementation Complexity
Simple Subtraction	Direct subtraction of SSC signal from long channel signal [60] [61]	Intuitive, computationally simple	Low (suitable for real-time applications)
Static Estimator	Fixed regression coefficient applied to SSC signal [60]	Stable performance, minimal parameter tuning	Low
Adaptive Least Mean Square (LMS)	Continuously updated regression weights based on signal characteristics [60]	Adapts to dynamic physiological changes	Medium [60]
Kalman Filter	Bayesian filtering approach with probabilistic state estimation [60]	Optimal tracking of non-stationary signals	High [60]
General Linear Model (GLM)	Statistical model with simultaneous HRF estimation and nuisance regression [11]	Integrated analysis, robust statistical framework	Medium-High [11]

Experimental Protocols for Implementation

Protocol 1: Resting-State Functional Connectivity with SSCs

Resting-state fNIRS experiments measure intrinsic brain activity through low-frequency fluctuations (~0.1 Hz) in the hemodynamic response without task presentation, primarily to investigate functional connectivity between brain regions [55]. This design is particularly valuable for studying typical and atypical brain development, neurological disorders, and intervention effects [55].

Procedure:

• Participant Preparation: Position the participant in a comfortable sitting or lying position in a quiet, light-dimmed environment to minimize distractions [55].
• Optode Placement: Implement a probe design incorporating both standard channels (25-35 mm separation) and short-separation channels (8-12 mm for adults) based on the targeted brain regions and configuration strategy [60] [59].
• Data Acquisition: Record a minimum of 8-10 minutes of resting-state data to ensure stable functional connectivity metrics, with participants instructed to keep their eyes either closed or focused on a fixation cross [55].
• Signal Processing: Apply a bandpass filter (0.01-0.1 Hz) to isolate low-frequency fluctuations, then implement GLM-based regression using short-separation channels as nuisance regressors [55] [11].
• Connectivity Analysis: Compute correlation matrices between cleaned fNIRS signals from different brain regions and apply appropriate multiple comparisons correction [55].

Protocol 2: Task-Based Activation Studies with SSCs

Task-based fNIRS experiments employ block or event-related designs to evoke hemodynamic responses through controlled cognitive, motor, or sensory tasks.

Procedure:

• Experimental Design: For block designs, include a sufficient number of trials (typically 8-12 blocks per condition) with appropriate inter-block intervals (15-30 seconds) to allow hemodynamic responses to return to baseline [62].
• Optode Placement: Use the fNIRS Optodes' Location Decider (fOLD) toolbox to determine optimal optode positions based on photon transport simulations and maximize anatomical specificity to targeted brain regions-of-interest [63].
• Short-Separation Integration: Implement a local or symmetrical-local configuration strategy with one short-separation channel for each standard channel or symmetrical channel pair [60].
• Data Acquisition: Monitor data quality in real-time using signal quality metrics, rejecting channels with poor signal-to-noise ratio or excessive motion artifacts [62].
• GLM Analysis: Construct a design matrix with separate regressors for each experimental condition, short-separation signals, and additional physiological confounds, then estimate condition-specific hemodynamic responses [11] [62].

Protocol 3: Naturalistic Paradigms with Wearable fNIRS Systems

Recent technological advances have enabled wearable, wireless fNIRS systems that facilitate data collection in real-world settings, offering enhanced ecological validity for studying brain function during natural behaviors [29].

Procedure:

• System Setup: Utilize a wireless, portable multichannel fNIRS device with integrated short-separation capabilities, potentially augmented with reality guidance for reproducible device placement [29].
• Experimental Design: Implement ecologically valid tasks such as face-to-face interactions, navigation of real environments, or performance of occupational activities while maintaining sufficient task structure for meaningful hemodynamic response estimation [29].
• Motion Artifact Handling: Apply robust motion artifact correction algorithms (e.g., wavelet-based methods, spline interpolation) before implementing short-separation regression to address increased movement in naturalistic settings [62].
• Data Processing: Employ advanced processing pipelines that combine short-separation regression with other denoising techniques appropriate for naturalistic data, potentially including accelerometer-based motion parameter incorporation [29].

The Scientist's Toolkit: Essential Research Reagents and Materials

Table 4: Essential Materials for fNIRS Research with Short-Separation Channels

Item	Specifications	Function/Purpose	Example Systems
fNIRS System with SSC Capability	Continuous wave system with dedicated short-separation detector inputs	Enables simultaneous acquisition of superficial and cerebral signals	Artinis PortaLite MKII, OxyMon, OctaMon, Brite [60] [61]
Optodes and Headgear	Miniaturized optodes capable of <10mm spacing	Physical implementation of short-separation channels on the scalp	Custom designs based on 10-5 or 10-10 international systems [63]
Photon Migration Simulation Software	Monte Carlo simulation tools (e.g., MCX)	Models light propagation in tissue and optimizes probe design [59] [63]	Monte Carlo eXtreme (MCX) [63]
Probe Design Toolbox	Automated optode placement software	Determines optimal optode positions for targeting specific brain regions	fNIRS Optodes' Location Decider (fOLD) [63]
Analysis Pipeline Software	GLM implementation with SSC nuisance regression	Processes fNIRS data with integrated superficial signal removal	Homer2, NIRS-KIT, OxySoft [60] [11]
Wearable fNIRS Platform	Wireless, self-administered systems with AR guidance	Enables naturalistic data collection in real-world settings	Custom wearable fNIRS headbands [29]

The integration of short-separation channels with advanced algorithmic approaches represents a critical methodological advancement in addressing the fundamental challenge of superficial contamination in fNIRS signals. When implemented according to the protocols and specifications outlined in this document, researchers can significantly enhance the accuracy and reliability of fNIRS measurements across diverse experimental contexts from controlled laboratory settings to naturalistic environments. As the field progresses toward standardized analysis pipelines and enhanced reporting practices [34] [62], the systematic incorporation of short-separation methodologies will play an increasingly vital role in establishing fNIRS as a robust and valid neuroimaging tool for basic research and clinical applications. Future developments will likely focus on optimizing probe designs for specific populations, refining automated processing pipelines, and further validating these methods against established neuroimaging modalities in diverse experimental contexts.

The integration of functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG) provides a powerful multimodal approach for investigating brain function in naturalistic settings. This integration leverages the complementary strengths of each modality: fNIRS offers good spatial resolution by measuring hemodynamic responses (changes in oxygenated and deoxygenated hemoglobin concentration) associated with neural activity, while EEG provides excellent temporal resolution by directly recording electrical potentials from neuronal populations [64] [65]. The neurovascular coupling—the inherent relationship between neural electrical activity and subsequent hemodynamic changes—forms the physiological basis for combining these modalities [65]. This combination is particularly valuable for naturalistic research because both technologies are portable, relatively robust to motion artifacts, and can be used in real-world environments outside traditional laboratory settings [64] [66]. This article outlines practical hardware integration strategies, protocols, and analytical considerations for implementing concurrent fNIRS-EEG systems in research applications.

Hardware Integration Strategies

Headset Design and Co-Registration

Successfully integrating fNIRS and EEG hardware requires careful consideration of headset design to ensure proper sensor placement and signal quality. The primary challenge involves sharing limited scalp space between fNIRS optodes and EEG electrodes while maintaining the technical requirements of each modality [67].

Common Integration Approaches:

• EEG-Centric Caps with fNIRS Integration: Using a standard EEG electrode cap as a foundation, with punctures made at specific locations to accommodate fNIRS probe fixtures [64]. This approach benefits from standardized EEG positioning but may lead to inconsistent fNIRS optode placement due to cap stretchability.
• fNIRS-Centric Caps with EEG Integration: Utilizing custom fNIRS headcaps (e.g., neoprene) with printed EEG 10-20 system locations for electrode placement [67]. This offers superior fNIRS optode stability but may require adapters for certain EEG electrodes.
• Customized Helmets: Employing 3D printing or cryogenic thermoplastic sheets to create customized joint-acquisition helmets tailored to experimental requirements and individual head shapes [64]. This provides optimal sensor placement but at higher cost and complexity.

Table 1: Headset Design Approaches for fNIRS-EEG Integration

Approach	Description	Advantages	Limitations
EEG-Centric Cap	fNIRS probes integrated into existing EEG electrode caps	Leverages standardized EEG systems; Lower cost	Potential fNIRS optode movement; Variable source-detector distances
fNIRS-Centric Cap	EEG electrodes added to structured fNIRS headcaps	Superior fNIRS optode stability; Consistent geometry	May require electrode adapters; Potentially limited EEG coverage
Customized Helmets	3D-printed or thermoplastic custom-fit solutions	Optimal sensor placement; Individualized fit	Higher cost; Increased fabrication time
Combined Holders	Integrated holders accommodating both sensors simultaneously	Minimal inter-sensor distance; Compact design	Higher potential for crosstalk; Technical complexity

For optimal coverage, researchers typically place EEG electrodes according to standard systems (e.g., 10-20 system) and position fNIRS optodes in between, maintaining an inter-optode distance of approximately 30 mm to ensure proper sensitivity to cerebral hemodynamics [67]. The fNIRS template should be selected based on the size and shape of the target brain area, potentially using alternative sub-templates or reducing EEG channel count when spatial constraints exist [67].

Signal Acquisition and Synchronization

Precise temporal synchronization between fNIRS and EEG data streams is crucial for multimodal analysis. Two primary integration methods exist:

Dual-System Integration: fNIRS and EEG data are acquired using separate systems (e.g., NIRScout and BrainAMP) synchronized via a host computer for subsequent analysis [64]. While simpler to implement, this approach may lack the precision required for microsecond-level EEG analysis.

Unified Processor Integration: A single processor simultaneously acquires and processes both EEG and fNIRS signals [64]. This method offers more precise synchronization and streamlined analysis but requires more complex system design.

Table 2: fNIRS-EEG Synchronization Methods

Method	Implementation	Synchronization Precision	Complexity
Dual-System	Separate devices synchronized via host computer	Moderate (millisecond range)	Lower
Unified Processor	Single device processes both modalities	High (microsecond range)	Higher
Trigger-Based	TTL pulses mark events in both data streams	Good for event marking	Moderate
Software Control	Experiment control software coordinates acquisition	Variable	Low to Moderate

Synchronization is typically achieved through TTL (Transistor-Transistor Logic) signaling, where the stimulus presentation computer sends simultaneous triggers to both acquisition systems [68]. For example, integrating with Brain Products hardware involves "TCP/IP communication to configure and control BrainVision Recorder and TTL signaling to mark critical trial events" [68].

Crosstalk Mitigation

A significant technical challenge in fNIRS-EEG integration is electromagnetic crosstalk, where fNIRS optodes' firing can introduce artifacts into EEG recordings due to electromagnetic fields generated by the driving currents controlling light sources [69]. Fortunately, research demonstrates that with proper design, high-quality, interference-free EEG recordings are achievable even when electrodes and optodes are co-located.

Strategies to Minimize Crosstalk:

• Maintain Low Electrode Impedances: Keeping EEG electrode impedances below 5 kΩ significantly reduces susceptibility to crosstalk [69].
• Use Shielded Cables: Actively shielded EEG cables reduce susceptibility to electromagnetic interference [69].
• Optimize fNIRS Sampling Rates: Configuring fNIRS systems to high sampling frequencies (≥50 Hz) prevents overlap with typical EEG frequency bands of interest [69].
• Strategic Sensor Placement: When possible, placing EEG electrodes and fNIRS optodes a small distance apart (e.g., 30 mm) reduces interference [69].

Evidence from Rogers et al. (cited in [69]) confirms that with proper implementation, "no observable peaks at the optode firing frequency" appear in EEG spectral analysis, even with co-located sensors.

Experimental Protocols for Naturalistic Research

Protocol 1: Collaborative Learning Dyads (fNIRS Hyperscanning)

This protocol measures inter-brain synchrony (IBS) during collaborative learning tasks, capturing the neurobiological underpinnings of social interaction in naturalistic educational environments [70].

