Motion Artifact Correction in fNIRS vs EEG: A Comprehensive Guide for Biomedical Research

Abstract

This article provides a systematic comparison of motion artifact (MA) correction techniques for functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG), two pivotal non-invasive neuroimaging tools. Aimed at researchers, scientists, and drug development professionals, it explores the fundamental origins of MAs rooted in each technology's distinct physiological signal source—hemodynamic for fNIRS and electrical for EEG. The review methodically categorizes and evaluates hardware-based and algorithmic correction methods, including traditional signal processing and emerging deep-learning approaches. It further offers practical guidelines for troubleshooting and optimizing data quality in real-world experimental scenarios and discusses standardized validation metrics. By synthesizing the strengths and limitations of MA correction across modalities, this guide aims to empower the design of robust neuroimaging studies and enhance the reliability of data in clinical and research applications.

Understanding the Signal Source: Why fNIRS and EEG Are Susceptible to Different Motion Artifacts

Fundamental Physiological Principles and Signal Characteristics

This section details the core physiological signals measured by fNIRS and EEG, providing a foundation for understanding the motion artifacts that corrupt them.

Functional Near-Infrared Spectroscopy (fNIRS)

fNIRS is a non-invasive optical brain imaging technique that monitors hemodynamic changes in the cerebral cortex. It uses near-infrared light to measure concentration changes of oxyhemoglobin (HbO) and deoxyhemoglobin (HbR) in the blood, which are correlated with neural activity [1] [2] [3]. This hemodynamic response is a slow, metabolic process, typically lasting several seconds, and shares a physiological basis with the BOLD signal measured in fMRI [1].

Electroencephalography (EEG)

EEG measures the brain's spontaneous electrical activity from the scalp surface in a non-invasive fashion. It reflects the current flow from synchronized postsynaptic excitations of multiple pyramidal neurons in the cerebral cortex [4]. The amplitude of these signals is very small, typically ranging from 20 to 200 microvolts, and is categorized into different frequency bands (delta, theta, alpha, beta, gamma) associated with various brain states [4].

The diagram below illustrates the fundamental differences in the origin and nature of these two signals.

Figure 1: Core Signaling Pathways for fNIRS and EEG

Troubleshooting Guide: Motion Artifact FAQs

This section addresses the most common issues researchers face regarding motion artifacts in fNIRS and EEG experiments.

Q1: Why are motion artifacts particularly problematic in wearable fNIRS and EEG systems compared to traditional lab setups?

Motion artifacts are exacerbated in wearable systems due to a combination of factors: uncontrolled environments, subject mobility, and the use of dry or semi-dry electrodes that offer less stable contact with the scalp than traditional wet electrodes [5] [4]. Furthermore, wearable systems often have a reduced number of channels (typically below 16), which limits the effectiveness of spatial filtering techniques like Independent Component Analysis (ICA) that are standard in high-density lab systems [5].

Q2: What are the primary physical causes of motion artifacts in each modality?

• In fNIRS: Motion artifacts are primarily caused by a decoupling between the source/detector fiber and the scalp [1]. This can result from head movements, jaw movements (e.g., during talking or eating), eyebrow raising, or body movements that cause device displacement [1] [3]. This decoupling causes shifts in baseline light intensity and high-frequency spikes [1].
• In EEG: Artifacts arise from several mechanisms: electrode movement relative to the skin, causing potential changes; cable sway, leading to electromagnetic induction; muscle contractions from the head, neck, or jaw (EMG artifacts); and skin stretch altering the electrode-skin interface [6] [4] [7].

Q3: How can I quickly identify a motion artifact in my fNIRS or EEG data?

• fNIRS Artifact Identification: Motion artifacts in fNIRS often manifest as sudden, high-amplitude spikes or baseline shifts that deviate significantly from the smoother hemodynamic response. They can be categorized into types: Type A (fast spikes), Type B (slower peaks), Type C (gentle slopes), and Type D (very slow baseline shifts) [8].
• EEG Artifact Identification: In EEG, motion artifacts often appear as high-amplitude, low-frequency drifts or sharp, non-physiological transients that are time-locked to movement, such as steps during walking. They often exhibit a broadband spectral signature that can be distinguished from brain rhythms [9] [7].

Q4: When should I use trial rejection versus motion correction algorithms?

Trial rejection (removing contaminated data segments) is a safe and straightforward method. It is most suitable when the number of motion artifacts is low and the total number of trials is high [1]. However, in studies with vulnerable populations (e.g., infants, clinical patients) or challenging paradigms where the number of trials is limited, trial rejection may not be feasible. In these cases, motion correction algorithms are essential to retain enough data for a meaningful analysis [1] [8]. For pediatric fNIRS, it is almost always better to correct for motion artifacts than to reject trials [8].

Q5: Which motion correction technique should I start with for my specific application?

The choice of technique depends on your signal modality, data quality, and computational resources. The following table summarizes the performance of key algorithms as reported in the literature.

Table 1: Performance Comparison of Motion Artifact Correction Techniques

Technique	Modality	Reported Performance	Key Strengths	Primary Use Case
Wavelet-based (WPD-CCA) [10] [2]	EEG	Avg. ΔSNR: 30.76 dB, Avg. Artifact Reduction: 59.51% [10] [2]	Effective for non-stationary signals, strong noise reduction.	Single-channel EEG & fNIRS denoising.
iCanClean [9]	EEG	Improved ICA dipolarity; effective P300 ERP recovery during running.	Leverages noise references; excellent for locomotion studies.	Mobile EEG with motion (running, walking).
Artifact Subspace Reconstruction (ASR) [5] [9]	EEG	Reduced power at gait frequency; improved component dipolarity.	Fast, automated; good for multi-channel data.	Online preprocessing of high-density EEG.
Moving Average (MA) [8]	fNIRS (Pediatric)	Ranked among best for pediatric data in comparative study [8].	Simple, effective for certain artifact types.	Pediatric fNIRS with diverse artifacts.
Motion-Net (Deep Learning) [7]	EEG	Avg. Artifact Reduction: 86%, Avg. ΔSNR: 20 dB [7]	Subject-specific, high accuracy, uses visibility graph features.	High-accuracy removal for single-trial, mobile EEG.

Experimental Protocols and Methodologies

This section provides detailed methodologies for implementing some of the most effective motion correction techniques cited in this guide.

Protocol: Two-Stage Motion Correction using WPD-CCA

This protocol, validated for single-channel EEG and fNIRS, combines Wavelet Packet Decomposition (WPD) with Canonical Correlation Analysis (CCA) for robust artifact removal [10] [2].

• Signal Decomposition: Decompose the single-channel corrupted signal using Wavelet Packet Decomposition (WPD). This creates a complete binary tree of wavelet coefficients, providing a rich time-frequency representation.
• Component Reconstruction: Reconstruct components from the generated wavelet packet coefficients. This step transforms the signal into a multi-channel set of reconstructed components.
• Source Separation: Apply Canonical Correlation Analysis (CCA) to these reconstructed components. CCA identifies and separates underlying sources that are highly correlated with the motion artifact.
• Signal Reconstruction: Reconstruct the clean signal by excluding the artifact-related components identified by CCA.

The workflow for this advanced technique is outlined below.

Figure 2: WPD-CCA Motion Correction Workflow

Protocol: Preprocessing for Mobile EEG with iCanClean or ASR

This protocol is designed for multi-channel mobile EEG studies involving whole-body movement like walking or running [9].

• Data Input & Reference Creation: Start with raw, multi-channel EEG data. If physical noise sensors are unavailable, create pseudo-reference noise signals by applying a temporary notch filter (e.g., below 3 Hz) to the raw EEG to isolate low-frequency motion components.
• Artifact Removal Algorithm:
  • iCanClean Path: Use CCA to identify subspaces in the scalp EEG that are highly correlated with the (pseudo-)reference noise signals. Subtract these noise subspaces based on a user-defined R² correlation threshold (e.g., 0.65) [9].
  • ASR Path: Use a calibration period of clean data to compute the covariance matrix. A sliding-window PCA then identifies and removes components in the non-reference data that exceed a standard deviation threshold (parameter "k", often set between 10-30) [9].
• Data Validation: Perform ICA on the cleaned data. Evaluate the success of the preprocessing by assessing the dipolarity of the resulting independent components and the reduction of spectral power at the gait frequency and its harmonics [9].

The Scientist's Toolkit: Key Research Reagents and Materials

This table lists essential hardware and algorithmic "reagents" for conducting and correcting mobile neuroimaging studies.

Table 2: Essential Research Reagents and Materials

Item Name	Type	Function & Application	Key Consideration
Dry/Semi-Dry Electrodes	Hardware	Enables rapid setup for EEG without conductive gel; ideal for frequent use [5] [4].	Higher electrode-skin impedance, potentially more susceptible to motion artifacts [5].
Dual-Layer Electrodes	Hardware	Specialized EEG electrodes with a dedicated noise sensor layer mechanically coupled to the active electrode; provides a pure noise reference for algorithms like iCanClean [9].	Maximizes motion artifact removal efficacy but may not be available on all systems.
Accelerometer/Gyroscope (IMU)	Hardware	Provides independent measure of head movement; used as a reference signal for adaptive filtering (e.g., in fNIRS ABAMAR method) or to detect motion events [3].	Requires synchronization with neuroimaging data and integration into the processing pipeline.
Collodion-Fixed Fibers	Hardware	Secures fNIRS optodes to the scalp with a strong, glue-like substance; significantly reduces optode movement [1] [8].	Increases setup time and requires expertise for application and safe removal.
Wavelet Packet Families (dbN, fkN)	Algorithmic	A library of mathematical functions (e.g., Daubechies, Fejer-Korovkin) used to decompose signals for denoising; choice of wavelet impacts performance [10] [2].	db1 and fk8 wavelets reported as particularly effective for EEG and fNIRS, respectively [10] [2].
iCanClean Algorithm	Algorithmic	A software tool that uses CCA and reference noise signals to remove motion artifacts from EEG, improving ICA decomposition [9].	Most effective with dual-layer electrodes but can work with pseudo-references; excellent for locomotion studies.
Artifact Subspace Reconstruction (ASR)	Algorithmic	A real-time-capable EEG cleaning algorithm that uses PCA to remove high-variance components based on a clean calibration period [5] [9].	Performance is sensitive to the calibration data quality and the "k" threshold parameter [9].

FAQ: Understanding Motion Artifacts

What are the fundamental physical causes of motion artifacts in fNIRS and EEG?

Motion artifacts in both fNIRS and EEG arise from physical movements that disrupt the delicate sensor-scalp interface. However, the underlying physical principles differ due to the distinct signals each technology measures.

• In fNIRS, the primary issue is the disruption of optical coupling. Motion causes an imperfect contact between the optodes (light sources and detectors) and the scalp, leading to displacement, non-orthogonal contact, or oscillation of the optodes [3]. This decoupling results in significant, rapid changes in the intensity of the detected near-infrared light, which are recorded as large, non-physiological spikes or baseline shifts in the hemodynamic signal [1].

• In EEG, artifacts originate from multiple phenomena within the electrical measurement chain [11]:

  • Electrode-Skin Interface: Relative movement between the electrode and the skin alters the ion distribution at the contact point, generating a changing electrical potential that is added to the brain's signal [11].
  • Connecting Cables: Movement of the cables causes triboelectric effects—friction and deformation of the cable insulator generates an additive voltage potential [11].
  • Electrode-Amplifier System: Motion can cause sudden changes in electrode-skin impedance, which modulates any residual power line interference (PLI), spreading artifact energy across the EEG frequency spectrum [11].

Why are some types of head movement more problematic than others?

Research indicates that the type of movement influences the severity and correctability of the artifact. For instance, studies in simultaneous EEG-fMRI have shown that head shaking produces a more complex motion artifact that is harder to correct compared to head nodding [12]. This is likely due to non-rigid body movement of the skull and skin during a shake, which creates a larger discrepancy between the artifact measured on the scalp and any reference signal used for correction [12]. In fNIRS, movements like upward and downward motions or repeated rotations have been shown to particularly compromise signal quality, with susceptibility varying across different scalp regions [13].

How can I identify a motion artifact in my recorded data?

Motion artifacts typically manifest as high-amplitude, abrupt signal changes. The table below summarizes their key characteristics in each modality.

Table 1: Characteristics of Motion Artifacts in fNIRS and EEG

Feature	fNIRS Artifacts [3] [1]	EEG Artifacts [11]
Common Morphologies	High-amplitude spikes, baseline shifts, and low-frequency shifts.	Baseline shifts (low-freq.), spike-like transients (high-freq.), and modulated PLI.
Typical Causes	Head movement (nod, shake), jaw movement (talking, eating), facial muscle activity (eyebrow raising), and body movement.	Cable tugging, electrode shift/slip, muscle twitches in neck/head, and gait-related head movements.
Spectral Content	Can span a wide range, often overlapping with the hemodynamic response (<0.1 Hz).	Broadband, overlapping with the entire EEG spectrum (0.1-100 Hz).

Troubleshooting Guide: Mitigating and Correcting Artifacts

What are the most effective strategies for correcting motion artifacts?

Correction strategies can be broadly divided into hardware-based solutions and algorithmic (signal processing) approaches.

Table 2: Motion Artifact Correction Methods for fNIRS and EEG

Method Category	fNIRS Solutions	EEG Solutions
Hardware-Based	Use of accelerometers or Inertial Measurement Units (IMUs) to record motion for reference [3]. Fixed prism-based optical fibers and headposts [3].	Active electrodes and high-input-impedance amplifiers [14]. Small, lightweight passive electrodes (e.g., microelectrodes) [14]. Strain relief stickers to prevent cable pull.
Algorithmic (Software)	Wavelet Filtering: Identifies and removes artifacts in the wavelet domain [1] [15].Temporal Derivative Distribution Repair (TDDR): Assumes motion derivatives are large outliers and down-weights them [15].Spline Interpolation (e.g., MARA): Detects and replaces artifact segments with spline curves [15].Correlation-Based Signal Improvement (CBSI): Leverages the negative correlation between HbO and HbR [15].	iCanClean: A modern method that uses pseudo-reference noise signals and deep learning for effective denoising [16].Artifact Subspace Reconstruction (ASR): Identifies and removes high-variance components indicative of artifacts [16].Wavelet & CCA Hybrid (WPD-CCA): A two-stage method that first decomposes then cleans the signal [17].

What is the experimental evidence supporting these correction methods?

Recent comparative studies have evaluated the performance of various algorithms on real and simulated data.

• For fNIRS Functional Connectivity: A 2024 study evaluated six algorithms and found that Temporal Derivative Distribution Repair (TDDR) and Wavelet filtering were the most effective for brain network analysis, demonstrating superior denoising and the best ability to recover the original functional connectivity patterns [15].
• For EEG During Locomotion: A 2025 study compared artifact removal during overground running. Preprocessing with iCanClean and Artifact Subspace Reconstruction (ASR) led to the recovery of more brain-like independent components, reduced power at the gait frequency, and enabled the identification of the expected P300 event-related potential [16]. iCanClean was noted as somewhat more effective than ASR [16].
• For Single-Channel Denoising: A 2022 study introduced a two-stage method combining Wavelet Packet Decomposition and Canonical Correlation Analysis (WPD-CCA). This approach achieved an average motion artifact reduction of 59.51% for EEG and 41.40% for fNIRS, outperforming single-stage techniques [17].

Experimental Protocol: Characterizing Head Movement Artifacts with Computer Vision

Objective: To systematically associate specific head movements with motion artifacts in fNIRS signals using ground-truth movement data [13].

Methodology:

• Participant Setup: Participants are fitted with a whole-head fNIRS cap.
• Movement Tasks: Participants perform controlled, instructed head movements along the three main rotational axes (vertical, frontal, sagittal). Movements are categorized by speed (fast, slow) and type (e.g., half rotation, full rotation, repeated rotation).
• Synchronized Data Acquisition:
  • fNIRS Signals: Recorded continuously throughout the session.
  • Video Recording: The experimental session is recorded using a standard video camera.
• Data Processing & Analysis:
  • Computer Vision Analysis: Video recordings are analyzed frame-by-frame using a deep neural network (e.g., SynergyNet) to compute precise head orientation angles [13].
  • Movement Metric Extraction: Maximal movement amplitude and speed are extracted from the head orientation data.
  • Artifact Identification: Motion artifacts (spikes, baseline shifts) are identified in the synchronized fNIRS signals.
  • Correlation: The extracted head movement metrics are directly correlated with the occurrence and properties of the identified fNIRS artifacts to characterize their relationship [13].

The Scientist's Toolkit: Essential Research Reagents & Materials

Table 3: Key Resources for Motion Artifact Research

Item	Function / Application
Accelerometer / Inertial Measurement Unit (IMU)	Provides a hardware-based reference signal of head motion for adaptive filtering in both fNIRS and EEG [3].
MR-Compatible Optical Camera (e.g., Kineticor)	Tracks head and cap motion with high precision (e.g., ~10 μm) in controlled environments like MRI scanners, providing ground-truth motion data [12].
Deep Neural Network for Computer Vision (e.g., SynergyNet)	Enables automated, frame-by-frame computation of head orientation angles from video recordings for ground-truth movement analysis [13].
Reference Layer Cap (RLAS)	An EEG cap with paired, overlaid electrodes. One contacts the scalp, the other a reference layer, allowing direct measurement of the motion artifact for subtraction [12].
Wavelet Toolbox Software	Provides algorithms for wavelet-based motion artifact correction, a top-performing method for both fNIRS and EEG [1] [17] [15].
Conductive Hydrogel	Used to create a tight-fitting, conductive reference layer for EEG caps, improving artifact measurement and contact stability [12].
Microelectrodes	Small, lightweight passive electrodes that minimize the movement artifact by reducing the surface area and pressure on the gel layer [14].