Preparation:

• Participants: 40 dyads (mean age 22.1±1.2 years)
• Materials: Homemade NIRS caps created from elastic swimming caps with optode holder grids; two cap sizes accommodate different head sizes
• Probe Placement: Cover prefrontal and left temporoparietal regions using the 10-20 system for positioning
• fNIRS System: Hitachi ETG-7100 Optical Topography System capable of 92-channel measurement

Procedure:

• Resting-State Session (5 minutes): Participants relax and keep still while baseline fNIRS and EEG data are collected.
• Collaborative Session (15-20 minutes): Dyads engage with learning materials together while simultaneous fNIRS-EEG recordings capture brain activity during interaction.
• Data Synchronization: Psychology software (E-Prime 2.0) starts the fNIRS measurement system and sends triggers marking rest and collaborative learning phases in the recorded data.

Analysis Pipeline:

• Preprocessing using HOMER2 toolbox for fNIRS data
• Calculation of inter-brain synchrony of oxygenated hemoglobin (Oxy-Hb) signals
• Statistical analysis to correlate neural synchrony with behavioral measures of collaboration

This protocol enables "investigating the concurrent brain activity of two or more interactive individuals" in naturalistic learning environments, providing insights into the neural basis of social learning [70].

Protocol 2: Motor Execution, Observation, and Imagery

This protocol examines neural correlates of the Action Observation Network (AON) during three conditions (motor execution, observation, and imagery) using simultaneous fNIRS-EEG recordings [71].

Preparation:

• Participants: 60 healthy adults (18-65 years)
• Materials: 24-channel continuous-wave fNIRS system (Hitachi ETG-4100) embedded within a 128-electrode EEG cap (Electrical Geodesics)
• Probe Placement: Optodes positioned over sensorimotor and parietal cortices to index AON hemodynamic activity
• Inter-optode spacing: 2.88±0.13 cm (variation due to head size differences)
• Digitization: Use a 3D-magnetic space digitizer (Polhemus Fastrak) to record optode positions relative to anatomical landmarks (nasion, inion, preauricular points)

Procedure:

• Participants sit face-to-face with an experimenter across a table.
• Three experimental conditions are performed:
  • Motor Execution (ME): Participant grasps and moves a cup with their right hand upon audio cue ("Your turn")
  • Motor Observation (MO): Participant observes experimenter performing the same cup-moving task upon audio cue ("My turn")
  • Motor Imagery (MI): Participant mentally imagines performing the cup-moving task without physical movement
• Simultaneous fNIRS-EEG recordings capture both hemodynamic and electrical neural activity during all conditions.

Analysis Approach:

• Unimodal analysis of fNIRS (hemodynamic response) and EEG (electrical activity) data separately
• Multimodal data fusion using structured sparse multiset Canonical Correlation Analysis (ssmCCA) to identify brain regions consistently detected by both modalities
• Comparison of activation patterns across the three conditions to identify shared and distinct neural mechanisms

This protocol demonstrates how "simultaneous collection of these modalities would help characterize the AON" and validate findings across complementary neural signals [71].

Essential Research Materials and Tools

Table 3: Research Reagent Solutions for fNIRS-EEG Experiments

Item	Function/Purpose	Example Products/Specifications
fNIRS System	Measures hemodynamic responses via near-infrared light	Hitachi ETG-7100 or ETG-4100; Continuous-wave systems with 695nm & 830nm wavelengths
EEG System	Records electrical brain activity	Electrical Geodesics 128-electrode cap; ANT Neuro 64-channel system
Head Caps	Secure sensor placement with accurate positioning	Neoprene fNIRS caps with 10-20 markings; Elastic EEG caps with optode holder grids
3D Digitizer	Records precise sensor locations relative to head landmarks	Polhemus Fastrak; Patriot 3D Digitizer
Stimulus Presentation Software	Presents experimental paradigm and sends synchronization triggers	E-Prime 2.0; Presentation; PsychToolbox
Data Analysis Toolboxes	Processes and analyzes multimodal data	HOMER2/HOMER3 (fNIRS); EEGLAB; NIRS Toolbox
Synchronization Hardware	Ensures temporal alignment of multimodal data streams	TTL trigger cables; USB to TTL devices; Parallel port connectors

Data Analysis Approaches

Multimodal fNIRS-EEG data analysis typically follows three methodological categories [65]:

• EEG-Informed fNIRS Analyses: Using EEG features (e.g., event-related potentials or spectral power) to inform the analysis of fNIRS data, leveraging EEG's superior temporal resolution.

• fNIRS-Informed EEG Analyses: Utilizing hemodynamic responses from fNIRS to guide EEG analysis, taking advantage of fNIRS's better spatial specificity.

• Parallel fNIRS-EEG Analyses: Analyzing both modalities separately and comparing or integrating results at the interpretation stage.

Advanced fusion techniques like structured sparse multiset Canonical Correlation Analysis (ssmCCA) enable true multimodal integration by identifying latent variables that capture shared variance between electrical and hemodynamic signals [71]. This approach has successfully revealed consistent activation in the left inferior parietal lobe, superior marginal gyrus, and post-central gyrus during motor tasks that were not fully apparent in unimodal analyses [71].

Implementation Workflow

The following diagram illustrates the complete workflow for designing and implementing a compatible fNIRS-EEG study in naturalistic settings:

Integrating fNIRS and EEG technologies creates a powerful multimodal platform for investigating brain function in naturalistic settings. Successful implementation requires careful attention to hardware compatibility, headset design, synchronization methods, and crosstalk mitigation. The protocols and methodologies outlined provide researchers with practical frameworks for studying complex brain functions during ecologically valid tasks including collaborative learning, motor processing, and other real-world activities. As mobile neuroimaging technologies continue to advance, simultaneous fNIRS-EEG systems offer unprecedented opportunities to bridge the gap between laboratory findings and natural brain functioning, ultimately enhancing our understanding of the human brain in real-world contexts.

Establishing Credibility: How fNIRS Complements and Validates Against Gold Standards

Functional Near-Infrared Spectroscopy (fNIRS) and Functional Magnetic Resonance Imaging (fMRI) represent two pivotal techniques in modern neuroimaging, each deriving its strengths from fundamentally different physical principles. fNIRS is an optical imaging technique that utilizes near-infrared light (650–950 nm) to measure hemodynamic changes associated with neural activity. By projecting light through the scalp and skull and measuring its attenuation after passing through cerebral tissue, fNIRS quantifies concentration changes in oxygenated (HbO) and deoxygenated hemoglobin (HbR) in the cortical surface [72] [73]. The modified Beer-Lambert law is applied to convert changes in light absorption into quantitative hemoglobin changes, providing an indirect measure of neural activity via neurovascular coupling [33].

In contrast, fMRI detects brain activity by measuring the Blood Oxygen Level Dependent (BOLD) signal, which reflects changes in blood oxygenation, flow, and volume related to neuronal firing. The BOLD signal arises from the magnetic susceptibility differences between oxyhemoglobin (diamagnetic) and deoxyhemoglobin (paramagnetic). When neural activity increases, a localized hemodynamic response delivers oxygenated blood, decreasing deoxyhemoglobin concentration and increasing the T2* relaxation time of hydrogen nuclei, which is detected as an increased signal on MRI [72] [73]. This neurovascular response typically lags behind neural activity by 4–6 seconds, fundamentally limiting fMRI's temporal resolution [72].

The partnership between these modalities emerges from their complementary characteristics. fNIRS offers superior temporal resolution (often millisecond-level precision) and considerable portability, allowing measurements in naturalistic settings [72] [9]. However, it is limited to superficial cortical regions (1-3 cm depth) with spatial resolution typically ranging from 1-3 centimeters [72] [3]. fMRI provides unparalleled spatial resolution (millimeter-level), whole-brain coverage including deep structures, but requires expensive, immobile equipment and is highly sensitive to motion artifacts [72] [3]. This inherent complementarity creates a powerful framework for brain research, particularly in naturalistic designs where fNIRS can validate portable applications while fMRI provides precise anatomical localization.

Quantitative Comparison: Spatial and Temporal Resolution Profiles

Table 1: Technical and Operational Comparison Between fMRI and fNIRS

Feature	fMRI	fNIRS
Spatial Resolution	Millimeter-level (high) [72]	1-3 centimeters (moderate) [72]
Temporal Resolution	0.33-2 Hz (limited by hemodynamic response) [72]	Millisecond to second-level (high) [72]
Penetration Depth	Whole-brain (including subcortical structures) [72]	Superficial cortex (1-2.5 cm) [72] [74]
Portability	Low (requires fixed scanner environment) [3]	High (wearable systems available) [9] [74]
Motion Tolerance	Low (highly sensitive to motion artifacts) [72]	Moderate (more tolerant to movement) [74]
Measurement Basis	BOLD signal (ratio of HbO/HbR) [15]	Direct HbO and HbR concentration changes [73]
Naturalistic Research Suitability	Low (constrained laboratory environment) [3]	High (suitable for real-world settings) [9] [75]
Operational Costs	Very high [72]	Relatively lower [74]

Table 2: Hemodynamic Signal Characteristics Across Modalities

Parameter	fMRI BOLD Signal	fNIRS HbO	fNIRS HbR
Physiological Basis	Magnetic susceptibility from deoxyhemoglobin [73]	Concentration changes in oxygenated blood [33]	Concentration changes in deoxygenated blood [33]
Typical Response to Neural Activation	Signal increase (due to decreased HbR) [73]	Signal increase [15]	Signal decrease [15]
Temporal Relationship	Delayed 4-6 seconds post-stimulus [72]	Delayed 2-6 seconds post-stimulus [74]	Delayed 2-6 seconds post-stimulus [74]
Correlation Between Modalities	Reference standard	Moderate to high correlation [15]	Moderate to high (inverse) correlation [15]
Sensitivity to Physiological Noise	High [72]	High (cardiac, respiratory, Mayer waves) [76]	High (cardiac, respiratory, Mayer waves) [76]

Methodological Protocols for Multimodal Integration

Synchronous fMRI-fNIRS Acquisition Protocol

Objective: To simultaneously capture high spatial resolution data (fMRI) with high temporal resolution cortical hemodynamics (fNIRS) for validating fNIRS signals against the BOLD response and enhancing temporal characterization of brain activity.

Materials and Equipment:

• 3T MRI scanner with compatible head coil
• MRI-compatible fNIRS system (e.g., NIRSport2) [15]
• fNIRS optodes with magnetic resonance-compatible materials
• Synchronization trigger box or shared clock system
• Physiological monitoring equipment (pulse oximeter, respiratory belt)

Step-by-Step Procedure:

• Participant Preparation: Screen for MRI contraindications. Explain experimental procedures and obtain informed consent. Position participant in scanner, minimizing head movement using foam padding.
• fNIRS Probe Placement: Arrange fNIRS optodes over regions of interest (e.g., motor cortex, prefrontal cortex) using international 10-20 system for localization [15]. Ensure secure attachment and light-tight contact.
• Hardware Synchronization: Connect fNIRS system to MRI scanner via TTL pulse triggers to synchronize data acquisition timelines precisely.
• Quality Assessment: Conduct initial fNIRS signal quality check while participant is in scanner position. Prune channels with insufficient signal-to-noise ratio (SNR < 15 dB) [15].
• Anatomical Reference Scan: Acquire high-resolution T1-weighted anatomical scan (MPRAGE sequence: 176 slices, TE: 3.42 ms, TR: 2530 ms, 1mm³ voxels) for co-registration [15].
• Simultaneous Data Acquisition: Implement block-design or event-related paradigm. For motor tasks: bilateral finger tapping sequences (e.g., 1-2-1-4-3-4 finger sequence at 2Hz) in 30-second blocks interspersed with baseline [15]. Acquire fMRI (EPI sequence: TR=1500ms, TE=30ms, 3×3mm in-plane resolution) while recording fNIRS data continuously at 5.08 Hz or higher.
• Physiological Monitoring: Record cardiac and respiratory fluctuations throughout session for noise modeling.

Data Processing Pipeline:

• fMRI Preprocessing: Perform slice timing correction, motion realignment, spatial smoothing (Gaussian filter FWHM=6mm), and normalization to standard space (e.g., Talairach) [15].
• fNIRS Preprocessing: Convert raw intensity to optical density, then to hemoglobin concentrations using modified Beer-Lambert law [33]. Apply bandpass filtering (0.01-0.5 Hz) to remove physiological noise [33] [76]. Incorporate short-distance channel regression for superficial noise removal [15].
• Temporal Alignment: Use synchronization pulses to align fNIRS and fMRI data streams. Resample fNIRS data to match fMRI TR.
• Statistical Analysis: For fMRI, implement General Linear Model (GLM) with task regressors. For fNIRS, apply GLM with hemodynamic response function convolution. Conduct cross-modal correlation analysis between BOLD and HbO/HbR signals.

Naturalistic fNIRS Protocol with fMRI Ground Truth

Objective: To develop ecologically valid experimental paradigms that leverage fNIRS portability for naturalistic settings while using fMRI for anatomical validation and spatial localization.

Materials and Equipment:

• Wearable fNIRS system (e.g., portable continuous wave system) [9]
• MRI scanner for separate anatomical and functional localizer sessions
• Virtual reality equipment or naturalistic task materials
• Behavioral response recording system

Step-by-Step Procedure:

• fMRI Localizer Session: Conduct initial fMRI scanning session using task paradigms designed to activate regions of interest (e.g., motor imagery for premotor cortex, n-back for prefrontal cortex) [15] [9]. Acquire high-resolution anatomical reference.
• Individual Activation Mapping: Identify subject-specific regions of interest from fMRI localizer data using GLM contrast analysis (e.g., MA > Baseline for primary motor cortex) with FDR correction (q<0.005) [15].
• fNIRS Probe Placement: Customize fNIRS optode placement based on individual fMRI activation maps to ensure coverage of functionally defined regions.
• Naturalistic Task Implementation: Administer ecologically valid tasks in realistic environments. Example: Social media assessment measuring prefrontal cortex activity during executive function tasks (n-back, Go/No-Go) before and after social media exposure [9]. Record behavioral performance (accuracy, reaction time).
• fNIRS Data Collection: Acquire continuous fNIRS data throughout naturalistic task performance. For social media study: 50 participants, 5.08 Hz sampling, 16 sources, 15 detectors covering bilateral prefrontal regions [9].
• Motion Management: Implement movement-tolerant protocols including secure headgear, motion artifact detection algorithms, and task designs accommodating natural movement.

Data Analysis Framework:

• fNIRS Processing: Employ preprocessing pipeline including channel pruning, motion artifact correction (e.g., wavelet-based methods), bandpass filtering (0.01-0.5 Hz), and conversion to hemoglobin concentrations [33] [9].
• Individual-Level Analysis: Compute task-related hemodynamic responses (block averaging or GLM) for each participant. Extract contrast values between conditions (e.g., post-social media vs. pre-social media) [9].
• Group-Level Analysis: Implement random effects models to assess consistent activation patterns across participants. Correlate hemodynamic changes with behavioral measures.
• Satial Localization Validation: Project fNIRS activation onto individual anatomical space using photon migration models informed by optode positions co-registered to MRI-derived head anatomy.

Naturalistic fNIRS with fMRI Ground Truth Workflow

Signaling Pathways and Neurovascular Coupling

The physiological foundation unifying fMRI and fNIRS measurements is neurovascular coupling—the mechanism linking neuronal activity to subsequent hemodynamic changes. Understanding this pathway is essential for interpreting signals from both modalities and explaining their temporal discrepancies.

Neurovascular Coupling Sequence:

• Neuronal Activation: Glutamatergic synaptic transmission leads to postsynaptic neuronal firing, consuming ATP and increasing local energy demands [73].
• Metabolic Signaling: Increased neural activity elevates glutamate reuptake by astrocytes, triggering calcium signaling cascades that stimulate arachidonic acid metabolism [73].
• Vasodilation: Astrocytic endfeet surrounding cerebral arterioles release vasoactive compounds (including prostaglandins, epoxyeicosatrienoic acids, and nitric oxide), causing arteriolar dilation [73].
• Hemodynamic Response: Vasodilation increases cerebral blood flow (CBF) and volume (CBV), delivering oxygenated blood. This occurs after a 1-2 second delay post-neural activity initiation [73].
• Initial Dip (1-2s): Brief increase in oxygen consumption slightly raises deoxyhemoglobin concentration, detectable as a small signal decrease in fMRI and potential increase in fNIRS HbR [73].
• Oversupply Phase (3-6s): Blood flow increases disproportionately to oxygen consumption, causing a decrease in deoxyhemoglobin and increase in oxyhemoglobin. This produces the primary BOLD signal increase in fMRI and HbO increase/HbR decrease in fNIRS [73].
• Post-Stimulus Undershoot: After stimulus cessation, transient blood volume decrease while oxygen consumption normalizes creates a brief signal decrease in fMRI and complex HbO/HbR dynamics in fNIRS.

Neurovascular Coupling and Signal Detection

The Scientist's Toolkit: Essential Research Reagents and Materials

Table 3: Essential Equipment and Analytical Tools for Multimodal fNIRS-fMRI Research

Category	Specific Item/Technique	Function/Purpose	Example Specifications
fNIRS Hardware	Continuous Wave fNIRS System	Measures light attenuation to calculate HbO/HbR concentration changes	NIRSport2: 16 sources (760/850nm), 15 detectors, 5.08Hz sampling [15]
fMRI Hardware	3T MRI Scanner with Head Coil	Acquires BOLD signal and high-resolution anatomical reference	Siemens Magnetom Trio: EPI sequence, TR=1500ms, TE=30ms, 3×3mm resolution [15]
Synchronization	TTL Trigger Interface	Synchronizes fNIRS and fMRI data acquisition timelines	MRI-compatible trigger box sending 5V pulses at scan onset [15]
Optode Placement	International 10-20 System	Standardized positioning of fNIRS optodes for reproducible placement	EEG cap with pre-defined fNIRS-compatible openings [74]
Short-Distance Detectors	Supplemental Optode Pairs	Measures superficial signals for physiological noise regression	8mm source-detector separation for extracerebral signal estimation [15]
Data Processing	HOMER3 Software	Comprehensive fNIRS data processing pipeline	MATLAB-based toolbox for conversion, filtering, and GLM analysis [15]
fMRI Analysis	BrainVoyager QX	fMRI preprocessing and statistical analysis	Slice timing correction, motion realignment, spatial smoothing (FWHM=6mm) [15]
Physiological Monitoring	Pulse Oximeter/Respiratory Belt	Records cardiac and respiratory fluctuations for noise modeling	Integration with fNIRS/fMRI for physiological noise regression [76]
Motion Correction	Accelerometer/Algorithmic Correction	Detects and corrects for motion artifacts in fNIRS signals	Wavelet-based motion correction algorithms in HOMER3 [33]

Advanced Applications in Naturalistic Research Design

The complementary partnership of fNIRS and fMRI enables innovative research approaches, particularly in naturalistic settings that bridge laboratory constraints with real-world validity. Several advanced applications demonstrate this synergy:

Social Media Impact Assessment: A recent study utilized wearable fNIRS to examine immediate effects of social media use on executive function in college students. Participants completed n-back and Go/No-Go tasks before and after social media exposure while fNIRS measured prefrontal cortex activity. Results showed reduced accuracy post-exposure with altered PFC activation patterns: increased medial PFC activity (suggesting compensatory effort) but decreased dorsolateral and ventrolateral PFC activation (indicating working memory and inhibition impairments) [9]. This protocol demonstrates fNIRS's utility in ecological assessment of technology impacts on cognition, with potential for fMRI to provide detailed localization of affected networks.

Developmental Social Neuroscience: Novel fNIRS protocols enable study of neural bases of sustained attention during naturalistic parent-infant interactions—previously challenging with fMRI. This approach identified left temporo-parietal involvement during infant sustained attention, validating fNIRS for developmental social neuroscience [75]. The naturalistic paradigm maintains ecological validity while capturing neural dynamics, with fMRI serving as ground truth for anatomical localization.

Clinical Translation and Neurorehabilitation: Combined approaches show promise in stroke rehabilitation, where fNIRS provides continuous bedside monitoring of cortical reorganization while fMRI offers periodic high-resolution assessment of recovery progress. The portability of fNIRS enables assessment during actual rehabilitation exercises, providing insights into neural mechanisms of recovery in ecologically valid contexts [72] [3].

These applications highlight the powerful synergy wherein fNIRS captures temporal dynamics in realistic settings, while fMRI provides spatial precision for localization and validation, together creating a more complete picture of brain function across laboratory and real-world contexts.

Functional near-infrared spectroscopy (fNIRS) is an increasingly valuable tool for studying brain function in naturalistic settings, owing to its portability, relative low cost, and robustness to motion. However, its interpretation relies on a critical foundation: the validity of its hemodynamic response signals. Concurrent functional magnetic resonance imaging (fMRI) and fNIRS studies provide a powerful means for this validation, leveraging fMRI's established role as a gold standard in brain mapping to confirm the physiological meaning of fNIRS signals [77] [78]. This protocol details the methodologies for such concurrent studies, framed within the broader thesis that cross-validated fNIRS is pivotal for advancing ecologically valid neuroimaging research outside the scanner environment.

The core premise of these studies is that the Blood Oxygenation Level-Dependent (BOLD) signal measured by fMRI and the hemoglobin concentration changes measured by fNIRS are physiologically interrelated. Both signals are indirect measures of neuronal activity, stemming from the hemodynamic response following neural activation [78]. The BOLD signal is sensitive to changes in deoxygenated hemoglobin (deOxy-Hb), while fNIRS can independently quantify changes in both oxygenated (Oxy-Hb) and deoxygenated hemoglobin [77]. Concurrent measurements allow researchers to directly correlate these signals, thereby validating fNIRS against a well-understood metric and improving the accuracy of fNIRS in separating cerebral activity from superficial, extracerebral contributions [79].

Experimental Design and Paradigms

Selecting an appropriate experimental design is the first critical step in a concurrent validation study. The design must evoke a robust and well-localized hemodynamic response to facilitate a clear correlation between the two modalities.

Paradigm Selection

The table below summarizes common task paradigms used in concurrent fNIRS-fMRI studies.

Table 1: Common Experimental Paradigms for Concurrent fNIRS-fMRI Studies

Paradigm	Description	Targeted Brain Region	Key fNIRS-fMRI Validation Findings
Blocked Design	Alternating periods of task activity and rest/baseline, typically 20-30 seconds per block [80].	Motor cortex, prefrontal cortex, visual cortex.	Provides high signal-to-noise ratio, facilitating the detection of correlated signal changes between BOLD and HbO2 [80] [78].
Event-Related Design	Short, discrete trials presented in a randomized order with jittered inter-trial intervals [80].	Prefrontal cortex (for higher cognition).	Allows analysis of the temporal shape of the Hemodynamic Response Function (HRF); concurrent studies can validate if fNIRS captures the canonical HRF [78].
Naturalistic Stimuli	Use of continuous, ecologically valid stimuli like auditory narratives or video clips [81].	Prefrontal cortex, language networks.	Enables validation in more realistic contexts. Studies show higher intersubject correlation (ISC) in fNIRS for intact narratives vs. scrambled versions, correlating with fMRI findings on narrative processing [81].

The Hemodynamic Response Function

A cornerstone of concurrent studies is the characterization of the Hemodynamic Response Function. In response to a brief neural event, the HRF typically rises to a peak around 5-6 seconds post-stimulus before returning to baseline, sometimes with a slight undershoot [78]. In fMRI analysis, this is often modeled using a canonical HRF. Concurrent fNIRS-fMRI measurements allow researchers to verify that the fNIRS-measured Oxy-Hb and deOxy-Hb time courses align with the expected temporal dynamics of the BOLD signal, which is inversely related to deOxy-Hb [77] [78].

The following diagram illustrates the typical workflow for a concurrent fNIRS-fMRI study, from participant preparation to data analysis.

Technical Setup and Data Acquisition

A successful concurrent study requires meticulous integration of fNIRS and fMRI hardware and synchronization of their data streams.

fNIRS Setup within the MRI Environment

fNIRS systems used in MRI scanners must be specially designed for compatibility.