Motion artifacts represent a significant challenge in non-invasive neuroimaging, particularly for functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG). These artifacts can severely corrupt signal quality, leading to misinterpretation of neural data and potentially invalidating research findings. Understanding their precise morphology—categorized primarily as spikes, baseline shifts, and slow drifts—is fundamental to developing effective correction strategies. This guide provides technical support for researchers characterizing and addressing these artifacts within the context of both fNIRS and EEG studies, with particular relevance for drug development professionals monitoring neurological effects in clinical trials.

The fundamental difference in measurement principles between fNIRS (hemodynamic response) and EEG (electrical potentials) means that while motion artifacts manifest similarly in both modalities, their underlying generation mechanisms and optimal correction approaches differ significantly. Effective artifact management requires not only robust processing algorithms but also appropriate experimental design and hardware selection.

Characterizing Motion Artifact Morphology

Motion artifacts in fNIRS and EEG signals can be classified into three primary morphological types based on their temporal characteristics and underlying causes. The table below summarizes the key features of each type.

Table 1: Morphological Types of Motion Artifacts in fNIRS and EEG

Artifact Type	Temporal Signature	Amplitude Characteristics	Common Causes in fNIRS	Common Causes in EEG
Spikes	Sudden, high-frequency transients [1] [8]	High amplitude; 50-100 standard deviations from mean [8]	Rapid decoupling of optodes from scalp [1]	Electrode "pop" from impedance change [18] [19]
Baseline Shifts	Sustained signal displacement (1-30s) [8]	Large deviation (e.g., 300 SD from mean) [8]	Head movement altering optode-scalp contact [13]	Cable movement, triboelectric effect [19] [11]
Slow Drifts	Very gradual signal change (>30s) [8]	Slow baseline wandering [8]	Slow temperature changes, poor fit [3]	Perspiration, drying electrolyte gel [18]

Regional Susceptibility and Task-Dependent Artifacts

The brain region being measured and the nature of the experimental task significantly influence the type and severity of motion artifacts encountered.

• fNIRS Regional Vulnerability: In fNIRS, the occipital and pre-occipital regions are particularly susceptible to motion artifacts following upward or downward head movements, whereas the temporal regions are most affected by lateral movements such as bending left, right, or sideways [13]. This highlights the importance of considering cap adherence and fit in different head regions.
• Task-Correlated Artifacts: Certain cognitive tasks can induce specific, problematic artifacts. For example, in fNIRS studies involving speech, the opening and closing of the mouth during vocal responses can cause a low-frequency, low-amplitude motion artifact that is temporally correlated with the evoked cerebral response, making it particularly difficult to distinguish from the true hemodynamic signal [1].

Experimental Protocols for Artifact Characterization

Protocol for fNIRS Motion Artifact Characterization

This protocol is designed to systematically characterize motion artifacts in fNIRS signals using ground-truth movement data.

Objective: To associate specific head movements with motion artifact morphologies in fNIRS signals. Equipment:

• Whole-head fNIRS system
• High-frame-rate video camera
• Computer with SynergyNet deep neural network software or equivalent for head pose estimation [13]

Procedure:

• Participant Preparation: Fit the fNIRS cap securely on the participant, ensuring optimal optode-scalp coupling.
• Controlled Movements: Instruct participants to perform controlled head movements along three rotational axes:
  • Sagittal Axis: Nodding (e.g., upward, downward)
  • Frontal Axis: Head tilting (e.g., leftward, rightward)
  • Vertical Axis: Head turning (e.g., left, right)
• Movement Variation: For each movement direction, vary the speed (fast vs. slow) and type (half, full, or repeated rotations) [13].
• Simultaneous Recording: Record the fNIRS signals and video footage of the head movements simultaneously throughout the experiment.
• Video Analysis: Analyze the video recordings frame-by-frame to compute head orientation angles using computer vision tools like SynergyNet [13].
• Data Extraction:
  • From head orientation data: Extract maximal movement amplitude and speed.
  • From fNIRS signals: Identify spikes and baseline shifts using algorithms that detect sudden, high-amplitude deviations [13].

Protocol for EEG Motion Artifact Characterization

This protocol focuses on identifying the physical sources of motion artifacts in EEG recordings, which is crucial for hardware and methodological improvements.

Objective: To model and identify the primary sources of motion artifacts in dynamic EEG recordings. Equipment:

• EEG system with passive electrodes
• Inertial Measurement Unit (IMU) or motion tracking system
• Customized cable management system

Procedure:

• System Setup: Set up the EEG system with standard passive electrodes.
• Task Performance: Have participants perform dynamic tasks such as overground walking, treadmill walking at various speeds (1-4 mph), or repetitive head rotations [20] [19].
• Controlled Testing: To isolate specific artifact sources:
  • Cable Movement: Have an experimenter manually shake the connecting cables while the participant is at rest [19] [11].
  • Electrode-Skin Interface: Analyze signals during movements that cause slow, periodic changes in the baseline voltage on individual channels [19] [11].
  • Power Line Interference (PLI) Modulation: Monitor signals for artifacts resulting from sudden variations in electrode-skin impedance during movement [19] [11].
• Data Correlation: Correlate the recorded motion data from the IMU with the identified artifacts in the EEG signals to establish causality.
• Electrical Modeling: Develop lumped parameter models to describe the observed phenomena at the skin-electrode interface, connecting cables, and electrode-amplifier system [19] [11].

The Scientist's Toolkit: Essential Research Reagents and Materials

Table 2: Key Research Reagents and Materials for Motion Artifact Research

Item Name	Function/Application	Modality
Computer Vision Software (e.g., SynergyNet)	Provides frame-by-frame head orientation data for ground-truth movement analysis [13]	fNIRS
Inertial Measurement Units (IMUs)	Tracks head acceleration and movement for correlation with signal artifacts [20] [3]	fNIRS & EEG
Accelerometers	Used in Active Noise Cancellation (ANC) and ABAMAR methods for motion artifact removal [3]	fNIRS
Short-Separation Detectors	Helps isolate and subtract superficial, non-cerebral hemodynamic fluctuations [1] [3]	fNIRS
Collodion-Fixed Optical Fibers	Enhances optode-scalp coupling stability to reduce motion-induced decoupling [8] [3]	fNIRS
Active Electrodes	Reduces power line interference but may be comparable to passive electrodes for motion artifacts [19] [11]	EEG
Textile-Based Electrodes	Shows reduced sensitivity to motion artifacts but limited to hairless regions [19] [11]	EEG
Homer2/3 Software Package	Standard fNIRS processing package containing various motion correction algorithms [8]	fNIRS

Technical Support & FAQs

Frequently Asked Questions

Q1: What are the most effective software-based techniques for correcting motion artifacts in fNIRS signals?

Multiple comparative studies have identified several effective techniques. Wavelet-based filtering is consistently ranked among the top performers, particularly for handling spike-type artifacts [1] [8]. The moving average (MA) method has also been shown to yield excellent outcomes, especially in pediatric data [8]. For comprehensive correction, a hybrid approach combining spline interpolation and wavelet filtering is highly recommended. Spline interpolation effectively models and subtracts baseline shifts, while wavelet filtering targets spikes, resulting in a more robust correction across different artifact morphologies [21]. Studies using this hybrid method have reported channel improvement rates as high as 94.1% [21].

Q2: Why are traditional artifact removal techniques like ICA sometimes ineffective for motion artifacts in EEG?

Motion artifacts in EEG are often non-stationary and non-repetitive, meaning their shape and timing are highly variable and not time-locked to the movement in a predictable way [19] [11]. Furthermore, these artifacts can have spectral components that overlap completely with the typical EEG bandwidth (0.1–100 Hz), making it impossible to filter them out without also removing neural signals of interest [19] [11]. Unlike more stereotypical artifacts (e.g., eye blinks), the irregular nature of many motion artifacts makes it difficult for blind source separation techniques like ICA to isolate them reliably.

Q3: How can I objectively detect motion artifacts in my fNIRS data before applying correction algorithms?

A novel and effective method is Kurtosis-based Wavelet Detection (kbWD). This algorithm uses Continuous Wavelet Transform (CWT) to decompose the signal and then analyzes the kurtosis (the "tailedness") of the wavelet coefficient distribution [21]. Artifact segments tend to produce outlier coefficients that result in a high kurtosis value. The key advantage of kbWD is that it relies on a single threshold parameter (kurtosis) that demonstrates wide adaptability across different signal-to-noise ratios, making it more robust than methods requiring multiple user-defined thresholds [21].

Q4: What are the primary hardware-related sources of motion artifacts in EEG, and how can they be mitigated?

The main sources are:

• The Electrode-Skin Interface: Relative movement between the electrode and skin alters ion distribution, causing slow baseline shifts [19] [11]. Mitigation: Ensure secure electrode fit and use high-quality conductive gel.
• Connecting Cables: Friction and deformation of cable insulators generate triboelectric noise, causing high-amplitude, non-repeatable spikes [19] [11]. Mitigation: Secure cables to the participant's clothing or cap to minimize movement.
• Electrode-Amplifier System: Unstable electrode-skin contact can modulate power line interference (PLI), introducing unpredictable artifacts across the EEG spectrum [19] [11]. Mitigation: Maintain low and balanced electrode impedances.

Further advancements should focus on the transduction stage, including improved electrode technology and better interfacing with the acquisition system [19] [11].

Workflow and Signaling Pathways

The following diagram illustrates the logical workflow for characterizing and correcting motion artifacts, integrating both fNIRS and EEG modalities.

Diagram 1: Motion Artifact Characterization and Correction Workflow

Frequently Asked Questions (FAQs)

Q1: Which neuroimaging modality is more susceptible to motion artifacts, fNIRS or EEG? EEG is significantly more susceptible to motion artifacts. It measures electrical potentials on the scalp, and even minor head movements or cable sway can cause significant signal contamination, appearing as high-amplitude spikes or shifts [22] [23]. fNIRS, which measures hemodynamic responses using light, is more robust and tolerant to subject movement, making it more suitable for studies involving walking, children, or real-world settings [22] [3].

Q2: What are the typical signatures of motion artifacts in fNIRS and EEG signals? The characteristics differ between the two modalities [1] [24]:

• fNIRS Artifacts: Can manifest as high-frequency spikes, slow baseline shifts (BS), or low-frequency oscillations. These are caused by a decoupling between the optical sensors (optodes) and the scalp [1].
• EEG Artifacts: Often appear as high-amplitude, high-frequency spikes that are time-locked to movements like steps during walking. These artifacts can dominate the signal and reduce the quality of independent component analysis (ICA) [23].

Q3: Is it better to reject data segments with motion artifacts or to correct them? For both fNIRS and EEG, the scientific consensus is that it is almost always better to correct for motion artifacts than to reject entire trials [1] [23]. Trial rejection is only feasible when the number of artifacts is low and the total number of trials is high. In many real-world studies, particularly with clinical populations or children, the number of trials is limited, and rejecting contaminated segments would lead to an unacceptable loss of data [1].

Q4: Can fNIRS and EEG be used together to overcome their individual limitations with motion? Yes, a multimodal EEG-fNIRS approach is increasingly used to leverage the strengths of each technique [22] [25] [26]. While this introduces integration challenges, such as ensuring sensor compatibility and synchronizing hardware, it provides a more comprehensive view of brain activity by combining EEG's millisecond-scale temporal resolution with fNIRS's better spatial resolution and motion tolerance [22] [25]. This hybrid approach can be particularly powerful for applications like brain-computer interfaces (BCIs) and neurofeedback [25] [27].

Troubleshooting Guides

Issue: Motion Artifacts in fNIRS Signals

Problem: Your fNIRS data shows sudden, large spikes or slow, sustained baseline drifts, making it difficult to isolate the true hemodynamic response.

Solution: Implement a robust motion correction pipeline. Evidence suggests that a hybrid approach, which categorizes and treats different types of artifacts, is highly effective [24].

Recommended Protocol: A Hybrid fNIRS Motion Correction Approach This protocol is adapted from a method proven to enhance signal quality during long-term monitoring, such as sleep studies [24].

• Artifact Detection: Use an fNIRS-based detection strategy. Calculate the moving standard deviation of the signal. Data points that exceed a predetermined threshold are flagged as potential motion artifacts [24].
• Artifact Categorization: Classify the detected artifacts into three types:
  • Severe Oscillation: High-amplitude, high-frequency spikes.
  • Baseline Shift (BS): A slow, sustained drift in the signal baseline.
  • Slight Oscillation: Lower-amplitude, higher-frequency noise.
• Targeted Correction: Apply specific algorithms to each category:
  • Severe Oscillation & BS Correction: Use cubic spline interpolation. The spline is fitted to the non-artifactual data segments around the artifact and then subtracted from the original signal to correct it [24].
  • Slight Oscillation Correction: Apply a dual-threshold wavelet-based (WB) method to reduce high-frequency noise without distorting the underlying hemodynamic signal [24].
• Final Filtering: Use a high-pass filter to remove any remaining slow drifts.

The following workflow outlines this hybrid correction process:

Issue: Motion Artifacts Contaminating EEG During Locomotion

Problem: During walking or running, the EEG signal is overwhelmed by motion artifacts that are time-locked to the gait cycle, obscuring brain activity and degrading subsequent independent component analysis (ICA).

Solution: Preprocess the EEG data with advanced algorithms designed to handle motion artifacts before performing ICA. Recent research comparing methods during running tasks recommends the following [23]:

Recommended Protocol: Pre-ICA Motion Correction for Mobile EEG

• Algorithm Selection: Choose one of the following two high-performing methods:
  • iCanClean (Recommended): This method uses canonical correlation analysis (CCA) to identify and subtract noise subspaces from the EEG signal. It can operate using dedicated noise sensors or by creating "pseudo-reference" noise signals from the EEG data itself (e.g., by applying a notch filter below 3 Hz). An R² threshold of 0.65 is a good starting point [23].
  • Artifact Subspace Reconstruction (ASR): This method uses a sliding-window principal component analysis (PCA) to identify and remove high-variance components that deviate from a clean "calibration" period of data. A higher "k" parameter (e.g., 20-30) is less aggressive, while a lower value (e.g., 10) cleans more aggressively but risks removing neural signal [23].
• Application: Apply the chosen algorithm to the continuous EEG data. Studies show both methods significantly reduce power at the gait frequency and its harmonics and improve the dipolarity of independent components derived from ICA, with iCanClean having a slight edge in performance [23].
• Validation: After correction, proceed with your standard ICA and analysis. Validate the success by checking for a reduction in power at the step frequency and the recovery of expected event-related potentials (ERPs), such as the P300 [23].

The logical relationship for selecting a correction strategy is summarized below:

Comparative Technical Data

Table 1: Fundamental Characteristics of fNIRS and EEG Regarding Motion Tolerance

Feature	EEG (Electroencephalography)	fNIRS (Functional Near-Infrared Spectroscopy)
What It Measures	Electrical activity from cortical neurons [22]	Hemodynamic response (blood oxygenation) [22]
Primary Motion Artifact Source	Electrode-scalp displacement, cable sway [23]	Optode-scalp decoupling [1]
Typical Artifact Signature	High-amplitude, high-frequency spikes [23]	Spikes, baseline shifts, low-frequency oscillations [1] [24]
Inherent Motion Tolerance	Low – highly susceptible to movement artifacts [22]	Moderate to High – more robust to subject movement [22]
Best Suited Experimental Environment	Highly controlled lab settings with minimal movement [22]	Naturalistic, mobile, and real-world contexts [22]

Table 2: Performance Comparison of Motion Artifact Correction Algorithms

Modality	Correction Method	Key Principle	Reported Efficacy	Important Considerations
fNIRS	Wavelet Filtering [1]	Multi-resolution analysis to isolate and remove artifacts	93% reduction in artifact area in cognitive tasks [1]	Highly effective for spike-like and low-frequency artifacts [1]
fNIRS	Hybrid (Spline + Wavelet) [24]	Combines spline interpolation for BS & severe artifacts with wavelet for slight oscillations	Improves SNR and correlation in long-term data [24]	Addresses multiple artifact types; more complex pipeline [24]
fNIRS	WPD-CCA [28]	Two-stage: Wavelet Packet Decomposition + Canonical Correlation Analysis	ΔSNR: 16.55 dB; η: 41.40% for fNIRS [28]	Designed for single-channel analysis; outperforms many single-stage methods [28]
EEG	iCanClean [23]	Canonical Correlation Analysis with noise references	Recovers more dipolar ICs; reveals expected P300 effects during running [23]	Can use dedicated noise sensors or pseudo-references from EEG [23]
EEG	Artifact Subspace Reconstruction (ASR) [23]	Sliding-window PCA to remove high-variance components	Reduces power at gait frequency; improves ICA dipolarity [23]	Performance depends on calibration data and "k" parameter setting [23]
EEG	WPD-CCA [28]	Two-stage: Wavelet Packet Decomposition + Canonical Correlation Analysis	ΔSNR: 30.76 dB; η: 59.51% for EEG [28]	Effective for single-channel EEG correction [28]

The Scientist's Toolkit: Research Reagent Solutions

Table 3: Essential Materials and Algorithms for Motion Artifact Research

Item / Solution	Function / Description	Relevance to Motion Correction
Accelerometer / IMU	A small sensor that measures motion and acceleration.	Often used as a hardware-based solution to provide a reference noise signal for adaptive filtering in both fNIRS and EEG studies [3].
Cubic Spline Interpolation	A mathematical method for constructing a smooth curve that passes through a set of points.	A core technique in fNIRS correction for modeling and subtracting baseline shifts and severe motion artifacts [24].
Wavelet Transform	A signal processing technique that decomposes a signal into different frequency components.	The foundation of many powerful algorithms (e.g., Wavelet, WPD) for both fNIRS and EEG to isolate and remove motion-related spikes and oscillations [1] [28] [24].
Canonical Correlation Analysis (CCA)	A statistical method for finding relationships between two sets of multidimensional variables.	Used in advanced methods like WPD-CCA and iCanClean to identify and remove subspaces of the signal that are highly correlated with motion noise [23] [28].
Dual-Layer EEG Electrodes	Specialized EEG electrodes that include a separate sensor layer that is mechanically coupled but not in contact with the scalp.	Provides a "pure" noise reference for motion artifact correction algorithms like iCanClean, significantly improving their efficacy [23].
Structured Sparse Multiset CCA (ssmCCA)	A advanced data fusion technique.	Used in multimodal EEG-fNIRS studies to fuse data from both modalities and identify brain activity consistently detected by both, enhancing result validity [26].

Motion artifacts represent one of the most significant challenges in non-invasive neuroimaging, particularly for functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG) research. These unwanted signals caused by participant movement can completely obscure genuine neural activity, leading to misinterpreted data and compromised research outcomes [8]. The portability that makes fNIRS and EEG valuable for real-world experiments also renders them vulnerable to motion artifacts, creating a critical tension in mobile brain imaging research. This technical support guide examines how artifacts impact data integrity across these modalities and provides evidence-based troubleshooting strategies to protect research validity.

Understanding Motion Artifacts and Their Impact

What are motion artifacts and why do they threaten data integrity?

Motion artifacts are unwanted signals introduced into neuroimaging data by physical movement. In fNIRS, these artifacts occur when motion causes relative displacement between optical fibers and the scalp, leading to rapid shifts in optical coupling and baseline measurements [29]. In EEG, motion artifacts arise from multiple sources including muscle twitches, electrode displacement, and head movements during walking, which cause baseline shifts and periodic oscillations [7].

The fundamental threat to data integrity stems from the magnitude of motion artifacts, which is typically far greater than the subtle changes associated with genuine neural activity. Motion artifacts can completely obscure the hemodynamic responses in fNIRS or mask neural electrical activity in EEG, making it difficult or impossible to detect the actual brain signals of interest [8] [29]. This is particularly problematic in pediatric populations, clinical patients, and naturalistic study designs where movement is unavoidable.

How do motion artifacts differ between fNIRS and EEG?

While both modalities are susceptible to motion artifacts, the nature and impact of these artifacts differ significantly:

fNIRS Artifacts:

• Type A: Sudden spikes with standard deviation >50 from mean within 1 second
• Type B: Peaks with standard deviation >100 from mean lasting 1-5 seconds
• Type C: Gentle slopes with standard deviation >300 from mean over 5-30 seconds
• Type D: Slow baseline shifts >30 seconds with standard deviation >500 from mean [8]

EEG Artifacts:

• Muscle twitches: Brief contractions causing sharp transients mimicking epileptic spikes
• Head movements: Vertical displacements during walking causing baseline shifts
• Electrode displacement: Sudden movements during gait causing amplitude bursts [7]

Table 1: Motion Artifact Classification in fNIRS and EEG

Category	fNIRS Artifact Types	EEG Artifact Types	Primary Impact
Short Duration	Type A (Spikes)	Muscle Twitches	Mimics event-related responses
Medium Duration	Type B (Peaks)	Electrode Displacement	Obscures rhythmic brain activity
Long Duration	Types C & D (Slopes/Shifts)	Head Movement	Masks baseline signals
Prevention Challenge	Optode-scalp coupling	Electrode-scalp interface	Both require stable physical contact

Quantitative Comparison of Motion Correction Techniques

What is the evidence for motion correction efficacy?

Research directly comparing motion correction techniques provides crucial guidance for method selection. A systematic comparison of fNIRS motion correction techniques found that all four major approaches significantly reduced mean-squared error and increased contrast-to-noise ratio compared to no correction or trial rejection [29].

Table 2: Performance Comparison of fNIRS Motion Correction Techniques

Correction Method	MSE Reduction	CNR Improvement	Best Use Case
Spline Interpolation	55% (Highest)	Moderate	Preserving HRF shape accuracy
Wavelet Analysis	Moderate	39% (Highest)	General purpose applications
Principal Component Analysis	Significant	Significant	Multi-channel datasets
Moving Average	Significant	Significant	Pediatric populations [8]
Kalman Filtering	Significant	Significant	Real-time applications

For EEG signals, novel approaches combining wavelet packet decomposition with canonical correlation analysis (WPD-CCA) have demonstrated impressive performance, achieving motion artifact reduction of 59.51% for EEG and 41.40% for fNIRS signals [17] [28]. The difference in SNR (ΔSNR) improved by 30.76 dB for EEG and 16.55 dB for fNIRS using these techniques [28].

Troubleshooting Guide: Frequently Asked Questions

How can I identify motion artifacts in my data?

Problem: Researchers struggle to distinguish motion artifacts from genuine neural signals.

Solution: Implement systematic artifact detection:

For fNIRS:

• Use automated algorithms like hmrMotionArtifactbyChannel in HOMER2 with parameters set for your artifact type (e.g., tMotion=1.0, tMask=1.0, STDthresh=50.0, AMPthresh=5.0) [8]
• Visually inspect for characteristic patterns: sudden spikes, rapid baseline shifts, or high-frequency noise
• Compare across channels - motion artifacts often appear simultaneously in multiple channels

For EEG:

• Look for high-frequency bursts time-locked to movement
• Identify periodic oscillations corresponding to gait cycles
• Detect slow drifts indicating electrode displacement [7]

Which motion correction method should I choose for my research?

Problem: Overwhelming method selection leads to suboptimal correction.

Solution: Match correction technique to research context:

For pediatric fNIRS studies:

• Recommended: Moving Average and Wavelet methods demonstrated best outcomes with child participants [8]
• Avoid: Excessive trial rejection due to typically short attention spans and limited data collection windows

For EEG in mobile applications:

• Recommended: WPD-CCA for single-channel applications [28]
• Emerging option: Motion-Net deep learning algorithm for subject-specific correction (achieving 86% artifact reduction) [7]

For real-time processing requirements:

• Recommended: Kalman filtering or recursive least-square methods [8]
• Alternative: Moving average with appropriate window sizing

How can I prevent motion artifacts during experimental design?

Problem: Post-hoc correction cannot fully recover data quality compromised by excessive motion.

Solution: Implement preventive strategies:

• Physical stabilization: Custom-made caps with additional wrapping bands improve optode stability [8]
• Participant training: Practice sessions to familiarize participants with tasks while minimizing movement
• Task design: Incorporate adequate rest periods and minimize unnecessary movements during critical measurements
• Hardware selection: Consider systems with accelerometers for motion tracking when possible [7]

Research Reagent Solutions for Motion Artifact Management

Table 3: Essential Tools for Motion Artifact Research

Resource/Tool	Function	Application Context
HOMER2 Software Package	Comprehensive fNIRS processing including motion correction algorithms	fNIRS data analysis, particularly with block designs [8]
Wavelet Toolbox	Signal decomposition for artifact separation	Both EEG and fNIRS signal processing [17]
Accelerometer Data	Reference signal for motion artifact regression	Mobile EEG studies, real-world movement paradigms [7]
Structured Sparse Multiset CCA	Multimodal data fusion for improved artifact identification	Simultaneous EEG-fNIRS studies [26]
Custom Head Caps	Improved optode/electrode stability during movement	Pediatric populations, clinical patients [8]
Deep Learning Frameworks	Subject-specific artifact removal (e.g., Motion-Net, U-Net)	Large datasets, individual subject analysis [7] [30]

Advanced Methodologies: Experimental Protocols

Protocol: Implementing Wavelet-Based Motion Correction

For researchers implementing wavelet-based correction methods based on published successful approaches [17] [28]:

• Signal Preparation

  • Convert raw fNIRS to optical density changes
  • Resample EEG signals to appropriate frequency (typically 250-500Hz)
  • Apply baseline correction

• Wavelet Packet Decomposition

  • Select appropriate wavelet family (Db1 for EEG, Db2 for fNIRS recommended)
  • Perform multi-level decomposition (typically 4-8 levels)
  • Identify artifact-contaminated components using thresholding

• Canonical Correlation Analysis (for hybrid method)

  • Apply CCA to isolate artifact components
  • Remove components with artifact characteristics
  • Reconstruct signal using inverse transformation

• Validation

  • Calculate ΔSNR and artifact reduction percentage (η)
  • Compare with pre-correction signal quality
  • Verify preservation of physiological signals

Protocol: Multimodal Data Fusion for Enhanced Artifact Detection

For studies employing simultaneous EEG-fNIRS recording [26]:

• Synchronized Data Collection

  • Ensure precise time synchronization between EEG and fNIRS systems
  • Record trigger signals simultaneously on both systems
  • Monitor signal quality in real-time during acquisition

• Structured Sparse Multiset Canonical Correlation Analysis (ssmCCA)

  • Extract common components across modalities
  • Identify motion artifacts evident in both electrical and hemodynamic domains
  • Preserve neural activity unique to each modality

• Cross-validation

  • Verify artifact removal by comparing results across modalities
  • Ensure neural signals of interest remain intact after correction
  • Validate against known physiological principles

Emerging Solutions and Future Directions

The field of motion artifact correction is rapidly evolving, with machine learning approaches showing significant promise. Deep learning methods like Denoising Auto-Encoders (DAE) and U-Net architectures can reconstruct clean hemodynamic responses even from heavily contaminated signals [30]. These approaches are particularly valuable for real-world applications where traditional methods struggle with complex, non-stationary artifacts.

As research continues, the integration of multiple approaches—combining hardware improvements, sophisticated signal processing, and artificial intelligence—holds the greatest potential for effectively managing motion artifacts while preserving the neural signals that underpin research validity in cognitive neuroscience and clinical applications.

A Practical Toolkit: Motion Artifact Correction Algorithms for fNIRS and EEG Data

Troubleshooting Guides

Guide 1: Addressing Over-Correction and Signal Loss in PCA

Problem: When using Principal Component Analysis (PCA) for motion artifact removal, the brain signal is often partially removed along with the artifact, leading to signal loss and inaccurate physiological interpretation [31].

Solution: Implement Targeted PCA (tPCA) tPCA applies PCA only to pre-identified segments of data containing motion artifacts, rather than the entire dataset. This confines the correction to noisy periods and preserves clean signal segments [31].

Steps:

• Motion Artifact Detection: First, identify intervals containing motion artifacts. This can be done using accelerometer data [7], or by analyzing the signal for sudden, large-amplitude shifts [31].
• Apply PCA Locally: Perform PCA only on the data segments (across channels) identified in step 1.
• Remove Artifact Components: Identify and remove the principal components that represent the motion artifact. This is typically based on the components explaining the largest variances [31].
• Reconstruct Signal: Reconstruct the artifact-corrected segments and integrate them back into the full dataset.

Considerations:

• Parameter Sensitivity: The performance of tPCA depends on several user-defined parameters, primarily the thresholds used for motion detection and the number of components to remove [31].
• Multi-channel Requirement: Both PCA and tPCA require data from multiple sensors to calculate cross-correlations [28].

Guide 2: Managing Spline Interpolation Overfitting and Oscillation

Problem: High-degree spline interpolation can cause excessive oscillation or "ringing" near sharp artifacts, distorting the underlying physiological signal [32].

Solution: Use Cubic Splines and Focus on Artifact Segments Cubic splines (3rd degree) are generally optimal, providing a smooth fit without the high-frequency oscillations common with higher-order splines [32]. Techniques like the Movement Artifact Reduction Algorithm (MARA) use this principle effectively [31].

Steps:

• Identify Artifact Segments: Detect the specific time intervals contaminated by motion artifacts.
• Fit Cubic Spline: Fit a cubic spline exclusively to these artifact segments. The spline is designed to trace the low-frequency motion artifact [31].
• Subtract Artifact: Subtract the fitted spline from the original contaminated signal segment to recover the clean underlying signal [31].

Considerations:

• Advantages: This method is particularly effective for correcting large, slow-moving baseline shifts and spikes [31].
• Disadvantages: Its performance is highly dependent on the accurate detection of motion artifact intervals, which can be a complex process [31].

Guide 3: Ineffective Motion Artifact Removal with Moving Average

Problem: Simple Moving Average (MA) filters are ineffective at removing motion artifacts because the artifact's frequency content often overlaps with the frequency band of the desired brain signal [33].

Solution: Combine with Correlation-Based Methods or Use Alternative Filters The Moving Average filter alone is not a robust solution for motion artifacts in fNIRS or EEG. For sporadic noise, a moving average filter can be sufficient [34], but for motion artifacts, consider these alternatives:

• Use Savitzky-Golay (S-G) Filter: The S-G filter is a generalized moving average that performs local polynomial regression to preserve high-frequency components better than a simple moving average [31]. It can be effective for high-frequency motion artifacts but is less effective for slow drifts and baseline shifts [31].
• Implement Correlation-Based Signal Improvement (CBSI): This method is used in fNIRS and operates on the principle that motion-free hemodynamic responses (HbO and HbR) are negatively correlated, while motion artifacts are positively correlated. It can effectively remove large spikes and baseline shifts and has the advantage of being fully automated [31].

Frequently Asked Questions (FAQs)

FAQ 1: Can I use PCA or ICA for single-channel EEG/fNIRS data? No. Standard Principal Component Analysis (PCA) and Independent Component Analysis (ICA) require at least two or more input channels to separate components based on covariance or statistical independence [28]. For single-channel data, you must use techniques that operate on a single signal, such as:

• Wavelet Packet Decomposition (WPD) [28]
• Empirical Mode Decomposition (EMD) [28]
• Singular Spectrum Analysis (SSA) [28]
• Variational Mode Decomposition (VMD) [28]

FAQ 2: What is the most effective single-technique for motion artifact correction? There is no single "best" technique, as performance depends on the artifact type and signal modality. However, hybrid methods that combine multiple techniques consistently outperform single-method approaches. For instance:

• A two-stage method combining Wavelet Packet Decomposition and Canonical Correlation Analysis (WPD-CCA) showed a significant performance increase over single-stage WPD for both EEG and fNIRS signals [28].
• A combined Wavelet and CBSI (WCBSI) approach was shown to have superior and consistent performance across various metrics compared to several established single-method corrections in fNIRS [31].

FAQ 3: How do I choose between the many artifact correction algorithms? Base your choice on the nature of your data and the algorithm's strengths:

• For data with many channels: PCA-based methods (standard or targeted) can be effective [31].
• For data with clear, discrete artifact periods: Spline-based correction (like MARA) is a strong candidate [31].
• For fully automated processing without artifact detection: Wavelet filtering or CBSI (for fNIRS) are good options [31].
• For the most robust correction: Prefer combined methods like WPD-CCA [28] or WCBSI [31]. Always validate the chosen method's performance on a subset of your data with known artifacts.

Quantitative Performance Comparison of Techniques

The following tables summarize the performance of various motion artifact correction techniques as reported in recent studies. This data can help you select an appropriate method for your research.

Table 1: Performance of Deep Learning and Decomposition Techniques on EEG Signals

Technique	Signal Modality	Key Metric	Reported Performance	Key Advantage
Motion-Net (CNN) [7]	EEG	Artifact Reduction (η)	86% ± 4.13	Subject-specific training; uses Visibility Graph features.
		SNR Improvement	20 ± 4.47 dB
WPD-CCA (2-stage) [28]	EEG	Artifact Reduction (η)	59.51%	Designed for single-channel signals.
		ΔSNR	30.76 dB
WPD (1-stage) [28]	EEG	Artifact Reduction (η)	53.48%	Simpler, single-stage approach.
		ΔSNR	29.44 dB

Table 2: Performance of Various Algorithms on fNIRS Signals

Technique	Key Metric	Reported Performance	Key Characteristics
WCBSI (Wavelet + CBSI) [31]	Ranking	Best overall performance	Combines advantages of wavelet and correlation methods.
WPD-CCA (2-stage) [28]	Artifact Reduction (η)	41.40%	Effective for single-channel fNIRS.
	ΔSNR	16.55 dB
CBSI [31]	General Performance	Effective for spikes & baseline shifts	Fully automated; assumes negative HbO-HbR correlation.
Spline Interpolation (MARA) [31]	General Performance	Effective for baseline shifts	Performance depends on accurate artifact detection.
Wavelet Filter [31]	General Performance	Effective for spikes and drift	Fully automated; no need for artifact detection.
tPCA [31]	General Performance	Reduces over-correction vs. PCA	Complex; many user parameters.
PCA [31]	General Performance	Can over-correct the signal	Requires multiple channels.

Experimental Protocols for Key Techniques

Protocol 1: Implementing the WPD-CCA Method for Single-Channel Signals

This two-stage protocol is designed for robust artifact removal from single-channel EEG or fNIRS data [28].