• MRI-Compatible Hardware: All fNIRS components inside the scanner room, including optodes and fiber optics, must be non-magnetic and non-metallic to ensure patient safety and prevent image artifacts [77]. Systems like the NIRx NIRSport and NIRScout can be adapted with MRI-compatible modules [77].
• Optode Placement and Coregistration: The fNIRS optode array is typically mounted on an elastic cap positioned on the participant's head. Precise coregistration of fNIRS channels to anatomical locations is critical. This is achieved by measuring the 3D coordinates of optodes (e.g., using a digitizer) and overlaying them on the participant's high-resolution anatomical MRI scan. This allows for mapping fNIRS channels to standard brain atlases and direct comparison with fMRI activation maps [56].
• Synchronization: A digital trigger signal from the MRI scanner should be sent to the fNIRS system at the beginning of the functional scan to synchronize the two data streams precisely [77].

Data Acquisition Parameters

Table 2: Typical Data Acquisition Parameters for Concurrent Studies

Parameter	fNIRS	fMRI
Primary Signal	Concentration changes in Oxy-Hb and deOxy-Hb [77].	Blood Oxygenation Level-Dependent (BOLD) signal, sensitive to deOxy-Hb [77] [78].
Temporal Resolution	High (~10 Hz) [77].	Lower (e.g., TR = 2 seconds) [80].
Spatial Resolution	Limited, superficial cortical regions (a few cm penetration) [77].	High, whole-brain coverage [78].
Key Acquisition Settings	Wavelengths (e.g., 760 nm, 850 nm), sample rate, source-detector separations (e.g., 3 cm for cerebral signals) [56] [79].	Time to Repetition (TR), Echo Time (TE), Field of View (FOV), voxel size [80] [78].

Core Protocol: Signal Validation Using Multidistance Optodes

A primary goal of concurrent studies is to validate methods for separating deep (cerebral) fNIRS signals from shallow (extracerebral) confounds. The following protocol, adapted from the MD-ICA method validated by simultaneous fMRI [79], provides a detailed roadmap.

Protocol Steps

• Participant Preparation & Setup: After obtaining informed consent, position the participant in the MRI scanner. Mount the fNIRS cap with multidistance optodes over the region of interest (e.g., prefrontal or motor cortex). Ensure all equipment is MRI-safe and secure the long optical fibers to prevent motion artifacts. Pre-scan, measure the 3D coordinates of all fNIRS sources and detectors.
• Concurrent Data Acquisition: Initiate the fMRI sequence (e.g., T2*-weighted EPI) and send a synchronization trigger to the fNIRS system. Have the participant perform the chosen experimental paradigm (e.g., finger tapping from Table 1). Collect simultaneous fNIRS and fMRI data throughout the task and rest periods.
• fMRI Data Preprocessing: Process the fMRI data using standard pipelines (e.g., in FSL, SPM). This includes slice-timing correction, motion correction, spatial normalization to a standard template (e.g., MNI), and spatial smoothing [78]. Statistical analysis (e.g., GLM) will generate a BOLD activation map for the task.
• fNIRS Data Preprocessing:
  • Convert Raw Data: Apply the Modified Beer-Lambert Law (MBLL) to convert raw optical intensity signals into changes in Oxy-Hb and deOxy-Hb concentrations, using appropriate differential pathlength factors [56].
  • Quality Control & Channel Rejection: Check signal quality for all channels and reject those with poor signal-to-noise ratio or excessive motion artifacts [56].
  • Signal Processing: Apply band-pass filtering (e.g., 0.01-0.2 Hz) to remove physiological noise (heart rate, respiration) and slow drifts.
• Spatial Coregistration: Use the digitized optode coordinates and the participant's anatomical MRI to map each fNIRS channel to its corresponding location in the brain and in the fMRI statistical map.
• Separate Deep and Shallow Signals (MD-ICA): This is the key validation step.
  • Input: fNIRS signals from multiple source-detector pairs with different distances (e.g., short - 1 cm for shallow signals, long - 3 cm for mixed deep and shallow signals).
  • Processing: Apply Independent Component Analysis (ICA) to the combined fNIRS data from all distance channels.
  • Output: The algorithm separates the data into independent components. Components that are strongly represented in long-distance channels and temporally correlated with the task paradigm are identified as "deep" (cerebral). Components dominant in short-distance channels are identified as "shallow" (extracerebral) [79].
• Hemodynamic Correlation Analysis: Extract the time course of the BOLD signal from the fMRI activation cluster in the brain region corresponding to the fNIRS measurement. Correlate this BOLD time course with the time courses of the "deep" fNIRS Oxy-Hb signal and the "shallow" fNIRS signal. A successful separation is indicated by a significantly higher correlation between the BOLD signal and the "deep" fNIRS signal than with the "shallow" signal [79].

The diagram below outlines the core signal processing and validation workflow.

The Scientist's Toolkit: Research Reagent Solutions

Table 3: Essential Materials and Tools for Concurrent fNIRS-fMRI Studies

Item	Function & Importance
MRI-Compatible fNIRS System	A core requirement. Systems like NIRx NIRSport/NIRScout with specialized modules ensure safety and data quality by preventing artifacts and interference with the MRI signal [77].
Multidistance Optode Probe Set	Enables the separation of deep and shallow hemodynamic signals. Short-separation channels act as a regressor for confounding physiological noise, a method validated by concurrent fMRI [79].
3D Digitizer	Critical for spatial coregistration. Accurately records the 3D positions of fNIRS optodes relative to cranial landmarks, allowing projection onto anatomical MRI scans and standard brain spaces [56].
Synchronization Hardware	A digital I/O interface to transmit TTL pulses from the MRI scanner to the fNIRS system. This ensures temporal alignment of the two data streams for precise correlation analysis.
Phantom Test Objects	Used for system validation and quality assurance before human studies. Phantoms with known optical properties help characterize system performance and ensure data reliability [56].

The quest to understand human brain function in real-world, naturalistic settings presents a significant challenge for traditional neuroimaging techniques. Functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG) have emerged as particularly suitable modalities for naturalistic research, with their integration offering a unique window into brain function by capturing complementary aspects of neural activity [82] [65]. This combination provides researchers with both the excellent temporal resolution of electrical recordings and improved spatial localization of hemodynamic responses, overcoming the fundamental limitations of either technique used in isolation [74] [83].

The theoretical foundation for fNIRS-EEG integration rests on the principle of neurovascular coupling—the intimate relationship between neuronal electrical activity and subsequent hemodynamic responses [65] [84]. When neurons become active, they trigger a complex cascade of vascular events that increases cerebral blood flow to the active region, altering the relative concentrations of oxygenated and deoxygenated hemoglobin [65] [13]. This biological partnership means that EEG and fNIRS measure two fundamentally different yet physiologically linked processes: EEG captures the direct electrical manifestations of neural processing with millisecond precision, while fNIRS tracks the indirect metabolic consequences of this activity over seconds [74] [65].

For researchers investigating cognitive processes in naturalistic environments, this synergistic approach offers distinct advantages. fNIRS provides superior tolerance to movement artifacts compared to EEG alone, making it particularly valuable for studying populations that have difficulty remaining still (e.g., children, clinical populations) or for experiments requiring ecological validity [74] [13]. Furthermore, the combined approach enables investigation of both rapid neural dynamics and sustained cognitive states within the same experimental paradigm, providing a more comprehensive understanding of brain function as it occurs in daily life [82] [85].

Technical Integration Framework

Hardware Configuration and Synchronization

Successful integration of fNIRS and EEG begins with careful hardware configuration. Modern systems typically utilize integrated caps that accommodate both EEG electrodes and fNIRS optodes following the international 10-20 or 10-5 systems for standardized placement [86] [74]. These caps are designed to minimize interference between modalities, with pre-defined openings that maintain optimal optode-electrode distance while ensuring proper scalp contact for both systems [86].

Synchronization represents a critical technical challenge in multimodal recording. The Lab Streaming Layer (LSL) protocol has emerged as a robust solution for precise temporal alignment of fNIRS and EEG data streams [86]. This open-source platform handles the collection of time-series data from various instruments with sub-millisecond precision, ensuring that electrical and hemodynamic events can be accurately correlated during analysis. Alternative synchronization methods include hardware triggers (TTL pulses) or shared clock systems, though these typically offer less flexibility than LSL for complex experimental paradigms [74].

Table 1: Technical Specifications of fNIRS-EEG Systems

Parameter	EEG Component	fNIRS Component	Integrated System
Temporal Resolution	Millisecond (ms) level [74]	Slower (seconds) [74]	Millisecond to seconds
Spatial Resolution	Limited (cm-level) [74]	Moderate (better than EEG) [74]	Enhanced spatial localization
Penetration Depth	Cortical surface [74]	Outer cortex (1-2.5 cm) [74]	Cortical and subcortical superficial layers
Portability	High (wireless systems available) [86] [74]	High (wearable systems) [86] [74]	Fully portable solutions available
Movement Tolerance	Low (susceptible to artifacts) [74]	High (relatively robust) [74]	Moderate (with motion correction)

Signal Acquisition and Preprocessing

Establishing robust preprocessing pipelines is essential for maximizing signal quality in simultaneous fNIRS-EEG recordings. The fundamentally different nature of these signals necessitates separate preprocessing workflows before integrated analysis can proceed [74] [65].

For EEG data, standard preprocessing typically includes filtering (e.g., 0.5-40 Hz bandpass), artifact removal (particularly for ocular and muscle movements), and re-referencing [65]. The simultaneous recording of fNIRS provides a unique advantage for EEG preprocessing, as fNIRS data can help identify and correct artifacts in EEG recordings through correlation analysis [83].

fNIRS preprocessing involves converting raw light intensity measurements into hemoglobin concentration changes using the Modified Beer-Lambert Law [1] [65]. Additional steps include filtering to remove cardiac (∼1 Hz) and respiratory (∼0.3 Hz) oscillations, motion artifact correction, and removal of superficial signals using short-separation channels [65] [34]. The preprocessing pipeline choice significantly impacts results, with recent studies showing that methodological variability represents a major source of inconsistency in fNIRS research [34].

Experimental Protocols for Naturalistic Research

Protocol 1: Cognitive Workload Assessment in Simulated Environments

Application Context: This protocol is designed to assess cognitive workload and fatigue in real-world scenarios such as air traffic control, surgical procedures, or driving simulation [82]. The combined fNIRS-EEG approach captures both the rapid neural oscillations associated with shifting attention and the sustained hemodynamic changes indicative of prolonged cognitive effort.

Experimental Setup:

• Participant Preparation: Apply integrated fNIRS-EEG cap with optodes focused on prefrontal cortex (PFC) and EEG electrodes distributed according to 10-20 system [86] [74]
• Baseline Recording: 5 minutes of resting-state data with eyes open
• Task Paradigm: 30-minute simulated task environment with graded difficulty levels
• Data Synchronization: LSL protocol for multimodal temporal alignment [86]

Data Acquisition Parameters:

Table 2: Acquisition Parameters for Cognitive Workload Assessment

Parameter	EEG Specifications	fNIRS Specifications
Sampling Rate	500-1000 Hz [65]	10-100 Hz [65]
Key Regions	Frontal, Parietal	Prefrontal Cortex (dlPFC, vlPFC) [82]
EEG Metrics	Theta (4-7 Hz) and Alpha (8-14 Hz) power [82]	-
fNIRS Metrics	-	HbO and HbR concentration changes [82]
Task Duration	30 minutes with alternating blocks of high/low demand

Analysis Pipeline:

• Preprocessing: Independent component analysis (ICA) for EEG artifact removal; motion correction for fNIRS [65]
• Feature Extraction:
  • EEG: Compute frontal theta/parietal alpha power ratios [82]
  • fNIRS: Extract HbO concentration from dorsolateral and ventrolateral PFC [82]
• Data Fusion: Joint Independent Component Analysis (jICA) to identify components that covary across modalities [74] [65]

Protocol 2: Impact of Digital Stimuli on Executive Function

Application Context: This protocol investigates the immediate impact of digital stimuli (e.g., social media use) on subsequent executive function tasks, particularly relevant for understanding technology's cognitive effects in naturalistic settings [85].