• Signal Decomposition: Decompose the contaminated single-channel signal into multiple frequency sub-bands using Wavelet Packet Decomposition (WPD). Select an appropriate wavelet packet (e.g., from Daubechies or Fejer-Korovkin families) and decomposition level.
• Reconstruction for CCA: Reconstruct the signal from each of the sub-band coefficients. This creates a multivariate set of signals from a single channel.
• Apply Canonical Correlation Analysis (CCA): Perform CCA on this multivariate set. CCA will identify and separate underlying components that are highly correlated with the motion artifact.
• Remove Artifact Component: Identify and remove the component(s) corresponding to the motion artifact.
• Signal Reconstruction: Reconstruct the cleaned signal from the remaining components.

Protocol 2: Applying Combined WCBSI for fNIRS Data

This protocol combines the strengths of wavelet filtering and correlation-based logic for fNIRS signals [31].

• Wavelet Decomposition: Decompose the measured fNIRS signals (both HbO and HbR) using a wavelet transform.
• Thresholding: Identify and zero out the wavelet coefficients that are likely to represent motion artifacts based on their magnitude.
• Initial Signal Reconstruction: Reconstruct preliminary artifact-reduced HbO and HbR signals from the thresholded coefficients.
• Apply CBSI Logic: Use the Correlation-Based Signal Improvement algorithm on the wavelet-filtered signals. The CBSI algorithm uses the negative correlation principle between HbO and HbR to further refine the signal and estimate the final artifact-corrected hemodynamic responses.

Workflow and Signaling Pathways

The Scientist's Toolkit: Essential Research Reagents & Materials

Table 3: Key Materials and Computational Tools for Motion Artifact Research

Item Name	Function / Application	Relevance in Research
Accelerometer [7] [33]	Hardware-based motion tracking.	Provides a reference signal for motion onset and intensity, used to validate artifact detection or as an input for adaptive filters [34].
Visibility Graph (VG) Features [7]	A signal feature extraction method.	Converts time-series signals into graph structures, providing features that enhance deep learning model accuracy, especially with smaller datasets [7].
Canonical Correlation Analysis (CCA) [28]	A statistical multivariate method.	Used to separate artifact components from brain signals, particularly in hybrid methods like WPD-CCA for single-channel analysis [28].
Wavelet Packet Decomposition (WPD) [28]	A signal decomposition technique.	Provides a more detailed frequency breakdown than standard wavelet transform, forming the first stage of powerful hybrid correction algorithms [28].
HOMER3 Software Toolkit [31]	An open-source fNIRS processing software.	Provides a standardized platform with integrated functions (PCA, tPCA, CBSI, Wavelet, Spline) for developing and comparing artifact correction methods [31].
U-Net Convolutional Neural Network [7]	A deep learning architecture for signal reconstruction.	The backbone of the Motion-Net model, enabling subject-specific motion artifact removal from EEG signals with high accuracy [7].

Motion artifacts represent a significant challenge in non-invasive neuroimaging, particularly for electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS). These artifacts arise from participants' movements—such as head motion, jaw clenching, or eyebrow raising—which create imperfect contact between sensors and the scalp, leading to signal corruption that can mask genuine brain activity and produce false positives or negatives in research data [3]. The portable nature of both EEG and fNIRS technology makes them particularly vulnerable to these artifacts, as they're often used in naturalistic settings where movement restriction is impractical. While both modalities suffer from motion artifacts, the fundamental nature of the corruption differs: EEG records electrical signals distorted by motion-induced changes in electrode-scalp contact, whereas fNIRS optical signals are affected by changes in light transmission path and scalp blood volume [17] [3]. Wavelet-based denoising has emerged as a particularly powerful approach for addressing these artifacts in both modalities due to its ability to handle non-stationary signals and localize transient artifacts in time-frequency space.

Understanding Wavelet-Based Denoising

Core Principles of Wavelet Analysis

Wavelet transform operates on fundamentally different principles compared to traditional Fourier analysis. While Fourier transform decomposes signals into frequency components that exist throughout the entire signal duration, wavelet analysis uses localized basis functions (wavelets) that can be shifted and scaled to capture both frequency content and temporal localization [35]. This multi-resolution analysis capability makes wavelet transforms exceptionally suited for physiological signals like EEG and fNIRS, which contain non-stationary characteristics and transient artifacts that need to be identified and removed without distorting the underlying neural signals of interest.

The two primary implementations of wavelet analysis are Discrete Wavelet Transform (DWT) and Continuous Wavelet Transform (CWT). DWT is computationally efficient and decomposes signals into approximation and detail coefficients through a series of high-pass and low-pass filters, followed by downsampling [35]. In contrast, CWT provides a more redundant representation that can be advantageous for detailed time-frequency analysis but requires greater computational resources. A more advanced variant, Wavelet Packet Decomposition (WPD), further generalizes DWT by decomposing both the approximation and detail coefficients at each level, creating a complete binary tree structure that offers finer frequency resolution in the higher frequency bands [17] [28].

Why Wavelets Excel for EEG and fNIRS Denoising

The mathematical properties of wavelet transforms align exceptionally well with the characteristics of both EEG and fNIRS signals:

• Non-stationarity handling: Brain signals are inherently non-stationary, with statistical properties that change over time, making traditional frequency-domain filtering suboptimal [35].

• Multi-scale analysis: Wavelets naturally separate signals into different frequency bands, aligning well with the conventional frequency bands used in EEG analysis (delta, theta, alpha, beta, gamma) and the characteristic frequencies of hemodynamic responses in fNIRS [35].

• Transient detection: The localized nature of wavelets enables precise identification and characterization of motion artifacts, which typically manifest as short-duration, high-amplitude transients in both EEG and fNIRS recordings [17] [3].

• Adaptability: Different wavelet families (Daubechies, Symlets, Coiflets, etc.) can be selected based on their similarity to the signal characteristics of interest, providing flexibility in optimizing the denoising approach for specific applications [17] [28].

Quantitative Performance Comparison

Performance Metrics for Artifact Removal

Researchers typically use several standardized metrics to evaluate the effectiveness of artifact removal techniques:

• ΔSNR (Change in Signal-to-Noise Ratio): Measures the improvement in signal quality after processing, calculated as the difference between output and input SNR in decibels (dB) [17] [28].

• η (Percentage Reduction in Motion Artifacts): Quantifies the percentage of artifact power removed from the contaminated signal [17] [28].

• Residual Variance: Assesses the amount of signal variance remaining after regression-based methods, particularly for fNIRS with short-channel regression [36].

• Similarity Metrics: Correlation coefficients (e.g., Pearson's r) between processed signals and ground truth measurements, when available [36].

Comparative Performance of Wavelet Methods

Table 1: Performance Comparison of Wavelet-Based Methods for EEG Motion Artifact Removal

Method	Wavelet Type	Average ΔSNR (dB)	Average Artifact Reduction (η%)	Computational Complexity
WPD (Single-stage)	db2	29.44	53.48%	Medium
WPD (Single-stage)	db1	28.45	54.12%	Medium
WPD-CCA (Two-stage)	db1	30.76	59.51%	High
EWT-PCA	N/A	28.26	Not Reported	Medium-High
EMD-CCA	N/A	27.82	55.30%	High

Table 2: Performance Comparison of Wavelet-Based Methods for fNIRS Motion Artifact Removal

Method	Wavelet Type	Average ΔSNR (dB)	Average Artifact Reduction (η%)	Compatible Signal Types
WPD (Single-stage)	fk4	16.11	26.40%	Single-channel
WPD-CCA (Two-stage)	db1	16.55	41.40%	Single-channel
WPD-CCA (Two-stage)	fk8	15.89	42.15%	Single-channel
Wavelet-MA	N/A	Not Reported	Superior in pediatric data	Multi-channel
WCBSI	N/A	Not Reported	Consistently favorable	Multi-channel

Experimental Protocols and Methodologies

Standardized Experimental Setup

For reproducible results in wavelet-based denoising studies, researchers typically follow standardized protocols:

Data Acquisition Specifications:

• EEG systems: Minimum 14-channel setup with 10-20 international electrode placement, sampling rate ≥256 Hz [35]
• fNIRS systems: Continuous-wave systems with dual wavelengths (690nm and 830nm), source-detector distance of 3cm for long channels and 0.8-1.5cm for short channels, sampling rate ≥10Hz [36] [3]
• Motion artifact induction: Controlled movements including head nodding, jaw clenching, eyebrow raising, and walking in place [3]
• Ground truth recording: Simultaneous accelerometer data or expert-annotated artifact segments for validation [3]

Benchmark Datasets:

• Physionet EEG dataset: Publicly available containing both clean and artifact-contaminated segments [37]
• fNIRS motor task datasets: Finger-tapping experiments with known hemodynamic response patterns [36]
• Synthetic datasets: Realistically simulated fNIRS-EEG data with known ground truth for method validation [38]

Wavelet Packet Decomposition with CCA Protocol

The two-stage WPD-CCA method has demonstrated superior performance for both EEG and fNIRS denoising [17] [28]:

Stage 1: Signal Decomposition

• Select appropriate wavelet family (db1 for EEG, fk8 for fNIRS) based on signal characteristics
• Perform 5-level wavelet packet decomposition using selected wavelet
• Obtain 2^5 = 64 wavelet packet nodes containing different frequency components
• Reconstruct signals from each node to create a multi-channel dataset from single-channel input

Stage 2: Artifact Removal via CCA

• Apply Canonical Correlation Analysis to the reconstructed multi-channel dataset
• Identify components with highest correlation to motion artifacts
• Remove artifact-correlated components
• Reconstruct clean signal from remaining components

Parameter Optimization:

• Decomposition level: 5-8 levels typically optimal for physiological signals
• Wavelet selection: Daubechies (db1-db3) for EEG, Fejer-Korovkin (fk4-fk8) for fNIRS
• Thresholding: Adaptive threshold based on noise estimation at each node

Empirical Wavelet Transform with Variance Protocol

For EEG denoising, the EWT-based approach has shown promising results [37]:

• Signal Decomposition:

  • Apply Empirical Wavelet Transform to decompose EEG signals into Intrinsic Mode Functions (IMFs)
  • Adaptively determine frequency boundaries based on signal spectrum

• Artifact Suppression:

  • Approach 1 (PCA-based): Apply Principal Component Analysis to IMFs to suppress noise components
  • Approach 2 (Variance-based): Calculate variance of each IMF, identify and remove IMFs with variance exceeding threshold
  • Reconstruct signal from remaining IMFs

• Performance Validation:

  • Compare ΔSNR against traditional methods
  • Validate preservation of neural signals in task-based paradigms

Troubleshooting Guide: Common Implementation Challenges

Wavelet Selection and Parameter Optimization

Problem: Poor artifact removal performance with specific wavelet types

• Solution: Implement systematic wavelet family evaluation. For EEG: Begin with Daubechies (db1-db3). For fNIRS: Start with Fejer-Korovkin (fk4-fk8). Use quantitative metrics (ΔSNR, η) not visual inspection alone for objective comparison [17] [28].

Problem: Over-smoothing or excessive signal distortion

• Solution: Adjust decomposition level based on signal characteristics. For EEG sampled at 256Hz, 6-8 levels typically optimal. For fNIRS at 10Hz, 4-5 levels sufficient. Reduce threshold multipliers conservatively (start with 0.5-1.0 times noise estimate) [17].

Problem: Inconsistent performance across subjects or sessions

• Solution: Implement subject-specific parameter optimization. Extract noise statistics from clean signal segments or use robust statistical measures (median absolute deviation) instead of global thresholds [3].

Computational and Real-Time Implementation Issues

Problem: High computational load preventing real-time application

• Solution: Optimize implementation by limiting decomposition levels, using simpler wavelet families (Haar, db1), or implementing downsampling for fNIRS applications where high temporal resolution less critical [3].

Problem: Memory issues with long-duration recordings

• Solution: Implement block processing approach with 30-60 second segments with 10-20% overlap to maintain continuity while reducing memory footprint [17].

Problem: Integration with existing preprocessing pipelines

• Solution: Develop modular implementation compatible with popular neuroimaging platforms (Homer2, EEGLAB). Ensure input/output formatting matches expected pipeline requirements [8] [3].

Frequently Asked Questions (FAQ)

Q1: Which wavelet family performs best for EEG versus fNIRS signals? Research indicates that Daubechies wavelets (particularly db1 and db2) achieve optimal performance for EEG signals, while Fejer-Korovkin wavelets (fk4, fk8) demonstrate superior results for fNIRS data [17] [28]. This difference stems from the distinct signal characteristics: EEG contains more transient neural oscillations better matched by Daubechies wavelets, while fNIRS hemodynamic responses are smoother and better captured by Fejer-Korovkin filters.

Q2: When should I choose single-stage versus two-stage wavelet approaches? Single-stage WPD works well for mild to moderate motion artifacts and offers computational efficiency for real-time applications. The two-stage WPD-CCA approach demonstrates significantly better performance (11-56% improvement in artifact reduction) for severe motion artifacts and is recommended when signal fidelity is paramount, despite higher computational demands [17] [28].

Q3: How does wavelet performance compare to other popular artifact removal methods? Wavelet-based methods consistently outperform or match alternative approaches across both modalities. For EEG, WPD-CCA surpasses EMD-based and PCA-based methods in ΔSNR. For fNIRS, wavelet methods demonstrate particular advantage in pediatric populations where motion artifacts are more pronounced compared to traditional approaches like spline interpolation or moving average [8] [17].

Q4: Can wavelet methods distinguish motion artifacts from physiological artifacts? Advanced wavelet approaches can partially differentiate artifact types through their characteristic time-frequency signatures. Motion artifacts typically manifest as high-amplitude, short-duration transients across multiple frequency bands, while physiological artifacts (cardiac, respiratory) show more periodic patterns. However, for severe contamination, combined approaches like WPD-CCA or hybrid wavelet-accelerometer methods may be necessary [35] [3].

Q5: What are the limitations of wavelet-based denoising approaches? Primary limitations include the sensitivity to parameter selection (wavelet type, decomposition level, thresholding method), potential for over-smoothing of neural signals with aggressive thresholding, computational demands for high-density arrays, and challenge in completely removing artifacts that spectrally overlap with neural signals of interest [17] [3].

Essential Research Reagents and Computational Tools

Table 3: Essential Research Tools for Wavelet-Based Denoising Implementation

Tool/Category	Specific Examples	Function/Purpose	Compatibility
Programming Environments	MATLAB, Python, Julia	Algorithm implementation and signal processing	EEG/fNIRS
Wavelet Toolboxes	MATLAB Wavelet Toolbox, PyWavelets, EEGLAB	Pre-built wavelet functions and utilities	EEG/fNIRS
Neuroimaging Suites	Homer2 (fNIRS), EEGLAB, BrainVision Analyzer	Integration with existing preprocessing pipelines	EEG/fNIRS
Data Acquisition Systems	ActiChamp (EEG), TechEN CW6 (fNIRS), NIRx	Hardware interface and signal recording	EEG/fNIRS
Validation Tools	Accelerometers, 3D motion capture	Ground truth motion tracking	EEG/fNIRS
Benchmark Datasets	Physionet EEG, fNIRS motor tasks	Method validation and comparison	EEG/fNIRS

Wavelet-based denoising represents a powerful, flexible approach for addressing the critical challenge of motion artifacts in both EEG and fNIRS research. The method's strong performance stems from its mathematical compatibility with the non-stationary, multi-scale characteristics of neurophysiological signals. Current evidence demonstrates that advanced implementations like WPD-CCA can achieve impressive artifact reduction (59.51% for EEG, 41.40% for fNIRS) while preserving neural signal integrity [17] [28].

Future developments in wavelet denoising will likely focus on several key areas: (1) increased integration with machine learning approaches for automated parameter selection and artifact classification; (2) development of standardized, modality-specific protocols to improve reproducibility across laboratories; (3) optimization for real-time implementation in brain-computer interface and neurofeedback applications; and (4) hybrid approaches that combine wavelet methods with auxiliary hardware (accelerometers, IMUs) for improved artifact characterization [36] [3].

For researchers implementing these methods, we recommend beginning with single-stage WPD using modality-appropriate wavelets (db1 for EEG, fk4 for fNIRS) for mild artifacts, progressing to two-stage WPD-CCA for more challenging cases. Systematic validation using both quantitative metrics (ΔSNR, η) and functional outcomes (task activation, classification accuracy) remains essential for ensuring method efficacy in specific research contexts.

This technical support center provides guidance on implementing advanced multi-stage signal processing techniques for motion artifact correction. Within the broader thesis context of comparing fNIRS and EEG research, this resource focuses specifically on the novel combination of Wavelet Packet Decomposition (WPD) and Canonical Correlation Analysis (CCA), a two-stage approach that demonstrates enhanced performance over single-stage methods for both EEG and fNIRS signals [28] [17]. The following guides and FAQs address specific implementation challenges researchers may encounter during their experiments.

Frequently Asked Questions (FAQs)

Q1: What is the fundamental performance advantage of using a two-stage WPD-CCA approach over single-stage WPD?

The two-stage WPD-CCA approach consistently outperforms single-stage WPD across both EEG and fNIRS modalities. The key advantage lies in its ability to more effectively separate motion artifacts from neural signals through sequential processing. Research demonstrates that WPD-CCA provides significant improvements in both signal-to-noise ratio enhancement and percentage reduction of motion artifacts [28].