Experimental Design:

• Participants: 20+ subjects divided into experimental and control groups [85]
• Session Structure:
  • Pre-test executive function assessment (n-back, Go/No-Go)
  • 15-minute intervention (social media use for experimental group; control activity)
  • Post-test executive function assessment
• Neural Recording: Continuous fNIRS-EEG throughout all phases

Key Neural Metrics:

• fNIRS: Activation changes in medial PFC (performance monitoring), dorsolateral PFC (working memory), and inferior frontal gyrus (response inhibition) [85]
• EEG: Event-related potentials (ERPs) during Go/No-Go tasks, particularly the P300 component associated with inhibition

Implementation Notes:

• The naturalistic design allows data collection in environments resembling everyday conditions rather than traditional labs [85]
• Passive social media interaction (scrolling without active engagement) enhances ecological validity [85]
• Wearable fNIRS systems enable monitoring during natural behaviors with minimal restriction [85]

Data Analysis and Fusion Techniques

Multimodal Data Fusion Approaches

The integration of fNIRS and EEG data can be accomplished through several methodological frameworks, each with distinct advantages for specific research questions:

Parallel Analysis: This approach involves analyzing fNIRS and EEG data separately and comparing results at the interpretation stage [65]. While this method preserves the unique characteristics of each modality, it may miss important cross-modal interactions.

Asymmetric (Informed) Analysis: One modality guides the analysis of the other—for example, using EEG-derived features to inform the analysis of fNIRS signals or vice versa [65]. This approach is particularly valuable when one modality provides superior spatial or temporal information for a specific research question.

Hierarchical Data Fusion: This sophisticated approach combines features from both modalities into a unified model [74] [65]. Common techniques include:

• Joint Independent Component Analysis (jICA): Identifies components that covary across modalities [74] [65]
• Canonical Correlation Analysis (CCA): Finds relationships between multimodal feature sets [74]
• Machine Learning Approaches: Combine fNIRS and EEG features to classify cognitive states or outcomes [74]

Structure-Function Relationship Exploration

The combination of fNIRS and EEG enables unique investigation of how functional activity relates to underlying brain organization. Recent research has revealed that structure-function coupling varies between electrical and hemodynamic networks due to their different sensitivities to physiological mechanisms at different timescales [84].

This approach typically involves:

• Coregistering EEG electrodes and fNIRS optodes to anatomical templates
• Mapping structural and functional data to common brain atlas regions (e.g., Desikan-Killiany atlas)
• Applying graph signal processing (GSP) to quantify structure-function relationships
• Computing metrics like the Structural-Decoupling Index (SDI) to quantify regional alignment between structural and functional networks [84]

Studies using this approach have found that fNIRS structure-function coupling resembles slower-frequency EEG coupling at rest, with variations across brain states and oscillations [84]. Furthermore, this coupling demonstrates heterogeneity across brain regions, following the unimodal to transmodal gradient, with greater coupling in sensory cortex and increased decoupling in association cortex [84].

The Scientist's Toolkit

Table 3: Essential Research Reagents and Materials for fNIRS-EEG Research

Item	Specifications	Function/Purpose
Integrated fNIRS-EEG Cap	Compatible with international 10-20 system; accommodates both optodes and electrodes [86] [74]	Standardized placement of recording elements; ensures optimal optode-electrode distance
fNIRS Optodes	Sources and detectors with 30mm typical separation; wavelengths 760nm and 850nm [1] [65]	Emit and detect near-infrared light for hemodynamic measurement
EEG Electrodes	Ag/AgCl or gold-coated; wet or semi-dry formulations [83]	Detect electrical potentials from scalp surface
Lab Streaming Layer (LSL)	Open-source platform for multimodal temporal alignment [86]	Precise synchronization of fNIRS and EEG data streams
Short-Separation Detectors	fNIRS detectors placed 8mm from sources [1]	Measure superficial signals for enhanced signal quality
Analysis Software	HOMER3, NIRS Toolbox, EEGLAB, MNE-Python [1] [65]	Data preprocessing, feature extraction, and multimodal fusion

Signaling Pathways in Neurovascular Coupling

The neurovascular coupling pathway illustrates the biological foundation for fNIRS-EEG integration. Neural activity triggers a cascade of signaling events beginning with glutamate release and astrocyte activation, leading to production of vasoactive messengers that ultimately increase local cerebral blood flow [65] [84]. This hemodynamic response manifests as increased oxygenated hemoglobin (HbO) and decreased deoxygenated hemoglobin (HbR) concentrations—the primary signals measured by fNIRS [65] [13].

The temporal sequence highlights the complementary nature of EEG and fNIRS signals: EEG captures the initial electrical events with millisecond precision, while fNIRS detects the subsequent hemodynamic response that peaks 4-6 seconds after neural activation [65]. This coupling relationship forms the physiological basis for interpreting simultaneous fNIRS-EEG recordings, particularly when investigating impaired neurovascular function in neurological disorders or developmental conditions [65].

The synergy between fNIRS and EEG represents a significant advancement in neuroimaging methodology, particularly for naturalistic research designs that prioritize ecological validity. By unifying electrical and hemodynamic perspectives on brain activity, this multimodal approach provides researchers with a more comprehensive toolkit for investigating brain function as it occurs in real-world contexts.

The protocols and frameworks outlined in this application note demonstrate how combined fNIRS-EEG can capture complementary aspects of neural processing—from millisecond-scale electrical oscillations to slower hemodynamic changes reflecting metabolic demand. This integrated approach is particularly valuable for studying complex cognitive processes that unfold over multiple temporal scales, such as executive function, workload, and fatigue [82] [85].

As technological advancements continue to improve the portability and usability of both fNIRS and EEG systems, their combined application promises to bridge the gap between highly controlled laboratory environments and the complexity of everyday cognitive functioning. This transition toward more naturalistic research paradigms will undoubtedly enhance our understanding of brain function in health and disease while providing valuable insights for developing interventions across clinical, educational, and occupational domains.

Functional near-infrared spectroscopy (fNIRS) is gaining prominence in naturalistic research settings due to its portability, tolerance to motion, and applicability across diverse populations. A critical question for its use in longitudinal studies and clinical trials is whether it can achieve high test-retest reliability, especially for individualized brain mapping. Emerging evidence demonstrates that with optimized experimental designs, specific preprocessing strategies, and dense sampling approaches, fNIRS can indeed achieve excellent reliability for both resting-state and task-evoked functional brain measures. This application note synthesizes recent evidence on fNIRS reliability, presents quantitative comparisons of reliability across metrics, and provides detailed protocols for implementing reliable fNIRS imaging in naturalistic design research, directly supporting its application in clinical and pharmaceutical development contexts.

The need for neuroimaging tools that can capture brain function in real-world, ecologically valid settings is particularly acute in clinical neuroscience and drug development. Functional Near-Infrared Spectroscopy (fNIRS) offers a non-invasive method for measuring cortical hemodynamics associated with neural activity, with significant advantages for naturalistic research including portability, cost-effectiveness, and relative immunity to motion artifacts [87] [29]. Unlike fMRI, which requires rigid participant positioning, fNIRS enables brain imaging during complex tasks, social interactions, and in environments that better mimic real-life conditions [34].

For fNIRS to serve as a viable biomarker in clinical trials and longitudinal studies, it must demonstrate two key properties: high test-retest reliability (the consistency of measurements across multiple sessions) and specificity (the ability to detect individualized neural patterns distinct from group averages). Recent community-wide initiatives like the fNIRS Reproducibility Study Hub (FRESH) have systematically investigated these properties, finding that while analytical flexibility exists, researchers with greater fNIRS experience can achieve high consensus, particularly for group-level analyses [34] [88]. This document synthesizes the evolving evidence for fNIRS reliability, with particular emphasis on methodologies that enhance individualized mapping for precision medicine applications.

Quantitative Evidence for fNIRS Reliability

Reliability of Resting-State and Task-Based Metrics

Test-retest reliability for fNIRS metrics is typically quantified using Intraclass Correlation Coefficients (ICC), where values >0.5, >0.75, and >0.9 represent moderate, good, and excellent reliability, respectively. The tables below summarize reliability evidence across multiple studies and metrics.

Table 1: Test-Retest Reliability of Resting-State fNIRS Metrics

Metric Category	Specific Metric	Population	ICC Range	Key Influencing Factors
Global Network Metrics	Clustering Coefficient [89]	Healthy Adults	0.76-0.78 (HbO/HbR)	Hemoglobin species, threshold sensitivity
	Global Efficiency [89]	Healthy Adults	0.70-0.78 (HbO/HbR)	Hemoglobin species, threshold sensitivity
Nodal Centrality Metrics	Nodal Degree [89]	Healthy Adults	0.52-0.84	More reliable than betweenness
	Nodal Efficiency [89]	Healthy Adults	0.50-0.84	Consistent across HbO, HbR, and HbT
	Nodal Betweenness [87] [89]	Stroke Patients & Healthy Adults	0.28-0.68 (Generally <0.5)	Moderate to poor reliability; use with caution
Cortical Activity	Regional Activity [87]	Stroke Patients	Improves >0.5 with 4+ min scan	Scanning duration, frequency band, averaging within regions

Table 2: Reliability Across Experimental Paradigms and Populations

Paradigm	Population	Reliability Level	Notes
Resting-State [87] [89]	Healthy Adults	Moderate to Excellent (ICC: 0.50-0.90+)	Global metrics and nodal efficiency show highest reliability
Resting-State [87]	Stroke Patients	Moderate to High after 4 minutes	Scanning duration critical; low-frequency band more reliable
Auditory Task [90]	Single-Subject	Variable (Improves with correction)	Systemic Physiology Augmentation (SPA) impacts reliability
Cognitive Tasks [29]	Healthy Adults (Precision Platform)	High within-participant consistency	Dense sampling (multiple sessions) key for individual specificity

Key Factors Influencing Reliability

Evidence consistently identifies several methodological factors that significantly impact fNIRS reliability:

• Scanning Duration: For resting-state studies in clinical populations like stroke, a scanning duration of more than 4 minutes significantly enhances the reliability of most fNIRS metrics, with ICC values for the low-frequency band generally exceeding 0.5 after this timepoint [87].
• Preprocessing Strategies: The choice of algorithm affects reliability. The Hemodynamic Modal Separation (HMS) algorithm has been shown to perform best in improving ICC values for low-frequency bands, outperforming other methods like Principal Component Analysis (PCA) or Common Average Reference (CAR) in some contexts [87]. For single-subject auditory research, correcting for systemic physiology using SPA-fNIRS approaches can either enhance or reduce reliability metrics depending on the signal of interest [90].
• Data Quality and Researcher Expertise: The FRESH initiative found that teams with higher self-reported analysis confidence and more fNIRS experience showed greater agreement in their results. Furthermore, agreement was higher for hypotheses strongly supported by existing literature and improved with better raw data quality [34] [88].
• Level of Analysis: Reliability is generally higher for group-level analyses compared to individual-level analyses [34]. However, dense-sampling approaches that collect large amounts of data per individual (e.g., 70 minutes per task over ten sessions) can significantly improve the reliability and specificity of individual-level functional connectivity measures [29].

Experimental Protocols for Reliable fNIRS

Protocol for Reliable Resting-State fNIRS in Clinical Populations

This protocol is adapted from studies demonstrating high test-retest reliability in stroke patients [87].

1. Participant Preparation and Setup

• Inclusion Criteria: Define clinical population clearly (e.g., subacute stroke patients within 2 weeks to 3 months post-stroke with unilateral lesions).
• Exclusion Criteria: Screen for severe organ dysfunction, significant cognitive impairment, or unstable medical conditions.
• Probe Placement: Use the international 10-20 system for consistent optode placement. Measure and record positions for each session. Ensure good scalp contact to minimize channels with poor signal-to-noise ratio.

2. Data Acquisition

• Device Parameters: Use a continuous-wave fNIRS system with appropriate source-detector separation (e.g., 3.0-3.5 cm).
• Session Timeline: Conduct two scanning sessions separated by a relatively short, consistent interval (e.g., 24 hours for populations with potential rapid change).
• Resting-State Procedure: Instruct the participant to remain still, with eyes closed but not asleep, for the duration of the scan. Minimize environmental stimuli.
• Minimum Duration: Acquire data for at least 4 minutes of stable recording after discarding the initial stabilization period (e.g., first minute) to ensure ICCs >0.5 for low-frequency brain function metrics [87].

3. Data Preprocessing and Analysis

• Preprocessing Choice: Apply the Hemodynamic Modal Separation (HMS) algorithm to improve reliability in low-frequency bands [87].
• Frequency Filtering: Analyze signals in specific frequency bands. Note that high-frequency bands may show higher ICCs than low-frequency bands, but the latter are often of primary interest.
• Metric Calculation:
  • Calculate global network metrics (e.g., clustering coefficient, global efficiency) which show high reliability.
  • Calculate nodal metrics cautiously, preferring nodal degree and efficiency over betweenness centrality.
  • When possible, average signals within brain regions rather than relying solely on individual channel-level data, as this improves reliability.

Protocol for Dense-Sampling, Individualized fNIRS Mapping

This protocol is designed for precision mental health applications where individual-specific brain patterns are the target, based on the wearable fNIRS platform validation [29].

1. Platform and Participant Setup

• Technology: Employ a wireless, portable, multi-channel fNIRS headband.
• Guidance System: Use an augmented reality (AR) guidance system via a tablet application to ensure highly reproducible device placement across multiple self-administered sessions.
• Session Design: Plan for multiple dense-sampling sessions (e.g., 10 sessions over 3 weeks).

2. Data Acquisition Paradigm

• Multi-Task Battery: In each session, collect data during both resting-state and a battery of standardized cognitive tasks (e.g., N-back, Flanker, Go/No-Go tests).
• Task Duration: Ensure sufficient single-task duration (e.g., 7 minutes per task) to obtain stable estimates.
• Self-Administration: Train participants to self-administer the tests in a naturalistic setting (e.g., at home) using the tablet application, which synchronizes behavioral and brain activity data.

3. Data Analysis for Individual Specificity

• Functional Connectivity: Calculate pairwise correlations between channels to derive individual functional connectivity matrices for each session.
• Within-Subject Consistency: Assess test-retest reliability by comparing connectivity patterns and activation maps across all sessions for the same individual, using ICC or similar metrics.
• Individual vs. Group Comparison: Statistically test whether an individual's functional connectivity pattern is significantly more similar to their own scans from other sessions than to the group average or other individuals' scans. This demonstrates specificity.

Visualization: Dense-Sampling fNIRS Workflow for Individualized Mapping

The diagram below illustrates the integrated workflow for achieving high test-retest reliability and individual specificity through dense sampling, as validated in recent studies [29].

The Scientist's Toolkit: Essential Reagents & Materials

Table 3: Key Research Reagent Solutions for Reliable fNIRS

Item	Specification / Example	Primary Function in Protocol
CW-fNIRS System	NIRSport2 (NIRx), CW6 (TechEn)	Core hardware for emitting NIR light and detecting attenuated signals to measure hemodynamic changes.
Optodes & Probes	Sources (e.g., 690 & 830 nm LEDs), Detectors, Holder	Interface with the scalp; specific wavelengths allow quantification of oxy- and deoxy-hemoglobin.
Short-Separation Channels	Source-detector pairs ~8 mm apart	Critical for measuring and subsequently regressing out superficial, scalp-derived hemodynamic signals.
SPA-fNIRS Module	NIRx WINGS, external physiological monitors	Simultaneously records systemic physiology (e.g., heart rate, blood pressure) for use as nuisance regressors.
AR Placement Guide	Tablet application with camera	Ensures highly reproducible optode placement across multiple sessions, vital for test-retest reliability.
Preprocessing Software	Homer2, NIRS-KIT, SPA-fNIRS Plugins	Implements algorithms (HMS, PCA, CAR) for signal cleaning, physiological denoising, and statistical analysis.
Cloud Data Platform	HIPAA-compliant cloud storage	Enables secure, centralized data management from remote/at-home sessions for large-scale studies.

Functional Near-Infrared Spectroscopy (fNIRS) has emerged as a powerful neuroimaging technology that occupies a unique niche in cognitive neuroscience, particularly for research conducted in naturalistic settings. As an optical brain monitoring technique that uses near-infrared spectroscopy for functional neuroimaging, fNIRS measures brain activity by estimating cortical hemodynamic activity associated with neural activity through the use of near-infrared light [1]. The fundamental principle underlying fNIRS is its ability to measure changes in the concentration of oxygenated hemoglobin (HbO) and deoxygenated hemoglobin (HbR) in the blood, which occur as a result of neuronal activity and the subsequent hemodynamic response [91] [13]. This neurovascular coupling mechanism forms the basis for detecting functional brain activation, similar to the BOLD signal measured by fMRI, but with distinct advantages for specific research scenarios [73].

The growing importance of fNIRS in cognitive neuroscience stems from its unique combination of portability, relative motion tolerance, and safety profile, making it particularly suitable for populations and environments that challenge traditional neuroimaging methods [91]. Over the past 25 years, fNIRS has rapidly evolved from single-channel devices to sophisticated multichannel systems capable of monitoring larger portions of the head and generating topographic HbO and HbR maps [91]. This technological progress, combined with an increasing emphasis on ecological validity in neuroscience research, has positioned fNIRS as an indispensable tool for studying brain function in real-world contexts that closely resemble everyday life situations [8].

Technical Comparative Analysis: fNIRS Versus Established Neuroimaging Modalities

Quantitative Comparison of Neuroimaging Techniques

Table 1: Comprehensive comparison of fNIRS with other major neuroimaging modalities

Feature	fNIRS	fMRI	EEG	PET
Spatial Resolution	Moderate (2-3 cm) [8]	High (mm-range) [92]	Low (5-9 cm) [8]	High (mm-range) [93]
Temporal Resolution	Moderate (~0.1s) [94]	Low (1-2s) [92]	High (ms-range) [8]	Very Low (minutes)
Portability	High (wearable systems available) [8]	None (stationary) [92]	Moderate (wireless systems available) [8]	None (stationary) [93]
Participant Motion Tolerance	High [91]	Very Low [92]	Moderate (limited by motion artifacts) [8]	Very Low [93]
Measurement Depth	Superficial cortex (1-1.5 cm) [94]	Whole brain [92]	Whole brain (with poor localization)	Whole brain
Cost	Low to Moderate [92]	Very High [92]	Low [93]	Very High
Safety Profile	Excellent (non-invasive, non-ionizing) [94]	Good (but metallic implants problematic) [92]	Excellent [93]	Poor (involves radioactive tracers) [93]
Environment Requirements	Minimal (usable outside lab) [8]	Strict (shielded room) [92]	Moderate (may require shielded room)	Strict (radiochemistry lab)
Measures	HbO and HbR separately [73]	BOLD (HbO/HbR ratio) [92]	Electrical activity [93]	Metabolic activity

Key Technical Advantages of fNIRS

fNIRS provides several distinct technical advantages that make it particularly suitable for naturalistic research designs. Unlike fMRI, which only measures the blood-oxygen-level-dependent (BOLD) response reflecting changes in deoxygenated hemoglobin, fNIRS separately measures both oxygenated and deoxygenated hemoglobin concentrations [73]. This capability enables more comprehensive analysis of the hemodynamic response, including the use of differential analysis techniques such as vector diagram analysis [73]. The separation of HbO and HbR measurements allows researchers to better define the initial dip in the hemodynamic response, potentially providing more precise temporal information about neural activation onset [73].

The physical principle underlying fNIRS involves transmitting near-infrared light (650-950 nm) onto the scalp, which penetrates biological tissues and is absorbed by chromophores, primarily hemoglobin [91]. The different absorption spectra of oxyhemoglobin and deoxyhemoglobin enable the calculation of concentration changes using the modified Beer-Lambert law [1]. Typical fNIRS systems utilize separate illumination sources and detectors with source-detector separations of 1.5-3 cm in children and 2.5-5 cm in adults, with penetration depth approximately half the source-detector separation [73]. This physical configuration allows fNIRS to specifically measure cortical activation while being relatively insensitive to deeper brain structures.

(Figure 1: Visual representation of key fNIRS technical advantages enabling naturalistic research)

Defining the Application Niche: When fNIRS Is the Optimal Choice

Naturalistic and Ecological Research Settings

fNIRS demonstrates particular strength in naturalistic research settings where ecological validity is paramount. Unlike fMRI, which requires participants to remain stationary in a confined scanner, fNIRS can be completely portable, enabling subjects to freely move during measurements [92]. This mobility allows researchers to measure brain activity in real-world settings outside the laboratory, including during movement, exercise, and social interactions [92] [8]. The capacity to study brain function in environments that closely mimic everyday situations addresses significant limitations of traditional neuroimaging approaches and opens new avenues for investigating cognitive processes as they naturally occur.

The ecological advantage of fNIRS is particularly evident in social cognitive neuroscience, where traditional neuroimaging methods struggle to capture the dynamic, interactive nature of social processes [91]. fNIRS enables the study of brain activity during real-time social interactions, providing insights that would be impossible to obtain in restrictive scanner environments. Furthermore, the quiet operation of fNIRS systems eliminates the auditory interference caused by fMRI gradient coils, making it suitable for studies involving auditory processing, language, and music perception [92]. This combination of mobility, tolerance to movement, and minimal environmental interference establishes fNIRS as the premier choice for research prioritizing ecological validity.

Special Populations and Clinical Applications

Table 2: Optimal fNIRS applications across different populations and settings

Population/Setting	Advantages of fNIRS	Research Applications
Infants & Children	High motion tolerance; safe for repeated use; does not require sedation [94]	Developmental cognitive neuroscience; language acquisition; social development [13]
Clinical Populations	Compatible with metallic implants; suitable for patients unable to tolerate fMRI [92] [73]	Neurorehabilitation monitoring; psychiatric disorders; epilepsy monitoring [91] [73]
Elderly Populations	Tolerability for cognitively impaired; portability for bedside assessment [91]	Neurodegenerative disease progression; cognitive decline assessment [91]
Natural Environments	Portable systems enable field research [8]	Classroom learning; sports performance; real-world decision making [8] [91]
Longitudinal Studies	Low cost per measurement; minimal participant burden [92]	Developmental trajectories; treatment response monitoring; learning studies [91]

fNIRS offers exceptional utility for research involving special populations that present challenges for traditional neuroimaging methods. Infant and developmental neuroscience has been particularly transformed by fNIRS, as it allows researchers to study awake, engaged infants without the constraints of fMRI [94]. The safety of fNIRS (non-invasive and non-ionizing) permits repeated measurements over extended periods, making it ideal for longitudinal studies of cognitive development [94]. Additionally, fNIRS has proven valuable for studying clinical populations such as individuals with cochlear implants, who cannot be scanned with fMRI due to metallic components and cannot be reliably tested with EEG due to signal interference [13].

In psychiatric and neurological research, fNIRS enables the investigation of cortical hemodynamics during actual motor tasks or walking, which is not feasible in the restrained environment of scanners [91]. This capability is particularly useful for mapping functional activation patterns during everyday life activities and exploring the effects of neurorehabilitation [91]. For example, fNIRS has been used to evaluate changes in cortical activation in stroke patients before and after rehabilitation, demonstrating its potential in detecting neuroplastic changes associated with recovery [91]. The combination of accessibility, tolerability, and portability makes fNIRS uniquely suited for bridging the gap between laboratory findings and real-world functioning in diverse populations.

Experimental Protocols for Naturalistic fNIRS Research

Protocol 1: Investigating Technology Impact on Executive Function

Background: This protocol is adapted from a recent study examining the immediate impact of social media consumption on executive functioning in college students using wearable fNIRS [9]. The approach demonstrates the application of fNIRS in naturalistic settings to investigate contemporary research questions with high ecological validity.