Table: Performance Comparison of WPD vs. WPD-CCA

Signal Modality	Method	Best Average ΔSNR (dB)	Best Average η (%)	Optimal Wavelet
EEG	WPD	29.44	53.48	db2 (ΔSNR), db1 (η)
EEG	WPD-CCA	30.76	59.51	db1
fNIRS	WPD	16.11	26.40	fk4
fNIRS	WPD-CCA	16.55	41.40	db1 (ΔSNR), fk8 (η)

Q2: Which wavelet packet families are most effective for motion artifact correction in fNIRS versus EEG?

Research indicates that different wavelet packet families yield optimal results for EEG versus fNIRS signals, and the choice also depends on whether using single-stage or multi-stage approaches [28]:

• For EEG signals: Daubechies wavelets (particularly db1 and db2) consistently deliver strong performance for both WPD and WPD-CCA methods.
• For fNIRS signals: Fejer-Korovkin wavelets (particularly fk4 and fk8) show excellent performance, especially in single-stage WPD applications.

Q3: Is it methodologically acceptable to use different motion correction parameters across subjects in a study?

No, this practice introduces significant methodological concerns. Experts strongly recommend maintaining consistent processing streams and parameters across all subjects to avoid introducing subjective bias into your results [39]. While some researchers report adjusting parameters like amplitude thresholds based on visual inspection of individual subject data, this practice risks producing artificially optimized results rather than objectively comparable data [39].

Q4: In what order should motion correction and stimulus rejection be applied in the processing pipeline?

Motion correction should be applied before stimulus rejection [39]. The rationale is that effective motion correction can preserve trials that would otherwise be rejected due to artifacts, thereby increasing the usable data for analysis and improving statistical power.

Troubleshooting Guides

Issue 1: Suboptimal Artifact Removal with WPD-CCA

Problem: The WPD-CCA method is not providing the expected level of motion artifact reduction reported in literature (e.g., ~59.51% for EEG, ~41.40% for fNIRS) [28].

Solutions:

• Verify wavelet selection: Systematically test different wavelet packets (db1, db2, db3, fk4, fk6, fk8) to identify the optimal choice for your specific signal characteristics [28].
• Check decomposition level: Ensure appropriate decomposition depth based on your signal's sampling frequency and the frequency characteristics of expected artifacts.
• Validate CCA implementation: Confirm that the CCA stage is properly identifying and removing components with high correlation to motion artifacts rather than neural signals.

Issue 2: Signal Distortion or Over-Correction

Problem: After applying WPD-CCA, the processed signal shows distortion of neural components or elimination of expected physiological patterns.

Solutions:

• Review component rejection criteria: Adjust thresholds for discarding artifact components to preserve more neural information.
• Implement validation checks: Compare processed signals with known task-related responses (e.g., event-related potentials for EEG, hemodynamic responses for fNIRS) to ensure neural signals are preserved.
• Consider hybrid approaches: For particularly challenging cases, investigate machine learning alternatives such as MLMRS-Net, which has shown promise in preserving signal integrity while removing artifacts [40].

Issue 3: Inconsistent Performance Across Subjects

Problem: WPD-CCA performs well on some subjects but poorly on others, creating dataset inconsistencies.

Solutions:

• Standardize parameters: Maintain consistent processing parameters across all subjects despite visual differences in artifact contamination [39].
• Pre-process acceleration data: If using accelerometer-based motion reference, ensure proper calibration and synchronization across recording sessions.
• Implement quality metrics: Compute pre- and post-processing signal quality indices (e.g., ΔSNR, η) for each subject to objectively identify outliers requiring special attention.

Experimental Protocols & Methodologies

Standardized WPD-CCA Implementation Protocol

Purpose: To provide a standardized methodology for implementing the WPD-CCA motion artifact correction technique for single-channel EEG or fNIRS signals, enabling reproducible results and valid cross-study comparisons.

Materials and Equipment:

• Motion-contaminated EEG or fNIRS recordings
• Signal processing software with wavelet and CCA capabilities (MATLAB, Python, etc.)
• Computational resources for signal decomposition and analysis

Table: Research Reagent Solutions

Reagent/Resource	Function/Application
Daubechies Wavelet Packets (db1, db2, db3)	Signal decomposition for EEG and fNIRS
Fejér-Korovkin Wavelet Packets (fk4, fk6, fk8)	Signal decomposition, particularly effective for fNIRS
CCA Algorithm	Identification and separation of artifact components
Benchmark Dataset [28]	Method validation and performance comparison
ΔSNR and η Metrics [28]	Quantitative performance evaluation

Step-by-Step Procedure:

• Signal Preprocessing: Bandpass filter raw signals according to modality-specific requirements (EEG: 0.5-45 Hz; fNIRS: 0.01-0.5 Hz).
• Wavelet Packet Selection: Choose appropriate wavelet packet family based on signal modality (Daubechies for EEG, Fejer-Korovkin for fNIRS).
• Wavelet Packet Decomposition: Decompose the signal using WPD to multiple levels (typically 4-8 levels depending on sampling frequency).
• Component Reconstruction: Reconstruct components from wavelet coefficients for each node.
• CCA Application: Apply CCA to identify components with high correlation to motion artifacts.
• Artifact Component Removal: Remove identified artifact components while preserving neural signal components.
• Signal Reconstruction: Reconstruct the cleaned signal from the remaining components.
• Validation: Compute performance metrics (ΔSNR and η) to quantify artifact reduction efficacy.

Validation Metrics:

• ΔSNR: Difference in signal-to-noise ratio before and after processing [28]
• η: Percentage reduction in motion artifacts [28]

Performance Validation Protocol

Purpose: To quantitatively validate the efficacy of WPD-CCA implementation against established benchmarks and alternative methods.

Comparative Framework:

• Benchmarking: Compare your implementation's performance against published results [28]:
  • Target: ≥59.51% average artifact reduction for EEG
  • Target: ≥41.40% average artifact reduction for fNIRS
• Method Comparison: Evaluate performance against alternative methods:
  • Single-stage WPD
  • Other multiresolution approaches (VMD, EMD, EEMD)
  • Deep learning models (MLMRS-Net) [40]
• Statistical Analysis: Perform appropriate statistical tests to confirm significant improvements in signal quality metrics.

The WPD-CCA method represents a significant advancement in motion artifact correction for both EEG and fNIRS signals, offering substantially improved performance over single-stage approaches. By following the standardized protocols and troubleshooting guides provided in this technical support resource, researchers can effectively implement this technique in their experiments, leading to more reliable and interpretable results in neuroimaging research.

FAQs: Troubleshooting Learning-Based Artifact Correction

Q1: Our deep learning model for motion artifact removal is overfitting to the training data. What strategies can we use to improve generalization?

A1: Overfitting is a common challenge, particularly with limited datasets. Based on recent research, you can employ several strategies:

• Incorporate Novel Feature Encoding: Enhance your model's learning capability with smaller datasets by integrating features from a Visibility Graph (VG), which provides structural information about the signal and improves model stability [7].
• Leverage Data Augmentation: During training, use an input data augmentation procedure to artificially expand your dataset and improve the model's robustness [41].
• Use Architectural Regularization: Implement a penalty network alongside your main CNN. This parallel network acts as a sophisticated regularization mechanism, assigning weights to the CNN's outputs to mitigate overfitting and enhance training stability [41].
• Apply Standard Techniques: Ensure you are using standard practices like dropout and L2 regularization, and employ early stopping based on a validation set to halt training before overfitting occurs [42].

Q2: For a real-time BCI application, which type of learning-based model should we prioritize for motion artifact correction?

A2: For real-time applications, processing speed and low latency are critical.

• 1D Convolutional Neural Networks (1D CNNs) are an excellent choice. A study on real-time fNIRS processing demonstrated that a 1D CNN with a penalty network achieved an average processing time of 0.53 ms per sample, which is suitable for online use [41].
• Subject-Specific Training: For optimal performance, train a subject-specific model. A CNN-based framework like Motion-Net, trained and tested on individual subjects, has shown high efficacy in removing motion artifacts from EEG, making it ideal for personalized real-time systems [7].
• Avoid Offline-Only Methods: Be cautious of methods like ICA and spline interpolation, which are often computationally heavy and designed for offline analysis [3] [43] [41].

Q3: How can we effectively remove large motion artifacts that completely overwhelm the underlying neural signal?

A3: Large artifacts that mask the brain signal require a detection step before correction.

• Anomaly Detection with Autoencoders: Train an autoencoder (AE) exclusively on clean EEG data. The reconstruction error can then be used as an anomaly metric; segments with high error are classified as containing large artifacts. This allows for the detection and subsequent rejection or specialized correction of heavily corrupted segments [44].
• Two-Stage Decomposition and Fusion: Employ a two-stage method such as Wavelet Packet Decomposition with Canonical Correlation Analysis (WPD-CCA). This technique first decomposes the single-channel signal and then uses CCA to isolate and remove artifact components, proving highly effective for large motion artifacts in both EEG and fNIRS [28].

Q4: What is a major advantage of using a deep learning autoencoder over traditional methods like ICA for artifact removal?

A4: The key advantage is the automation and elimination of manual intervention.

• ICA requires manual observation and identification of artifactual components for removal, a process that is time-consuming, labor-intensive, and prone to inaccuracies [43].
• Deep Autoencoders, such as LSTEEG or IC-U-Net, learn to automatically encode clean signal characteristics. They can be deployed in automated processing pipelines, significantly reducing the need for expert input and enabling scalable, real-time preprocessing [44].

Experimental Protocols & Performance Benchmarks

Detailed Methodology: 1D CNN with Penalty Network for fNIRS

This protocol outlines the procedure for real-time motion artifact suppression in fNIRS signals using a novel 1D Convolutional Neural Network [41].

• Objective: To remove motion artifacts from single-channel fNIRS signals in real-time with minimal prior data, adapting to various experimental paradigms.
• Network Architecture:
  • Main 1D CNN: Comprises seven convolutional layers. The first four are followed by max-pooling layers, and the last three by up-sampling layers. The final output is generated through a fully connected layer.
  • Penalty Network: A parallel, three-layer fully connected network that takes the same input. Its output is concatenated with the main CNN's output, providing a weighting mechanism that enhances robustness.
• Training Data: The network is trained on simulated data generated from the balloon model for initial validation, and on semi-simulated data for experimental validation.
• Input Processing: A moving window technique with input data augmentation is used during training.
• Key Steps:
  • Data Preparation: Segment continuous fNIRS signals (e.g., HbO or HbR) into epochs using a moving window.
  • Model Training: Train the 1DCNNwP model to map corrupted signal segments to their clean counterparts, minimizing the mean squared error (MSE).
  • Real-Time Application: Deploy the trained model, where new signal samples are fed through the network via the moving window for instantaneous artifact suppression.

Detailed Methodology: Motion-Net for EEG Artifact Removal

This protocol describes a subject-specific deep learning approach for removing motion artifacts from mobile EEG (mo-EEG) signals [7].

• Objective: To develop a subject-specific CNN model that robustly removes motion artifacts from EEG recordings on a single-trial basis.
• Network Architecture: A U-Net-based CNN (Motion-Net) designed for 1D signal reconstruction.
• Input Features: The model is trained using three different approaches, incorporating both raw EEG signals and Visibility Graph (VG) features to enhance learning stability with smaller datasets.
• Training Paradigm: The model is trained and tested separately for each subject to account for inter-subject variability.
• Key Steps:
  • Data Preprocessing: Synchronize EEG and accelerometer data via trigger points and resampling. Perform baseline correction.
  • Feature Extraction: Calculate VG features from the EEG signals to capture structural properties.
  • Model Training: Train the Motion-Net model using a subject-specific hold-out partition of the data (e.g., 60/20/20 for training/validation/test).
  • Performance Evaluation: Use the artifact reduction percentage (η), SNR improvement, and Mean Absolute Error (MAE) to evaluate the model.

Quantitative Performance Comparison

Table 1: Performance Benchmarks of Learning-Based Motion Artifact Removal Methods

Model	Modality	Key Metric	Reported Performance	Reference
1D CNN with Penalty (1DCNNwP)	fNIRS	Signal-to-Noise Ratio (SNR) Improvement	> 11.08 dB improvement	[41]
Motion-Net (U-Net CNN)	EEG	Artifact Reduction (η) / SNR Improvement	86% ± 4.13 / 20 ± 4.47 dB	[7]
Motion-Net (U-Net CNN)	EEG	Mean Absolute Error (MAE)	0.20 ± 0.16	[7]
WPD-CCA (Two-Stage)	EEG	ΔSNR / Artifact Reduction (η)	30.76 dB / 59.51%	[28]
WPD-CCA (Two-Stage)	fNIRS	ΔSNR / Artifact Reduction (η)	16.55 dB / 41.40%	[28]

Table 2: The Scientist's Toolkit: Key Research Reagents & Materials

Item / Algorithm	Function in Experiment	Relevance to fNIRS vs. EEG
Accelerometer (IMU)	Provides a reference signal for motion detection. Used in hardware-based and some model-based correction methods.	More critical for fNIRS where optode movement causes artifacts. Also used in mobile EEG for correlation [3] [7].
Visibility Graph (VG) Features	Transforms EEG time-series into graph structures, providing topological features that improve deep learning model accuracy on smaller datasets.	Primarily featured in EEG research to enhance feature input for CNNs [7].
Balloon Model	A physiological model used to generate simulated, clean fNIRS hemodynamic responses for training deep learning models.	Specific to fNIRS as it simulates the hemodynamic response function (HRF) [41].
Wavelet Packet Decomposition (WPD)	A signal processing technique that provides an adaptive time-frequency decomposition of a signal, used to isolate artifactual components.	Applied to both EEG and fNIRS as a pre-processing step for single-channel artifact correction [28].
Canonical Correlation Analysis (CCA)	A statistical method applied after WPD to identify and remove components with the highest correlation to the artifact.	Used in both modalities as part of a hybrid, two-stage artifact removal framework [28].

Signaling Pathways and Workflow Diagrams

Diagram 1: 1D CNN with Penalty Network Architecture.

Diagram 2: Autoencoder Workflow for Artifact Detection & Correction.

Motion artifacts represent a significant challenge in mobile neuroimaging, particularly for functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG). These artifacts arise from imperfect contact between sensors (electrodes for EEG, optodes for fNIRS) and the scalp during subject movement, causing signal distortions that can obscure genuine brain activity signals [3]. Hardware-based solutions utilizing accelerometers and inertial measurement units (IMUs) provide a reference-based noise cancellation approach by directly measuring the motion causing these artifacts, enabling more effective separation of motion-induced noise from true neurophysiological signals [45] [3].

Fundamental Principles of IMU-Based Motion Artifact Correction

Core Mechanism and Signaling Pathway

The fundamental principle behind IMU-based motion artifact correction involves using motion data from accelerometers and gyroscopes as a reference signal in adaptive filtering frameworks. These systems operate on the premise that motion artifacts in physiological signals correlate with physical movement measured by IMUs, allowing for the creation of a noise reference that can be subtracted from the contaminated signal [45] [3].

This diagram illustrates the core signaling pathway for IMU-based motion artifact correction. The mechanical movement of a subject is simultaneously transduced into (1) electrical motion signals via IMU sensors and (2) motion artifacts that corrupt the physiological signals of interest. These inputs are processed through an adaptive filter, which uses the motion reference to estimate and subtract the artifact component from the contaminated signal [45].

Hardware Configurations and Their Implications

The physical configuration of motion sensors significantly impacts the effectiveness of artifact correction. Research demonstrates that attaching IMUs to individual electrodes provides superior artifact removal compared to using a single IMU for an entire recording system, as it captures local motion variations at each measurement point [45].

Table: IMU Configuration Approaches for Motion Artifact Correction

Configuration Approach	Implementation	Advantages	Limitations
Per-Electrode/Optode IMUs	Individual IMUs attached to each recording element [45]	Captures local motion variations; Enables channel-specific correction	Increased system complexity; Higher power requirements
Single System IMU	One IMU attached to the main recording unit [3]	Simplified design; Lower cost	Cannot detect local motion differences between channels
Multi-Sensor Hybrid	Combination of IMUs with other sensors (e.g., optical motion capture) [3]	Comprehensive motion tracking; Enhanced correction accuracy	Maximum complexity; Cost-prohibitive for many applications

Implementation Methodologies

Experimental Setup and Workflow

Implementing IMU-based motion correction requires careful hardware integration and signal processing. The following workflow outlines the key stages in establishing an effective motion correction system:

Hardware Integration Protocols

Successful hardware implementation requires attention to several technical aspects. For EEG applications, researchers have developed active electrode designs incorporating miniature low-power three-axis accelerometer and gyroscope IMUs (such as the STMicroelectronics LSM6DS3) mounted directly on individual electrode printed circuit boards (PCBs). This approach maintains balance with a circular PCB design weighing approximately 1.7g with 18mm diameter, using snap connectors compatible with standard wet and dry electrodes [45].

In fNIRS systems, accelerometers have been integrated through various approaches including Adaptive Filtering, Active Noise Cancellation (ANC), Accelerometer-Based Motion Artifact Removal (ABAMAR), and Acceleration-Based Movement Artifact Reduction Algorithm (ABMARA) [3]. These implementations typically place accelerometers on the optode holders or headgear to capture motion directly at the source-scalp interface.

Signal Processing and Adaptive Filtering

The core signal processing methodology employs normalized least mean square (NLMS) adaptive filtering, which uses the IMU-derived motion signals as a reference input to estimate and subtract motion artifacts from the contaminated physiological signals [45]. Critical processing steps include:

• IMU Signal Conditioning: Raw acceleration signals often require integration to velocity using cumulative trapezoidal numerical integration, as research indicates velocity correlates better with motion artifacts in bio-signals [45]. Gyroscope data typically undergoes filtering with a third-order Savitzky-Golay filter to reduce high-frequency noise.