Research Question: How does brief social media exposure affect prefrontal cortex activation and behavioral performance on executive function tasks?

Participants: 20+ participants per group (social media group and control group); college-aged adults (18-25 years) [9].

Materials and Equipment:

• Wearable, wireless fNIRS system with minimum 16 channels
• Cap or headband with optode placement covering dlPFC, vlPFC, mPFC, and IFG
• Computer or tablet for cognitive tasks
• Personal smartphones for social media intervention
• Behavioral task software (E-Prime, PsychoPy, or equivalent)

Procedure:

• Baseline Assessment (Pre-intervention):
  • Obtain informed consent and demographic information
  • Apply fNIRS cap ensuring proper optode-scalp contact
  • Administer baseline executive function tasks (n-back for working memory, Go/No-Go for inhibition)
  • Record self-report measures of emotional state
  • Collect 5-minute resting-state fNIRS data

• Intervention Phase (10 minutes):

  • Social Media Group: Free browsing of preferred social media platforms (Instagram, Facebook, Twitter) via personal smartphones
  • Control Group: Quiet rest or reading neutral material

• Post-Intervention Assessment:

  • Repeat executive function tasks with parallel forms to minimize practice effects
  • Record self-report measures of emotional state
  • Continue fNIRS monitoring throughout post-assessment

• Data Processing:

  • Convert raw light intensity to HbO and HbR concentrations using modified Beer-Lambert law
  • Apply signal processing filters (bandpass 0.01-0.5 Hz) to remove physiological noise
  • Implement motion artifact correction algorithms
  • Perform baseline correction using pre-stimulus periods

Analytical Approach:

• Compare behavioral performance (accuracy, reaction time) pre- and post-intervention using repeated measures ANOVA
• Analyze fNIRS data using general linear model (GLM) with canonical hemodynamic response function
• Examine correlations between behavioral changes and hemodynamic responses
• Control for multiple comparisons using false discovery rate (FDR) correction

Expected Outcomes: The original study found reduced accuracy in executive function tasks accompanied by altered prefrontal activation patterns following social media use, including increased medial prefrontal cortex (mPFC) activation suggesting greater cognitive effort, and decreased dorsolateral (dlPFC) and ventrolateral prefrontal cortex (vlPFC) activation reflecting impairments in working memory and inhibition [9].

Protocol 2: Naturalistic Social Interaction Paradigm

Background: This protocol leverages fNIRS's portability to investigate brain activity during real-world social interactions, addressing a significant limitation of traditional neuroimaging methods.

Research Question: How does prefrontal cortex activation differ during face-to-face social interactions compared to screen-based communication?

Participants: 15+ dyads; adult participants without communication disorders.

Materials and Equipment:

• Dual fNIRS systems for simultaneous measurement of both interaction partners
• Optode arrays covering prefrontal regions associated with social cognition (mPFC, OFC, IFG)
• Audio-video recording equipment for behavioral coding
• Eye-tracking system (optional)
• Experimental room arranged for naturalistic interaction

Procedure:

• Setup and Calibration:
  • Apply fNIRS caps to both participants
  • Perform signal quality check for all channels
  • Synchronize fNIRS systems and external recording equipment

• Experimental Conditions:

  • Face-to-Face Conversation: Participants engage in unstructured conversation for 5 minutes
  • Video-Mediated Conversation: Participants communicate via video call from separate rooms
  • Resting Baseline: Quiet sitting without interaction for 3 minutes

• Data Collection:

  • Record continuous fNIRS data throughout all conditions
  • Synchronize with audio-video recordings for subsequent behavioral analysis
  • Collect self-report measures of social presence and engagement after each condition

• Data Processing:

  • Preprocess fNIRS data (filtering, motion correction, etc.)
  • Extract task-related hemodynamic responses for each condition
  • Calculate inter-brain synchrony measures for dyads using wavelet transform coherence

Analytical Approach:

• Compare HbO concentration changes across conditions using repeated measures ANOVA
• Examine correlations between self-reported engagement and neural activation
• Analyze temporal dynamics of inter-brain synchrony during social interaction
• Integrate behavioral coding with neural activation patterns

Research Reagent Solutions and Materials

Table 3: Essential materials and equipment for fNIRS research in naturalistic settings

Item Category	Specific Examples	Function/Purpose	Considerations for Naturalistic Research
fNIRS Systems	Wearable, wireless systems (e.g., Artinis, NIRx) [8]	Measures changes in HbO and HbR concentrations	Portability critical for real-world studies; battery life important for extended recordings
Optodes & Probes	Light sources (LEDs or lasers) and detectors [1]	Delivers light to scalp and detects reflected light	Flexible mounting systems improve comfort during movement; various inter-optode distances available for different penetration depths
Headgear	Flexible caps, headbands [8]	Holds optodes in stable position on head	Material should block ambient light; comfortable for extended wear; various sizes for different populations
Data Acquisition Software	Manufacturer-specific software [1]	Controls data collection parameters and storage	Should enable synchronization with other measures (behavioral tasks, physiological recordings)
Signal Processing Tools	HOMER3, NIRS Toolbox [1]	Processes raw fNIRS data to extract hemoglobin concentrations	Implementation of motion correction algorithms crucial for naturalistic studies
3D Digitalization Equipment	Patriot, Polhemus systems [92]	Records precise optode locations relative to head	Enables co-registration with anatomical images; important for accurate spatial localization
Short-Distance Detectors	Special optodes at 8mm separation [1]	Measures physiological signals from superficial tissues	Enables separation of cerebral and extracerebral signals; improves signal quality
Auxiliary Monitoring Equipment	EEG, EKG, respiration sensors [56]	Records confounding physiological signals	Helps identify and remove physiological noise from fNIRS signals

Methodological Considerations and Best Practices

Successful implementation of fNIRS research, particularly in naturalistic settings, requires careful attention to methodological details. Proper probe placement and secure attachment are essential for obtaining reliable signals, especially during participant movement [56]. Researchers should use the International 10-20 system for EEG electrode placement as a reference framework for optode positioning, ensuring consistency across participants and studies [94]. For naturalistic studies involving movement, additional securing methods such as elastic bandages or customized headgear may be necessary to maintain optode-scalp contact.

Signal quality control represents another critical consideration in fNIRS research. Researchers should implement systematic approaches for identifying and rejecting poor-quality channels, with common criteria including signal-to-noise ratio thresholds, coefficient of variation calculations, and detection of excessive motion artifacts [56]. The incorporation of short-distance channels (typically 8mm source-detector separation) enables better separation of cerebral hemodynamic signals from superficial physiological noise, significantly improving data quality [1]. Additionally, simultaneous recording of systemic physiological parameters (heart rate, blood pressure, respiration) provides valuable regressors for removing physiological confounding factors from fNIRS signals [56].

(Figure 2: Comprehensive fNIRS experimental workflow with critical decision points)

fNIRS occupies a distinctive and valuable position in the neuroimaging toolkit, particularly for research questions requiring ecological validity, participant mobility, or special population accessibility. While the technique does not replace fMRI for investigations requiring whole-brain coverage or exquisite spatial resolution, it offers compelling advantages for naturalistic research designs, longitudinal studies, and investigations involving populations that cannot tolerate fMRI constraints [92] [94]. The continuous technological advancements in wearable fNIRS systems, combined with improved analysis methods and growing methodological standardization, promise to further expand the applications and robustness of this imaging modality.

Researchers should consider fNIRS as the primary neuroimaging approach when their research questions involve: (1) real-world environments and tasks that cannot be replicated in scanner settings; (2) populations with limited compliance with fMRI requirements (infants, children, clinical groups); (3) studies requiring repeated or prolonged monitoring; and (4) research contexts with limited resources or infrastructure [92] [91] [94]. As the field continues to mature, fNIRS is poised to make increasingly significant contributions to our understanding of brain function in authentic contexts, ultimately bridging the gap between laboratory findings and real-world cognitive processes.

Conclusion

The integration of fNIRS into naturalistic design settings marks a significant leap forward for neuroscience and drug development. By moving beyond the artificial confines of the laboratory, fNIRS provides ecologically valid, individualized, and clinically relevant insights into brain function. This paradigm shift, powered by wearable technology and robust analytical methods, enables the dense sampling necessary for precision mental health and the development of novel biomarkers. Future directions will involve refining hardware for greater comfort and channel density, standardizing data acquisition and analysis across sites, and further exploring multimodal integration with EEG and other technologies. For researchers and pharmaceutical professionals, adopting fNIRS means gaining the ability to monitor therapeutic effects and cognitive outcomes in real-time, in real-world environments, ultimately accelerating the path to more effective and personalized interventions.