• Bio-signal Preprocessing: EEG signals are filtered with Butterworth fourth-order zero-phase bandpass filters (0.16-40 Hz), while fNIRS signals undergo conversion to optical density changes before motion correction [45] [8]. Notch filtering (47.5-52.5 Hz) removes mains line interference.

• Adaptive Filter Implementation: The NLMS algorithm adaptively weights the motion reference signal to optimally fit the motion artifact component in the physiological signal, then subtracts this estimated artifact to recover the cleaned signal.

Troubleshooting Guide: FAQs and Solutions

Hardware Integration Issues

Q: What should I do if my IMU and EEG/fNIRS signals are not synchronized properly?

A: Implement hardware synchronization using a common clock signal or trigger mechanism. In post-processing, use clear temporal markers (such as sharp taps on the sensors) to align data streams. Research shows that proper synchronization can increase correlation between motion artifacts and accelerometer signals from 0.52 to 0.80 after alignment [7].

Q: How can I minimize the additional weight and bulk of IMUs on my EEG cap or fNIRS headgear?

A: Use miniature IMU components like the LSM6DS3 (used in recent research) and distribute weight evenly across the headgear. Balanced circular PCB designs approximately 18mm in diameter and weighing 1.7g have been successfully implemented without causing significant additional movement due to weight [45].

Signal Quality Problems

Q: Why does my adaptive filter perform poorly even with apparently good motion signals?

A: This may occur because acceleration signals sometimes correlate poorly with motion artifacts. Try integrating acceleration to velocity, as studies show velocity often has better correlation with motion artifacts in bio-signals [45]. Also ensure your motion sensors are capturing relevant movement axes.

Q: How do I handle different types of motion artifacts (spikes, slow drifts, etc.) with IMU-based methods?

A: Different artifact types may require tailored approaches. Research classifies motion artifacts into four types: Type A (spikes, >50 SD within 1s), Type B (peaks, 1-5s duration), Type C (gentle slopes, 5-30s), and Type D (baseline shifts, >30s) [8]. Ensure your motion reference signals adequately capture the frequency content of these different artifact types.

Performance Optimization

Q: What sampling rates should I use for IMUs in motion artifact correction?

A: Sample IMU data at rates sufficient to capture relevant motion frequencies. Research implementations typically use matching sampling rates for bio-signals and IMUs (e.g., 220Hz for EEG/ECG applications) [45]. Higher sampling rates may be necessary for capturing rapid head movements.

Q: When should I use gyroscope data in addition to accelerometer data?

A: Incorporate gyroscope data when rotational movements are significant in your experimental paradigm. Studies using both accelerometers and gyroscopes filter gyroscope data with a third-order Savitzky-Golay filter to reduce high-frequency noise before use in adaptive filtering [45].

Performance Metrics and Validation

Quantitative Assessment Metrics

Researchers should employ multiple metrics to validate motion correction performance:

Table: Performance Metrics for Motion Artifact Correction

Metric	Calculation	Interpretation	Typical Values
ΔSNR (Signal-to-Noise Ratio Improvement)	SNRafter - SNRbefore	Higher values indicate better noise reduction	16-30dB in successful implementations [28] [7]
η (Artifact Reduction Percentage)	(Artifactpowerbefore - Artifactpowerafter) / Artifactpowerbefore × 100%	Percentage of artifact power removed	41-87% in effective corrections [28] [7]
MAE (Mean Absolute Error)	1/n × Σ|cleanedsignal - groundtruth|	Lower values indicate better preservation of true signal	0.20 ± 0.16 in high-performance systems [7]
Data Quality Score	Correlation between cleaned signals and known sources	0-100% scale measuring signal fidelity	Improved from 15.7% to 55.9% in phantom tests [46]

Comparative Performance Analysis

Research comparing motion correction techniques demonstrates that hardware-based approaches offer distinct advantages. In fNIRS studies with pediatric populations (typically noisier than adult data), moving average and wavelet methods have shown particular effectiveness [8]. For EEG applications, the novel iCanClean algorithm, which can incorporate reference signals, outperformed other methods in phantom tests, improving data quality scores from 15.7% to 55.9% when multiple artifacts were present [46].

Research Reagent Solutions: Essential Materials and Tools

Table: Essential Research Components for IMU-Based Motion Correction

Component Category	Specific Examples	Function/Purpose	Implementation Notes
IMU Sensors	STMicroelectronics LSM6DS3 [45]	Measures acceleration and angular rotation	3-axis accelerometer & gyroscope; 16-bit sampling; Low power
Microcontroller Units	Arm Cortex-M0 (e.g., Simblee RFD77101) [45]	Data acquisition and system control	10-bit ADC; BLE capability; Sufficient I/O for multiple sensors
Bio-Signal Amplifiers	Instrumentation Amplifiers (e.g., Texas Instruments INA128) [45]	Amplifies weak physiological signals	High gain (e.g., 501); High common-mode rejection ratio
Active Electrode Components	Precision operational amplifiers (e.g., Linear Technology LTC6078) [45]	Buffering and signal conditioning at electrode site	Dual-channel; Low-power; Unity gain configuration
fNIRS System Components	ninjaNIRS22 system components [47]	Whole-head fNIRS acquisition	56 sources, up to 192 detectors; Open-hardware design
Validation Tools	Electrical phantom heads with embedded sources [46]	Algorithm validation and performance testing	Known ground-truth signals; Controlled artifact introduction

Hardware-based solutions utilizing accelerometers and IMUs provide effective reference-based noise cancellation for motion artifacts in fNIRS and EEG research. The integration of motion sensors directly at the measurement sites (electrodes or optodes) enables data-driven artifact removal through adaptive filtering approaches, significantly improving signal quality in mobile neuroimaging applications [45] [3].

Future developments in this field will likely focus on further miniaturization of sensors, improved wireless synchronization methods, and machine learning approaches that enhance the adaptive filtering process. As these technologies mature, hardware-based motion artifact correction will play an increasingly vital role in enabling robust neuroimaging studies in naturalistic settings and with clinical populations.

Motion artifacts represent a significant challenge in non-invasive neuroimaging, particularly for techniques like electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS). These artifacts can severely distort brain signals, leading to misinterpretation of neural data and compromising research outcomes, especially in real-world or clinical settings where patient movement is unavoidable. Selecting the appropriate artifact correction method is therefore critical for data integrity. This guide provides a structured framework for matching correction algorithms to your specific experimental design and data type, enabling researchers to make informed methodological decisions.

Frequently Asked Questions (FAQs)

FAQ 1: Why is motion artifact correction particularly important for fNIRS and EEG studies?

Motion artifacts are among the most significant sources of noise in both fNIRS and EEG data [28] [8]. For fNIRS, motion can cause various signal distortions including spikes, peaks, gentle slopes, and slow baseline shifts [8]. EEG signals are susceptible to artifacts from muscle twitches, head movements, and electrode displacement during activities like walking [7]. These artifacts can mimic neural activity of interest (such as epileptic spikes) or obscure underlying brain signals, potentially leading to erroneous conclusions in both basic research and clinical applications [7] [8]. The problem is especially pronounced in pediatric populations and studies requiring natural movement, where data tends to be noisier and trial rejection would result in unacceptable data loss [8].

FAQ 2: What are the fundamental differences between EEG and fNIRS that affect motion artifact correction?

EEG and fNIRS measure fundamentally different physiological processes with distinct artifact profiles:

• EEG records electrical activity from neurons with millisecond temporal resolution but limited spatial resolution. It is highly susceptible to environmental noise and motion artifacts [48] [49] [50].
• fNIRS measures hemodynamic responses by detecting changes in oxygenated (HbO) and deoxygenated hemoglobin (HbR) concentrations. It offers better spatial resolution but lower temporal resolution with an inherent 4-6 second delay in hemodynamic response [48] [51] [50].

These differences mean that motion artifacts affect each modality differently, necessitating specialized correction approaches. fNIRS artifacts often manifest as slow drifts, while EEG artifacts typically appear as high-frequency noise [48] [8].

FAQ 3: Which motion artifact correction method should I choose for my specific research setup?

Your choice should depend on your signal modality, experimental design, and processing requirements. The table below summarizes the performance of various techniques:

Table 1: Performance Comparison of Motion Artifact Correction Methods

Method	Modality	Key Principle	Reported Performance	Best Use Cases
WPD-CCA [28]	EEG	Wavelet Packet Decomposition with Canonical Correlation Analysis	ΔSNR: 30.76 dB, η: 59.51%	Single-channel EEG with significant motion artifacts
WPD-CCA [28]	fNIRS	Wavelet Packet Decomposition with Canonical Correlation Analysis	ΔSNR: 16.55 dB, η: 41.40%	Single-channel fNIRS denoising
Motion-Net [7]	EEG	CNN-based deep learning with Visibility Graph features	η: 86% ± 4.13, SNR improvement: 20 ± 4.47 dB	Mobile EEG with real-world motion artifacts
Moving Average & Wavelet [8]	fNIRS	Temporal filtering and multi-resolution analysis	Best outcomes for pediatric data	Pediatric populations with frequent movement
Hybrid EEG-fNIRS [48]	Both	Complementary information integration	Accuracy: 79.31% (vs 65.52% EEG alone, 58.62% fNIRS alone)	Multimodal studies needing enhanced classification

FAQ 4: How do I implement a basic motion artifact correction pipeline for fNIRS data?

A standard fNIRS processing pipeline incorporating motion correction involves sequential steps as shown in the workflow below:

Diagram 1: fNIRS Motion Correction Workflow

This workflow can be implemented using processing packages like Homer2 [8]. The critical decision point is selecting the appropriate correction method (WPD, spline interpolation, moving average, CBSI, etc.) based on your artifact characteristics and data quality.

FAQ 5: What experimental protocols have successfully used hybrid EEG-fNIRS for neurodegenerative disease classification?

Several studies have established protocols for classifying neurodegenerative diseases using hybrid EEG-fNIRS:

Table 2: Experimental Protocols for Neurodegenerative Disease Classification

Study Focus	Participants	Task Paradigm	Data Features	Classification Results
Alzheimer's Disease Classification [48]	29 subjects (HC, MCI, MAD, MSAD)	Random digit encoding-retrieval task	EEG-derived & fNIRS-derived features	Hybrid accuracy: 79.31% (EEG alone: 65.52%, fNIRS alone: 58.62%)
Parkinson's Disease Detection [51]	120 PD patients, 60 healthy controls	Prefrontal cortex monitoring during rest/tasks	Cerebral blood oxygen changes	SVM accuracy: 85%, f1 score: 0.85, AUC: 0.95
ALS vs. Controls [52]	9 ALS patients, 9 controls	Visuo-mental task	Mutual information-based hybrid features	Improved performance vs. single modality

The Alzheimer's study used a Pearson correlation coefficient-based feature selection strategy with a linear discriminant analysis classifier, identifying the right prefrontal and left parietal regions as key for tracking disease progression [48].

FAQ 6: What essential materials and tools are required for implementing these correction methods?

Table 3: Research Reagent Solutions for Motion Artifact Correction

Tool/Algorithm	Function	Implementation Requirements
Wavelet Packet Decomposition (WPD) [28]	Decomposes signals into frequency sub-bands for artifact isolation	Signal processing toolbox, wavelet packets (db1, db2, sym, etc.)
Canonical Correlation Analysis (CCA) [28]	Identifies relationships between multivariate datasets	Statistical toolbox, multiple input channels
Convolutional Neural Networks (Motion-Net) [7]	Learns artifact patterns from data for removal	Python/TensorFlow/PyTorch, training data with ground truth
Mutual Information-based Feature Selection [52]	Selects complementary features from multimodal data	Information theory toolbox, feature extraction pipeline
Homer2 Software Package [8]	Comprehensive fNIRS processing including motion correction	MATLAB environment, fNIRS data in supported format

Decision Framework for Method Selection

Use the following logical framework to guide your algorithm selection process:

Diagram 2: Method Selection Decision Framework

This decision tree incorporates the following key considerations:

• For mobile EEG studies with significant motion, deep learning approaches like Motion-Net provide superior performance [7].
• For fNIRS studies requiring real-time processing, simpler methods like Moving Average may be preferable, while offline analysis can leverage more computationally intensive approaches like WPD [28] [8].
• For hybrid EEG-fNIRS setups, mutual information-based feature selection optimally exploits complementary information from both modalities [52].

Selecting appropriate motion artifact correction methods requires careful consideration of your specific research context. Key factors include your signal modalities, subject population, experimental design, and analytical resources. The guidelines presented here provide evidence-based recommendations drawn from current literature, with performance metrics to inform your methodological decisions. As the field advances, deep learning approaches and sophisticated multimodal fusion techniques show particular promise for enhancing data quality in real-world research scenarios.

Optimizing Data Quality: Strategic Solutions for fNIRS and EEG Motion Challenges

Frequently Asked Questions

1. What is the most common source of motion artifacts in fNIRS and EEG? The most common source is relative movement between the optode/electrode and the scalp. This movement alters the optical or electrical contact, creating signals that are often orders of magnitude larger than the underlying physiological data you are trying to capture [53] [54] [55].

2. How can I improve optode stability for long-term or motion-prone recordings? For fNIRS, using collodion-fixed optical fibers is highly effective. This method adapts a standard from clinical EEG practice, using a clinical adhesive to secure miniaturized fiber tips directly to the scalp. Studies show this can reduce the percent signal change of motion artifacts by 90% and increase the Signal-to-Noise Ratio (SNR) by 6 and 3 fold at 690 and 830 nm wavelengths, respectively, compared to standard Velcro-based probes [53] [54].

3. Is it possible to co-localize fNIRS optodes and EEG electrodes without interference? Yes, recent designs enable co-localized optode-electrode placement. Custom fNIRS sources can be built to attach directly to EEG electrodes, allowing them to share the same position on the scalp. Research has demonstrated no observable interference from the fNIRS optodes on EEG spectral analysis, making this a promising approach for multimodal imaging without sacrificing modularity or portability [56].

4. What should I check if I see persistent drift or sudden "pops" in my signal? This is often a sign of loose electrode or optode contact with the scalp. It can be caused by a loose-fitting cap, body movement, or hair pushing the sensor away. For transient "pops," artifact rejection during processing can help. For persistent drift, the solution is to improve the physical contact by repositioning the sensor or ensuring a snug cap fit [55].

5. Besides hardware, what signal processing methods are effective against motion artifacts? Several software-based methods are effective:

• Wavelet-Based Filtering: Effective for handling motion artifacts as outliers in the signal without requiring extra measurements [57].
• Canonical Correlation Analysis (CCA): Often used in combination with other techniques like Wavelet Packet Decomposition (WPD) for a two-stage cleaning process, showing significant improvement in artifact removal for single-channel data [28].
• Deep Learning: Subject-specific CNN-based models like "Motion-Net" have been developed to remove motion artifacts from mobile EEG, showing high artifact reduction percentages [7].

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Comparison of Motion Artifact Reduction Techniques

The table below summarizes the performance of different hardware and signal processing techniques as reported in the literature.

Table 1: Performance of Motion Artifact Mitigation Methods

Method	Type	Key Performance Metrics	Key Findings / Advantages
Collodion-Fixed fNIRS Fibers [53] [54]	Hardware (fNIRS)	- 90% reduction in motion artifact signal change- 6x & 3x SNR increase (690/830 nm)- 2x SNR increase at rest	Superior to Velcro-based probes; allows for recording during excessive motion (e.g., epileptic seizures).
Co-localized Optode-Electrode Design [56]	Hardware (Multimodal)	- No observable interference in EEG spectra- Supported high-density (HD) fNIRS-EEG layout	Enables simultaneous HD-fNIRS and EEG without cross-talk, preserving standardized EEG layouts.
WPD-CCA Artifact Removal [28]	Signal Processing (EEG/fNIRS)	- EEG: 30.76 dB ΔSNR, 59.51% artifact reduction (η)- fNIRS: 16.55 dB ΔSNR, 41.40% artifact reduction (η)	A two-stage, single-channel method that outperforms many existing techniques.
Motion-Net (Deep Learning) [7]	Signal Processing (EEG)	- 86% ± 4.13 artifact reduction (η)- 20 ± 4.47 dB SNR improvement	A subject-specific CNN model that is effective with smaller datasets and real-world motion artifacts.

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Detailed Experimental Protocols

Protocol 1: Implementing Collodion-Fixed fNIRS Optodes

This protocol is adapted from methods used to successfully record fNIRS throughout epileptic seizures [53] [54].

• Preparation: Place a towel around the subject's shoulders to protect clothing from adhesive. Part the hair at the optode placement site using a cotton-tipped stick.
• Adhesive Application: Place a small square (2-3 cm) of collodion-impregnated gauze on the prepared scalp location.
• Drying: Use compressed air to dry the collodion adhesive completely, creating a secure bond.
• Optode Placement: The miniaturized optical fiber tip, which houses a glass prism and mirrored surface, is secured onto the scalp via this adhesive patch. The low profile and secure bond prevent movement relative to the scalp.

Protocol 2: Setting Up a Co-localized HD-fNIRS-EEG Probe

This protocol outlines the steps for creating a multimodal probe that allows electrodes and optodes to occupy the same scalp position [56].

• Probe Design: Design the HD-fNIRS and EEG layout in a software toolbox like AtlasViewer. Sources and detectors are arranged in a high-density (HD) layout (e.g., first-nearest neighbor distances of 8 mm). EEG electrodes are populated using standard 10-20/10-10 positions.
• Grommet Selection: For compatibility, select grommet types "NIRX2" for NIRSport2 fNIRS optodes and "EBPAS" for BrainVision LiveAmp EEG electrodes.
• Cap Fabrication: Convert the final probe design to an .stl file and 3D-print it using a flexible material like NinjaFlex TPU to create a custom cap.
• Sensor Integration: Mount the custom fNIRS source optodes directly onto the active EEG electrodes. These 3D-printed optodes snap onto the electrode housing, allowing a light pipe to contact the scalp through an access hole in the electrode, with a center-to-center distance of ~4.87 mm.

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The Scientist's Toolkit: Essential Materials

Table 2: Key Research Reagents and Materials

Item	Function	Application Context
Collodion Adhesive	A clinical adhesive used to firmly attach miniaturized fNIRS fiber tips or EEG electrodes directly to the scalp, drastically reducing motion-related signal disruptions.	Long-term clinical fNIRS monitoring, studies with patients prone to movement (e.g., epilepsy, pediatrics) [53] [54].
Flexible 3D-Printed Cap	A custom-fabricated cap (e.g., using NinjaFlex TPU) that holds both fNIRS optodes and EEG electrodes in a precise, co-localized arrangement according to a high-density design.	Multimodal HD-fNIRS-EEG studies aiming for high spatial resolution and coverage without sacrificing portability [56].
Conductive Electrode Gel	Enhances electrical conductivity between the scalp and EEG electrodes. For fNIRS, it also improves light coupling by displacing air at the optode-scalp interface.	Standard practice for both EEG and fNIRS recordings to ensure signal quality and stability [57].
Active EEG Electrodes	Electrodes with built-in amplification that reduce interference and artifacts caused by cable movement, improving signal quality in mobile settings.	Wearable EEG and mobile Brain-Computer Interface (BCI) applications [55].

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Workflow Diagram: Strategies for Motion Artifact Management

The diagram below illustrates a structured approach to minimizing motion artifacts, from physical setup to data processing.

Troubleshooting Guides

Guide 1: Addressing Motion Artifacts in fNIRS Data

Problem: My fNIRS data is contaminated with motion artifacts, leading to unreliable hemodynamic response data.

Solution: fNIRS signals are susceptible to motion artifacts from head movements, jaw movements (like speaking), or displacement of optodes. Several correction methods exist, each with different strengths and trade-offs between noise suppression and signal integrity [1].

Steps for Correction:

• Identify the Artifact Type: Determine if the artifact is a high-frequency spike, a slow baseline shift, or a low-frequency variation that mimics a hemodynamic response [1] [30].
• Select a Correction Method: Choose an algorithm based on the artifact type and your processing needs (online/real-time vs. offline). The table below summarizes standard methods.
• Apply and Validate: Process the signal and validate the correction using quality metrics like Signal-to-Noise Ratio (SNR) or Contrast-to-Noise Ratio (CNR) to ensure the hemodynamic response is physiologically plausible.

Comparison of Common fNIRS Motion Artifact Correction Methods

Method	Principle	Best For	Key Performance Metrics	Trade-offs
Wavelet Filtering [1]	Multi-resolution analysis to isolate and remove artifact components in specific frequency bands.	General use; effective on spike artifacts and task-correlated artifacts [1].	High success rate in reducing artifact area [1].	May distort high-frequency neural signals if wavelet parameters are not optimized.
Spline Interpolation (MARA) [1]	Identifies artifact segments and replaces them with a spline interpolation based on clean data portions.	Offline analysis; distinct, high-amplitude spikes [1].	N/A	Can over-smooth data if artifact segments are incorrectly identified, leading to loss of true signal.
1D CNN with Penalty (1DCNNwP) [41]	A deep learning model that uses convolutional layers and a penalty network to suppress artifacts in real-time.	Online/real-time processing; scenarios requiring minimal prior data [41].	Improves SNR by >11.08 dB; processes data in 0.53 ms/sample [41].	Requires training data; performance depends on the quality and variety of training datasets.
Temporal Derivative Distribution Repair (TDDR) [41]	Corrects artifacts by analyzing the statistical distribution of the temporal derivative of the signal.	Removing slow drifts and baseline shifts.	Effective in restoring Contrast-to-Noise Ratio (CNR) [41].	May be less effective for very high-amplitude, sudden motion artifacts [41].
Correlation-Based Signal Improvement (CBSI) [1]	Utilizes the negative correlation between HbO and HbR concentrations to improve the signal.	Simple, fast correction without complex parameter tuning.	N/A	Assumes a perfect negative correlation, which may not always hold, potentially introducing bias.

Guide 2: Addressing Motion Artifacts in EEG Data

Problem: Motion artifacts are contaminating my EEG recordings, particularly during mobile or simultaneous EEG-fMRI experiments.

Solution: EEG motion artifacts arise from electrode-skin interface changes, cable sway, or, in MRI environments, head movement in the magnetic field. The optimal correction strategy depends on the experimental setup [58] [12] [23].

Steps for Correction:

• Characterize the Source: Determine if artifacts stem from electrode impedance changes (common in mobile EEG) or induction from movement in static magnetic fields (EEG-fMRI) [58] [12].
• Choose a Hardware or Software Approach: For mobile EEG, consider using specialized amplifiers or reference sensors. For all setups, select a post-processing algorithm.
• Evaluate Component Integrity: After processing, use metrics like component dipolarity in Independent Component Analysis (ICA) to confirm that brain-derived signals have been preserved [23].

Comparison of Common EEG Motion Artifact Correction Methods

Method	Principle	Best For	Key Performance Metrics	Trade-offs
Artifact Subspace Reconstruction (ASR) [23]	Uses a sliding-window PCA to identify and remove high-variance components exceeding a threshold ("k").	Online mobile EEG; aggressive cleaning of high-amplitude artifacts.	Reduces power at gait frequency; improves ICA dipolarity [23].	A low "k" value can over-clean and remove neural signals. A high "k" may leave artifacts.
iCanClean [23]	Employs Canonical Correlation Analysis (CCA) to find and subtract noise subspaces highly correlated with reference noise signals.	Mobile EEG, especially with dual-layer electrodes; running artifacts [23].	Effectively recovers ERP components (e.g., P300); improves ICA dipolarity [23].	Performance with pseudo-reference signals (derived from EEG) is inferior to hardware reference signals.
Reference Layer Artifact Subtraction (RLAS) [12]	Uses a separate layer of electrodes to measure artifacts directly, which are then subtracted from the scalp EEG.	Motion artifacts in simultaneous EEG-fMRI.	N/A	Efficacy varies with movement type (less effective for head shakes); requires specialized hardware [12].
Independent Component Analysis (ICA) [23]	Blind source separation to isolate artifact components, which are then manually or automatically rejected.	Offline analysis when artifacts have distinct spatial and temporal features.	N/A	Prone to failure if artifacts are too large or pervasive; manual classification is subjective and time-consuming.

Guide 3: Choosing Between fNIRS and EEG for Motion-Prone Studies

Problem: I am designing a study involving patient movement and need to choose the most robust neuroimaging modality.

Solution: The choice between fNIRS and EEG involves a direct trade-off between spatial specificity and resilience to motion artifacts. fNIRS is generally less susceptible to motion artifacts, while EEG offers superior temporal resolution [59] [60].

Decision Workflow:

Frequently Asked Questions (FAQs)

Q1: What is the most effective single method for removing motion artifacts from fNIRS data? While the "best" method is data-dependent, a comparative study on real cognitive data found that wavelet filtering was the most effective approach, reducing the area under the curve of the artifact in 93% of cases [1]. For real-time applications, deep learning methods like 1DCNNwP show great promise, offering significant improvements in Signal-to-Noise Ratio with very low latency [41].

Q2: Why is it difficult to correct motion artifacts in EEG data recorded inside an MRI scanner? Motion artifacts in simultaneous EEG-fMRI are particularly challenging because they are caused by the complex movement of the head and EEG cables in the strong static magnetic field [12]. These artifacts are spatially and temporally variable. Furthermore, research shows that the head and cap do not always move as a perfectly rigid body, meaning the artifact can differ across electrodes, reducing the efficacy of reference-based correction methods for movements like head shakes [12].

Q3: Is it better to reject motion-contaminated trials or to correct them? It is almost always better to correct the artifacts rather than reject entire trials [1]. Trial rejection leads to a loss of statistical power and can render studies with limited trials or high artifact rates (common in infant, clinical, or mobile studies) unviable. Correction methods preserve data integrity and statistical power.

Q4: What are the key hardware considerations for minimizing motion artifacts? For fNIRS, using optodes fixed with collodion or a tight-fitting cap can reduce motion-induced decoupling [41] [1]. Integrating an accelerometer can also help detect motion events [41]. For EEG, using high-impedance amplifiers (≥1 GΩ) is critical when employing microelectrodes to prevent signal distortion at low frequencies [58]. Systems with dedicated motion reference sensors (like dual-layer electrodes for iCanClean) significantly improve artifact removal [23].

Q5: How can I validate that my artifact correction method didn't distort the underlying neural signal? Validation should include both signal quality metrics and physiological plausibility checks:

• Quantitative Metrics: Calculate Signal-to-Noise Ratio (SNR) or Contrast-to-Noise Ratio (CNR) before and after correction [41] [30].
• Physiological Plausibility: Ensure the corrected hemodynamic response (for fNIRS) follows the expected pattern (e.g., HbO increase with HbR decrease) [1]. For EEG, check that expected event-related potentials (ERPs) like the P300 are preserved with correct latency and topography [23].

Detailed Experimental Protocols

Protocol 1: Validating fNIRS Motion Correction Algorithms

This protocol is adapted from studies that evaluate the performance of different motion artifact correction techniques on real task data [1].

1. Objective: To quantitatively compare the efficacy of motion artifact correction methods (e.g., Wavelet, Spline, 1DCNNwP) in recovering a physiologically plausible hemodynamic response.

2. Materials:

• fNIRS system with configured sources and detectors.
• A cognitive or motor task paradigm (e.g., a color-naming task that induces jaw movement artifacts).
• Computing software with implemented correction algorithms (e.g., Homer2, custom scripts in Python/MATLAB).

3. Procedure:

• Data Acquisition: Record fNIRS data from participants performing the task. The paradigm should be designed such that motion artifacts are time-locked to the task (e.g., speaking aloud during a trial) [1].
• Artifact Identification: Manually or automatically mark segments of data contaminated with motion artifacts.
• Data Processing: Apply different motion correction algorithms to the raw data.
• Hemodynamic Response Extraction: For each condition and correction method, calculate the average HbO and HbR responses.
• Parameter Calculation: Compute quantitative metrics for each corrected signal.

4. Key Measurements to Record:

• Amplitude of the Hemodynamic Response: The peak change in HbO/HbR.
• Signal-to-Noise Ratio (SNR): Measure before and after correction [41].
• Contrast-to-Noise Ratio (CNR): Measure before and after correction [41].
• Morphology of the Response: Assess whether the shape of the recovered response is physiologically plausible.

Protocol 2: Comparing Mobile EEG Artifact Removal Techniques

This protocol is based on research evaluating artifact removal methods during whole-body movement like running [23].

1. Objective: To assess the performance of ASR and iCanClean in suppressing motion artifacts during locomotion and recovering stimulus-locked ERPs.

2. Materials:

• Mobile EEG system with a headset capable of secure placement.
• Optional: Dual-layer electrodes for iCanClean.
• A stimulus presentation system for a cognitive task (e.g., Flanker task).
• Treadmill or space for overground running.

3. Procedure:

• Experimental Design: Use a within-subjects design with two conditions: standing and running. Administer the same cognitive task in both conditions.
• Data Recording: Record EEG data while subjects perform the task during both standing and running.
• Data Preprocessing: Apply both ASR (with a standardized "k" parameter, e.g., 20) and iCanClean (with a defined R² threshold) to the running data.
• ICA Decomposition: Perform ICA on the datasets from all conditions and processing streams.
• Component Classification: Use ICLabel to classify components as brain or artifact.
• ERP Analysis: Extract and average ERPs time-locked to the cognitive task stimuli.

4. Key Measurements to Record:

• ICA Dipolarity: The number of brain-like, dipolar components from each processing method [23].
• Spectral Power: Power at the gait frequency and its harmonics before and after correction [23].
• ERP Components: Latency and amplitude of key ERP components (e.g., P300) in the running condition compared to the standing baseline [23].

The Scientist's Toolkit: Key Research Reagents & Materials

Item	Function	Example Use-Case
Collodion-fixed Optodes	Securely adhere fNIRS sources/detectors to the scalp to minimize relative movement during motion [41].	Studies with infants or clinical populations where sudden movements are likely.
Accelerometer	A hardware motion sensor integrated into the fNIRS cap to provide an independent measure of head movement timing [41].	Providing a reference signal for motion artifact detection algorithms (ABAMAR).
Dual-Layer EEG Electrodes	Specialized electrodes where the top layer records only motion-induced noise, providing a pristine reference for algorithms like iCanClean [23].	Mobile EEG studies involving walking, running, or whole-body movement to enhance artifact subtraction.
MR-Compatible EEG Cap with Reference Layer (RLAS)	An EEG cap with an additional conductive layer to directly measure the motion artifact induced in the MRI magnetic field for subtraction [12].	Simultaneous EEG-fMRI studies to mitigate motion artifacts originating from head rotation in the B₀ field.
High-Impedance Amplifier (≥1 GΩ)	An EEG amplifier with a very high input impedance to prevent signal distortion when using high-impedance microelectrodes [58].	Microelectrode studies investigating high-frequency oscillations or unit activity to preserve low-frequency signal content.

Signaling Pathway of Neurovascular Coupling and Measurement

This diagram illustrates the relationship between neural electrical activity and the hemodynamic response measured by fNIRS and EEG, which is the foundation of multimodal imaging [59].

FAQs: Motion Artifacts in Challenging Populations

FAQ 1: Why is motion artifact correction particularly critical for pediatric fNIRS and EEG studies?

Motion artifact correction is paramount in pediatric studies because data from children typically contains more motion artifacts than adult data [8]. Furthermore, the scope for collecting data is often limited by short attention spans, resulting in experimental designs that can be as brief as a few minutes [8]. Simply rejecting corrupted trials is often not feasible, as it can lead to an unacceptably low number of trials for analysis [1] [61]. Therefore, employing correction techniques that allow researchers to retain valuable data is essential.

FAQ 2: What types of motion artifacts are common in ambulatory studies?

In ambulatory settings, motion artifacts are diverse and pervasive. In fNIRS, they can arise from head movements (nodding, shaking), facial muscle movements (raising eyebrows, talking), and whole-body movements that cause sensor displacement [3]. For mobile EEG (mo-EEG), artifacts include muscle twitches causing sharp transients, vertical head movements during walking leading to baseline shifts, and gait-related amplitude bursts from sudden electrode displacement [7]. The arrhythmic nature of real-world movement makes these artifacts particularly challenging to resolve.

FAQ 3: For infant EEG, what is the trade-off between Independent Component Analysis (ICA) and Artifact Blocking (AB)?

A systematic comparison on infant EEG found that ICA is more sensitive (it better removes artifacts) but less specific (it distorts clean signals more). Conversely, Artifact Blocking (AB) has higher specificity, causing less distortion to clean EEG segments, but may not remove artifacts as effectively as ICA [62]. The choice depends on whether the research priority is maximal artifact removal or maximal preservation of neural signal integrity.

Troubleshooting Guides

Guide 1: Correcting Motion Artifacts in Pediatric fNIRS Data

• Problem: High-amplitude, frequent motion artifacts in child or infant fNIRS data are threatening data quality and reducing the number of valid trials.
• Investigation: First, identify the types of artifacts present. Common categories include spikes (Type A), longer peaks (Type B), gentle slopes (Type C), and slow baseline shifts (Type D) [8].
• Solution: Based on empirical evidence from multiple infant datasets, the most effective and reliable approach is the combined use of Spline interpolation and Wavelet filtering [61].
  • Workflow:
    • Identify Artifacts: Use motion artifact detection functions (e.g., hmrMotionArtifactbyChannel in Homer2) to locate the onset and duration of artifacts [8].
    • Apply Spline Correction: This method interpolates the corrupted data segments using cubic splines, effectively estimating and removing the artifact [1] [61].
    • Apply Wavelet Filtering: Following spline correction, use wavelet filtering to further denoise the signal. This multi-resolution analysis is powerful for isolating and removing motion-related noise [1] [61].
• Rationale: This combination has been shown to outperform either method used alone, effectively reducing between- and within-subject standard deviation and recovering nearly all corrupted trials across diverse infant datasets [61].

Guide 2: Removing Motion Artifacts from Ambulatory (Single-Channel) EEG

• Problem: Motion artifacts are corrupting single-channel EEG recordings from a wearable, ambulatory system, and traditional multi-channel correction methods are not applicable.
• Investigation: Determine the computational constraints. Some advanced methods are computationally intensive, which is a concern for battery-operated devices [63].
• Solution: Several single-channel capable methods are available, ranging from classical signal processing to modern deep learning.
  • Singular Spectrum Analysis (SSA): This is a low-computational complexity method suitable for power-constrained devices. It decomposes the signal and identifies artifact components based on the local mobility of eigenvectors for removal [63].
  • Wavelet-Based Techniques: A two-level SSA decomposition combined with a Relative Total Variation (RTV) filter has been proposed for effective motion artifact removal in ambulatory epileptic seizure detection, showing superior performance in metrics like signal-to-noise ratio improvement [64].
  • Deep Learning (Motion-Net): A subject-specific, 1D CNN model (Motion-Net) has been developed that can be trained on individual subjects. This approach incorporates visibility graph features to enhance performance with smaller datasets and has demonstrated high artifact reduction percentages [7].