References

• 1. Functional near-infrared spectroscopy - Wikipedia [https://en.wikipedia.org/wiki/Functional_near-infrared_spectroscopy]
• 2. How Artinis fNIRS systems are ideal for monitoring the brain of... [https://www.artinis.com/blogpost-all/2023/using-functional-near-infrared-spectroscopy-fnirs-in-sensitive-subjects]
• 3. Frontiers | Applications and advances of combined fMRI- fNIRs ... [https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2025.1542075/full]
• 4. NIRS fNIRS Fesbe 2025 [https://brainlatam.com/blog/nirs-fnirs-fesbe-2025-6090]
• 5. Conducting Hyperscanning Experiments with Functional Near-Infrared... [https://www.jove.com/t/58807/conducting-hyperscanning-experiments-with-functional-near-infrared]
• 6. Functional near-infrared spectroscopy for speech protocols ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC6953699/]
• 7. Measuring brain activity using functional... | Spectroscopy Europe/World [https://www.spectroscopyeurope.com/article/measuring-brain-activity-using-functional-near-infrared-spectroscopy-short-review]
• 8. A Review on the Use of Wearable Functional Near-Infrared... [https://pmc.ncbi.nlm.nih.gov/articles/PMC6329605/]
• 9. assessment reveals decline in executive function... Naturalistic fNIRS [https://www.nature.com/articles/s41598-025-20844-7?error=cookies_not_supported&code=ebfcff64-bb43-4a7a-a359-e8c5ecfdc306]
• 10. Preprocessing functional near-infrared spectroscopy ( fNIRS ) data [https://mne.tools/stable/auto_tutorials/preprocessing/70_fnirs_processing.html]
• 11. Frontiers | Using the General Linear Model to Improve Performance in... [https://www.frontiersin.org/journals/human-neuroscience/articles/10.3389/fnhum.2020.00030/full]
• 12. Functional Near Infrared Spectroscopy ( fNIRS ) | Academic Block [https://www.academicblock.com/technology/image-based-measurements/functional-near-infrared-spectroscopy]
• 13. sciencedirect.com/topics/neuroscience/ functional - near - infrared ... [https://www.sciencedirect.com/topics/neuroscience/functional-near-infrared-spectroscopy]
• 14. Functional Magnetic Resonance Imaging and Functional Near-Infrared Spectroscopy: Insights from Combined Recording Studies - PMC [https://pmc.ncbi.nlm.nih.gov/articles/PMC5563305/]
• 15. Multimodal assessment of the spatial correspondence between fNIRS and fMRI hemodynamic responses in motor tasks | Scientific Reports [https://www.nature.com/articles/s41598-023-29123-9]
• 16. Wearable fNIRS platform for dense sampling and precision functional... [https://www.nature.com/articles/s41746-025-01690-3?error=cookies_not_supported&code=e4783590-e6fb-45e5-87b3-61df634bf1e3]
• 17. The Power of fNIRS in Research & Medicine [https://www.pluxbiosignals.com/blogs/informative/the-power-of-fnirs-in-research-medicine]
• 18. Motion artifacts in functional near-infrared spectroscopy: a comparison of motion correction techniques applied to real cognitive data - PubMed [https://pubmed.ncbi.nlm.nih.gov/23639260/]
• 19. Improved Motion Artifact Correction in fNIRS Data by Combining Wavelet and Correlation-Based Signal Improvement [https://www.mdpi.com/1424-8220/23/8/3979]
• 20. Prefrontal fNIRS -based clinical data analysis of brain functions in... [https://pmc.ncbi.nlm.nih.gov/articles/PMC8620253/]
• 21. About fNIRS - Cortivision [https://www.cortivision.com/about/]
• 22. Frontiers | The Functional near infrared spectroscopy applications in... [https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2025.1495138/abstract]
• 23. vs Comparing vs CW | NIRx FD TD fNIRS [https://nirx.net/cwfnirs]
• 24. How much do time-domain functional near-infrared spectroscopy... [https://pmc.ncbi.nlm.nih.gov/articles/PMC9587749/]
• 25. What we can and cannot (yet) do with functional near infrared spectroscopy - PMC [https://pmc.ncbi.nlm.nih.gov/articles/PMC4033094/]
• 26. Simultaneous Data Collection of fMRI and fNIRS ... - JoVE [https://www.jove.com/de/t/65088]
• 27. Frontiers | Current Status and Issues Regarding Pre-processing of... [https://www.frontiersin.org/journals/human-neuroscience/articles/10.3389/fnhum.2018.00505/full]
• 28. , high-density Wearable and diffuse optical tomography... fNIRS [https://pmc.ncbi.nlm.nih.gov/articles/PMC10190166/]
• 29. Wearable fNIRS platform for dense sampling and precision functional neuroimaging | npj Digital Medicine [https://www.nature.com/articles/s41746-025-01690-3]
• 30. Frontiers | Editorial: Advanced fNIRS in Neuroscience... Applications [https://www.frontiersin.org/journals/neurology/articles/10.3389/fneur.2025.1686834/abstract]
• 31. A low-cost, wearable, do-it-yourself functional near-infrared spectroscopy (DIY-fNIRS) headband - ScienceDirect [https://www.sciencedirect.com/science/article/pii/S246806722100033X]
• 32. Which experimental designs to use in fNIRS — Event-related design — Artinis Medical Systems | (f)NIRS devices [https://www.artinis.com/blogpost-all/which-experimental-designs-to-use-in-fnirs-event-related-design]
• 33. Data Processing in Functional Near-Infrared Spectroscopy (fNIRS) Motor Control Research - PMC [https://pmc.ncbi.nlm.nih.gov/articles/PMC8151801/]
• 34. fNIRS reproducibility varies with data quality, analysis pipelines, and researcher experience | Communications Biology [https://www.nature.com/articles/s42003-025-08412-1]
• 35. Statistical analysis of fNIRS data: a comprehensive review - PubMed [https://pubmed.ncbi.nlm.nih.gov/23774396/]
• 36. Naturalistic fNIRS assessment reveals decline in executive function... [https://pubmed.ncbi.nlm.nih.gov/41125672/]
• 37. Functional near infrared spectroscopy for brain functional connectivity analysis: A graph theoretic approach - ScienceDirect [https://www.sciencedirect.com/science/article/pii/S2405844023022090]
• 38. Frontiers | The impact of digital technology, social , and artificial... media [https://www.frontiersin.org/journals/cognition/articles/10.3389/fcogn.2023.1203077/full]
• 39. and Brain Computer Interface for Communication | PDF fNIRS [https://www.slideshare.net/slideshow/fnirs-and-brain-computer-interface-for-communication/76769925]
• 40. Frontiers | Validating the reproducibility of a low- cost single-channel... [https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2024.1351341/full]
• 41. fNIR Near Infrared Spectroscopy Prefrontal Cortex Monitoring | Research | BIOPAC [https://www.biopac.com/application/fnir-functional-near-infrared-optical-brain-imaging/]
• 42. Brain Activity: Review and Progress on EEG- Interactive ... Based [https://pubmed.ncbi.nlm.nih.gov/30349495/]
• 43. Conducting Hyperscanning Experiments with Functional Near-Infrared... [https://app.jove.com/t/58807/conducting-hyperscanning-experiments-with-functional-near-infrared]
• 44. Frontiers | The brain and social : from paradigms of interactions ... social [https://www.frontiersin.org/research-topics/69686/the-brain-and-social-interactions-from-paradigms-of-social-emotion-recognition-to-hyperscanning]
• 45. and Emotional “Route” in Decision-Making Cognitive : The... Process [https://pmc.ncbi.nlm.nih.gov/articles/PMC11274958/]
• 46. Attachment and emotional : examining the role of prefrontal... regulation [https://link.springer.com/article/10.1007/s10339-023-01144-2]
• 47. | NIRS fNIRS Techniques, Homer2, NIRS... | NIRx Analysis Analysis [https://nirx.net/fnirs-analysis]
• 48. A Mini-Review on Functional Near-Infrared Spectroscopy (fNIRS): Where Do We Stand, and Where Should We Go? [https://www.mdpi.com/2304-6732/6/3/87]
• 49. Frontiers | Motion removal and evaluation techniques for... artifacts [https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2022.878750/full]
• 50. Deep learning-based motion artifact removal in functional near-infrared spectroscopy - PMC [https://pmc.ncbi.nlm.nih.gov/articles/PMC9034734/]
• 51. An Automatic Motion-Based Artifact Reduction Algorithm for fNIRS in Concurrent Functional Magnetic Resonance Imaging Studies (AMARA–fMRI) [https://www.mdpi.com/1999-4893/16/5/230]
• 52. Deep learning-based motion removal in functional... artifact [https://pubmed.ncbi.nlm.nih.gov/35475257/]
• 53. Frontiers | Integration of Spatial Information Increases Reproducibility ... [https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2020.00746/full]
• 54. NINFA: Non-commercial interface for neuro-feedback acquisitions - PMC [https://pmc.ncbi.nlm.nih.gov/articles/PMC12077575/]
• 55. Which experimental designs to use in fNIRS — Resting-state design — Artinis Medical Systems | (f)NIRS devices [https://www.artinis.com/blogpost-all/which-experimental-designs-to-use-in-fnirs-resting-state-design]
• 56. Best practices for fNIRS publications - PMC [https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7793571/]
• 57. Effects of Processing Methods on fNIRS Signals Assessed During... [https://pmc.ncbi.nlm.nih.gov/articles/PMC7768789/]
• 58. The functional near infrared spectroscopy applications in children with... [https://pubmed.ncbi.nlm.nih.gov/40599736/]
• 59. How short is short ? Optimum source–detector distance for... [https://pmc.ncbi.nlm.nih.gov/articles/PMC4478880/]
• 60. : the new trend in Short — Artinis Medical... separation channels fNIRS [https://www.artinis.com/blogpost-all/2019/2/6/short-separation-channels-the-new-trend-in-fnirs]
• 61. : the new trend in Short - from the Artinis... separation channels fNIRS [https://nbtltd.com/short-separation-channels-the-new-trend-in-fnirs/]
• 62. Best practices for fNIRS publications - PMC [https://pmc.ncbi.nlm.nih.gov/articles/PMC7793571/]
• 63. Optodes’ Location Decider (fOLD): a toolbox for probe... fNIRS [https://www.nature.com/articles/s41598-018-21716-z?error=cookies_not_supported&code=0aaead60-d432-498a-9c41-b6271a22534b]
• 64. Strategic Integration : A Cross-Disciplinary Review of the fNIRS - EEG ... [https://pmc.ncbi.nlm.nih.gov/articles/PMC11506513/]
• 65. Concurrent fNIRS and EEG for Brain Function Investigation: A Systematic, Methodology-Focused Review - PMC [https://pmc.ncbi.nlm.nih.gov/articles/PMC9371171/]
• 66. Opportunities and Limitations of Mobile Neuroimaging Technologies in... [https://pmc.ncbi.nlm.nih.gov/articles/PMC9292610/]
• 67. Multimodal fNIRS - EEG measurements — Integration on the head... [https://www.artinis.com/blogpost-all/2023/multimodal-fnirs-eeg-measurements-integration-on-the-head]
• 68. - Fast, Accurate, Reliable Eye Tracking Hardware Integration [https://www.sr-research.com/hardware-integration/]
• 69. - Multimodal measurements: Ensuring high-quality fNIRS ... EEG EEG [https://www.artinis.com/blogpost-all/ensuring-high-quality-eeg-recordings-in-integrated-fnirs-eeg]
• 70. Inter-Brain Synchrony in Open-Ended Collaborative Learning: An... [https://app.jove.com/t/62777/inter-brain-synchrony-open-ended-collaborative-learning-an-fnirs]
• 71. Simultaneous multimodal fNIRS-EEG recordings reveal new insights in neural activity during motor execution, observation, and imagery | Scientific Reports [https://www.nature.com/articles/s41598-023-31609-5]
• 72. Applications and advances of combined fMRI - fNIRs techniques in brain... [https://pmc.ncbi.nlm.nih.gov/articles/PMC11958174/]
• 73. Frontiers | Functional Near-Infrared Spectroscopy and Its Clinical Application in the Field of Neuroscience: Advances and Future Directions [https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2020.00724/full]
• 74. vs EEG: fNIRS Non-Invasive Brain Comparing ... - iMotions Imaging [https://imotions.com/blog/learning/research-fundamentals/fnirs-vs-eeg/]
• 75. Neural bases of sustained attention during naturalistic parent-infant... [https://pubmed.ncbi.nlm.nih.gov/41173996/]
• 76. Non-cerebral physiological interference in functional Near-Infrared... [https://www.artinis.com/blogpost-all/non-cerebral-physiological-interference-in-fnirs]
• 77. - fNIRS | fMRI NIRS Concurrent Recording Measurements... | NIRx MRI [https://nirx.net/fnirs-fmri]
• 78. Frontiers | A Hitchhiker's Guide to Functional Magnetic Resonance ... [http://www.frontiersin.org/articles/10.3389/fnins.2016.00515/full]
• 79. - Concurrent measurement to fNIRS a method for... fMRI validate [https://pubmed.ncbi.nlm.nih.gov/26157983/]
• 80. Recent developments in optimal experimental designs for functional magnetic resonance imaging - PMC [https://pmc.ncbi.nlm.nih.gov/articles/PMC4109095/]
• 81. auditory stimuli with Naturalistic prefrontal fNIRS imaging... cortex [https://pubmed.ncbi.nlm.nih.gov/37271305/]
• 82. The Advantages of Combining fNIRS and EEG for Brain Monitoring... [https://www.artinis.com/blogpost-all/advantages-of-combining-fnirs-and-eeg]
• 83. Fusion of fNIRS and EEG : a step further in brain research | Bitbrain [https://www.bitbrain.com/blog/fusion-fnirs-eeg-brain-activity-research]
• 84. structure–function relationships in brain networks using... Comparing [https://www.nature.com/articles/s41598-024-79817-x?error=cookies_not_supported&code=5f174c51-a16c-4671-9060-5889c2533d69]
• 85. Naturalistic fNIRS assessment reveals decline in executive function and altered prefrontal activation following social media use in college students | Scientific Reports [https://www.nature.com/articles/s41598-025-20844-7]
• 86. + fNIRS | BrainProducts - Cortivision EEG [https://www.cortivision.com/integrations-fnirs-eeg/]
• 87. - Test of retest in resting-state cortical activity and... reliability fNIRS [https://pmc.ncbi.nlm.nih.gov/articles/PMC10549743/]
• 88. FRESH: fNIRS REproducibility Study Hub [https://openfnirs.org/data/fresh/]
• 89. - Test of Graph Metrics in Functional... | PLOS One Retest Reliability [https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0072425]
• 90. Intra-subject test-retest reliability for auditory-evoked functional... [https://pmc.ncbi.nlm.nih.gov/articles/PMC11924667/]
• 91. The present and future use of functional near‐infrared spectroscopy... [https://pmc.ncbi.nlm.nih.gov/articles/PMC6367070/]
• 92. of Comparison with fNIRS – other ... neuroimaging modalities fNIRS [https://www.artinis.com/blogpost-all/comparison-fnirs-vs-fmri]
• 93. of Comparison with fnir relation... other neuroimaging modalities [https://www.slideshare.net/slideshow/comparison-of-fnir-with-other-neuroimaging-modalities-relation-between-eeg-systems-and-optical-imaging-systemsrelation-between-brain-activity-and-fnir/90531350]
• 94. fNIRS in the developmental sciences - PMC [https://pmc.ncbi.nlm.nih.gov/articles/PMC4979552/]