Performance Comparison of Motion Correction Techniques

The table below summarizes the quantitative performance of various correction methods as reported in the literature.

Table 1: Efficacy of Motion Artifact Correction Techniques in fNIRS

Method	Reported Efficacy (Context)	Key Metrics
Wavelet Filtering	Most effective for cognitive tasks with low-frequency artifacts; best at reducing area under the curve (93% of cases) [1].	Area under the curve reduction, within-/between-subject standard deviation [1] [61].
Spline + Wavelet Combination	Best overall performance on semi-simulated and real infant data; recovered most corrupted trials [61].	Hemodynamic response recovery error, number of trials saved [61].
Moving Average (MA)	One of the best outcomes for pediatric data in a language task [8].	Evaluation based on predefined metrics for pediatric data [8].
Correlation-Based Signal Improvement (CBSI)	Effective performance in comparative studies [1] [8].	Physiological plausibility of recovered HRF [1].

Table 2: Efficacy of Motion Artifact Correction Techniques in EEG

Method	Reported Efficacy (Context)	Key Metrics
Motion-Net (Deep Learning)	86% ± 4.13 artifact reduction; 20 ± 4.47 dB SNR improvement [7].	Artifact reduction (η), SNR improvement, Mean Absolute Error [7].
Singular Spectrum Analysis (SSA)	Lower computational complexity (∼6x less than EEMD-CCA); successful artifact removal from single-channel EEG [63].	SNR improvement, percentage reduction in artifact, computational cost [63].
Independent Component Analysis (ICA)	Higher sensitivity for removing eye-movement artifacts in infant EEG than Artifact Blocking [62].	Signal-to-Noise Ratio (SNR), Power-Spectral Density (PSD) [62].
Artifact Blocking (AB)	Higher specificity (less distortion to clean signals) than ICA in infant EEG [62].	Multiscale Entropy (MSE), Power-Spectral Density (PSD) [62].

Experimental Protocols for Key Studies

Protocol 1: Evaluating fNIRS Motion Correction in a Pediatric Language Task [8]

• Participants: 12 children (age 6.8-12.6 years).
• Task: Auditory grammatical judgment language task (rapid event-related design). Children pressed a button to indicate if a sentence was correct or contained a grammatical error.
• fNIRS Acquisition: A TechEN-CW6 system with one emitter and three detectors (2.7 cm spacing) placed over the left inferior frontal gyrus (language area), sampled at 10 Hz.
• Motion Artifact Categorization: Artifacts were classified into four types (A: spike; B: peak; C: gentle slope; D: baseline shift) for targeted analysis.
• Comparison Methods: Six motion correction techniques (Wavelet, Spline, PCA, Moving Average, CBSI, and a combination of Wavelet & MA) were compared using modified functions in Homer2 and a homemade trend detection algorithm.

Protocol 2: Comparing ICA and Artifact Blocking in Infant EEG [62]

• Participants: 50 infants (6-18 months of age) from the longitudinal EEG-IP dataset.
• Task & Recording: Infants sat on caregivers' laps and watched videos while EEG was recorded.
• Manual Annotation: EEG segments were manually annotated for the presence of saccadic eye-movement artifacts.
• Correction & Analysis: Both ICA and Artifact Blocking (AB) were applied. Performance was benchmarked using:
  • The proportion of effectively corrected segments.
  • Signal-to-Noise Ratio (SNR).
  • Power-Spectral Density (PSD).
  • Multiscale Entropy (MSE).

Workflow Diagrams

fNIRS Pediatric Correction

EEG Ambulatory Correction

The Scientist's Toolkit

Table 3: Key Research Reagents and Materials

Item	Function in Experiment
Homer2 Software Package	A standard fNIRS processing package used to implement and test various motion correction algorithms like Spline interpolation and Wavelet filtering [8] [61].
Accelerometer / IMU	Auxiliary hardware attached to the subject or sensor to measure motion dynamics, providing a reference signal for artifact regression in both fNIRS and EEG [3].
Collodion-Fixed Fibers	A hardware solution for fNIRS that uses a strong adhesive to secure optodes to the scalp, mechanically reducing motion-induced decoupling [8].
ICLabels / ADJUST Classifiers	Automated classifiers used in EEG preprocessing to label Independent Components (ICs) as neural or artifactual, streamlining the ICA process [62].
Artifact Subspace Reconstruction (ASR)	An algorithmic method, adapted for infant EEG in pipelines like NEAR, that detects and corrects bad channels and removes artifact-contaminated signal segments [62].

Technical Troubleshooting Guides

Motion Artifact Identification and Correction

Problem: How can I determine if my signal contains motion artifacts and which correction method to apply?

Motion artifacts (MAs) remain a significant challenge in neuroimaging data acquisition. The following workflow provides a systematic approach for identification and correction:

Experimental Protocol for Motion Artifact Validation: Researchers can intentionally introduce controlled head movements to characterize artifact patterns. As demonstrated in recent studies, participants should perform standardized movements (nodding, shaking, tilting) while recording with synchronized video monitoring and inertial measurement units (IMUs). Computer vision algorithms can then extract head orientation data frame-by-frame to correlate specific movements with artifact signatures in the signals [13].

Hardware Integration and Signal Quality Issues

Problem: My EEG and fNIRS signals show poor synchronization and inconsistent spatial alignment. How can I resolve this?

Hardware integration challenges commonly arise from mismatched temporal resolution and improper sensor placement. The following table outlines specific troubleshooting steps:

Problem	Root Cause	Solution	Validation Method
Signal Desynchronization	Lack of shared clock system; Software triggering delays	Use hardware synchronization (TTL pulses); Implement shared acquisition software with microsecond precision	Check temporal alignment of event markers across modalities
Spatial Misalignment	Different scalp localization standards; Variable optode-electrode distances	Co-register using international 10-20 system; Use integrated caps with pre-defined compatible openings	Verify placement with 3D digitization; Check anatomical consistency
Cross-Talk Artifacts	Physical interference between EEG electrodes and fNIRS optodes	Use optimized sensor geometry; Ensure no overlapping contact points	Check for signal correlations during no-task conditions
Motion Artifact Discrepancy	Different vulnerability to movement types	Implement modality-specific correction before fused analysis	Introduce controlled movements to characterize responses

Implementation Protocol: For spatial co-registration, use 3D-printed customized helmets tailored to individual head sizes or composite polymer cryogenic thermoplastic sheets that can be molded to precise head contours at approximately 60°C. This approach provides better stability than standard elastic caps, reducing probe movement during experiments [49].

Frequently Asked Questions (FAQs)

Motion Artifact Correction

Q1: Which motion artifact correction method performs best for single-channel EEG and fNIRS?

Recent comparative studies indicate that two-stage correction techniques generally outperform single-stage methods. The following table quantifies the performance of various approaches:

Method	Modality	Average ΔSNR	Average η (\% Reduction)	Best Performing Wavelet
WPD (Single-Stage)	EEG	29.44 dB	53.48%	db2 (ΔSNR), db1 (η)
WPD-CCA (Two-Stage)	EEG	30.76 dB	59.51%	db1
WPD (Single-Stage)	fNIRS	16.11 dB	26.40%	fk4
WPD-CCA (Two-Stage)	fNIRS	16.55 dB	41.40%	fk8

The Wavelet Packet Decomposition combined with Canonical Correlation Analysis (WPD-CCA) demonstrates superior performance, increasing motion artifact reduction by 11.28% for EEG and 56.82% for fNIRS compared to single-stage WPD [17].

Q2: Why do EEG and fNIRS show different susceptibility to motion artifacts?

The fundamental differences in measurement principles explain their different artifact profiles:

EEG measures electrical potentials at the scalp surface, where even minor head movements can alter electrode-skin contact impedance, causing rapid signal spikes. fNIRS relies on optical measurements where motion primarily affects light coupling between optodes and skin, creating slower baseline shifts [65] [3].

Experimental Design

Q3: When should I choose a multimodal EEG-fNIRS approach over a single modality?

The decision should be based on your research questions and experimental constraints:

Research Goal	Recommended Approach	Rationale
Fast neural dynamics (e.g., ERPs)	EEG alone	EEG's millisecond resolution is essential
Spatial localization of cortical activity	fNIRS alone	fNIRS provides better spatial resolution for surface cortical areas
Complete picture of neural activity & hemodynamics	Combined EEG-fNIRS	Cross-verification through neurovascular coupling
Naturalistic settings with movement	fNIRS with limited EEG	fNIRS is more motion-tolerant [65]
BCI applications requiring both speed & accuracy	Combined EEG-fNIRS	Complementary features improve classification [66]

Q4: How can I validate that my corrected signals maintain physiological relevance?

After applying motion artifact correction, employ these validation techniques:

• Check neurovascular coupling: Ensure hemodynamic responses in fNIRS align temporally with neural activity patterns in EEG (expected ~2-6 second delay) [59]
• Verify task-specific responses: Confirm that cleaned signals show expected activation in task-relevant brain regions
• Compare with ground truth: If available, use periods of minimal movement as reference signals
• Assess classification performance: For BCI applications, verify that corrected signals maintain or improve task classification accuracy [67]

The Scientist's Toolkit: Essential Research Reagents and Materials

Item	Function	Specification Notes
Integrated EEG-fNIRS Caps	Simultaneous signal acquisition	Pre-configured optode-electrode placement; Prefer customized 3D-printed or thermoplastic designs
WPD-CCA Algorithm Software	Motion artifact correction	Implement with db1 wavelets for EEG, fk8 for fNIRS; Requires MATLAB or Python programming
Synchronization Hardware	Temporal alignment of modalities	TTL pulse generators or shared clock systems with microsecond precision
Computer Vision System	Movement quantification	Video recording with algorithms like SynergyNet for head orientation tracking [13]
Inertial Measurement Units (IMUs)	Motion artifact reference	9-degree of freedom sensors (3-axis accelerometer, gyroscope, magnetometer)
3D Digitization Equipment	Spatial co-registration	Infrared or electromagnetic systems to map sensor positions on scalp
Customized Helmets	Stable sensor placement	3D-printed or thermoplastic molded to individual head anatomy [49]

Advanced Cross-Verification Methodology

The unique advantage of multimodal EEG-fNIRS setups lies in their capacity for cross-verification through neurovascular coupling. The following experimental protocol enables systematic validation:

Protocol: Cross-Verification Through Neurovascular Coupling

• Simultaneous Recording: Acquire EEG and fNIRS signals during task performance with precise temporal synchronization
• Modality-Specific Processing:
  • EEG: Apply WPD-CCA with db1 wavelet, focus on event-related desynchronization/synchronization (ERD/ERS)
  • fNIRS: Apply WPD-CCA with fk8 wavelet, analyze HbO and HbR concentration changes
• Temporal Alignment: Account for the hemodynamic response delay of 2-6 seconds when correlating EEG and fNIRS signals
• Spatial Correlation: Map EEG topography to fNIRS activation patterns using co-registration techniques
• Cross-Validation: Verify that neural activity (EEG) corresponds spatially and temporally with hemodynamic responses (fNIRS) in expected brain regions

This approach is particularly valuable for detecting residual motion artifacts that might corrupt only one modality, as true neural activation should manifest in both electrical and hemodynamic responses according to neurovascular coupling principles [59] [68].

Common Pitfalls in Artifact Correction and How to Avoid Them

FAQ: Troubleshooting Motion Artifact Correction

Q: Why does my motion artifact correction method work well on EEG data but perform poorly when applied to my fNIRS signals?

A: EEG and fNIRS measure fundamentally different physiological phenomena. EEG records electrical activity, while fNIRS measures hemodynamic changes through light absorption [17]. Motion artifacts manifest differently: in EEG, they often cause high-amplitude, sharp spikes, whereas in fNIRS, they typically produce slower baseline drifts and signal spikes [18]. A method tuned for the fast, high-frequency nature of EEG artifacts may fail to capture the distinct characteristics of fNIRS motion noise. Furthermore, the signal-to-noise ratio (SNR) and the optimal frequency bands for artifact removal differ between the two modalities [17]. Always validate and potentially re-parameterize your correction algorithms for each specific signal type.

Q: I am using a wavelet-based technique for artifact correction, but I am losing important neural signals. What am I doing wrong?

A: This is a common pitfall often due to an inappropriate selection of the wavelet function or decomposition level. Using a single wavelet for both EEG and fIRS, or for different types of artifacts, can lead to signal loss. For instance, research shows that for EEG, the Daubechies 1 (db1) wavelet may provide the best percentage reduction in motion artifacts, while for fNIRS, the Fejer-Korovkin 4 (fk4) wavelet might yield a higher SNR improvement [17]. To avoid this, systematically test different wavelet families (e.g., Db, Sym, Fk) and decomposition levels on a subset of your data where the neural signal of interest is known, and choose the one that maximizes artifact rejection while preserving signal integrity.

Q: My single-channel artifact correction is inconsistent. Should I switch to multi-channel methods?

A: While single-channel methods like Wavelet Packet Decomposition (WPD) are necessary for single-channel setups, they can be less robust than multi-channel methods. Techniques like Independent Component Analysis (ICA) require multiple channels to separate neural signals from artifacts effectively [69]. If your equipment allows, using multi-channel recordings and methods like ICA can provide a more robust correction. However, for single-channel data, a hybrid two-stage approach can significantly improve performance. For example, combining WPD with Canonical Correlation Analysis (WPD-CCA) has been shown to increase the percentage reduction in motion artifacts by over 11% for EEG and 56% for fNIRS compared to single-stage WPD alone [17].

Q: How can I tell if a high-amplitude spike is a motion artifact or an epileptiform discharge?

A: Misclassifying neural signals as artifacts is a critical error. Key differentiating factors include:

• Topographical Distribution: Motion artifacts are often maximal at the electrode sites closest to the movement (e.g., frontal for eye blinks, temporal for jaw clenching). Genuine epileptiform activity has a logical topographic field that aligns with known brain functional anatomy [18].
• Morphology: Motion artifacts like electrode "pops" appear as very abrupt, high-amplitude transients that look "unnatural" compared to the smoother morphology of brain signals [18].
• Context: Always note the subject's behavior during the recording. Correlate the timing of the spike with a video recording or experiment logs to see if it coincides with a head movement, swallow, or blink.

Performance of Artifact Correction Techniques

The table below summarizes the quantitative performance of different artifact correction methods as reported in a 2022 benchmark study. This data can help you select an appropriate method for your research [17].

Table 1: Performance Comparison of Motion Artifact Correction Techniques

Modality	Correction Method	Best Performing Wavelet	Average ΔSNR (dB)	Average η (\% Reduction)
EEG	Single-Stage (WPD)	Db2	29.44 dB	53.48% (Db1)
EEG	Two-Stage (WPD-CCA)	Db1	30.76 dB	59.51%
fNIRS	Single-Stage (WPD)	Fk4	16.11 dB	26.40%
fNIRS	Two-Stage (WPD-CCA)	Db1 / Fk8	16.55 dB (Db1)	41.40% (Fk8)

ΔSNR = Difference in Signal-to-Noise Ratio; η = Percentage Reduction in Motion Artifacts [17].

Experimental Protocol: Two-Stage WPD-CCA Motion Artifact Correction

This protocol details the methodology for implementing the high-performing WPD-CCA technique as cited in the research [17].

1. Objective: To robustly remove motion artifacts from single-channel EEG or fNIRS signals while preserving the underlying physiological information.

2. Materials and Software:

• A computing environment with signal processing capabilities (e.g., MATLAB, Python with SciPy).
• Recorded single-channel EEG or fNIRS data with known or labeled motion artifacts.
• Functions for Wavelet Packet Decomposition (WPD) and Canonical Correlation Analysis (CCA).

3. Step-by-Step Procedure:

• Step 1: Signal Decomposition using WPD

  • Select a wavelet packet family and decomposition level based on the modality. The research used Db1 for EEG and Fk8 for fNIRS for optimal results [17].
  • Decompose the contaminated single-channel signal into multiple wavelet packet nodes (sub-bands).

• Step 2: Create Multivariate Dataset from Nodes

  • Treat the obtained set of wavelet packet nodes as a multivariate dataset. This step artificially creates a multi-channel scenario from a single channel, enabling the use of CCA.

• Step 3: Apply Canonical Correlation Analysis (CCA)

  • Apply CCA to this multivariate dataset. CCA will identify and separate components that are highly correlated with the artifact, which is often consistent across multiple sub-bands.

• Step 4: Reconstruct the Signal

  • Reconstruct the signal by excluding the artifact-related components identified by CCA.
  • The output is a cleaned single-channel signal with a significantly reduced motion artifact component.

The following workflow diagram illustrates this two-stage process:

The Scientist's Toolkit: Essential Research Reagents & Materials

Table 2: Key Materials and Algorithms for Artifact Correction Research

Item	Function in Research
Wavelet Packet Decomposition (WPD)	A signal processing technique that decomposes a signal into a set of frequency sub-bands, providing a detailed time-frequency representation ideal for isolating non-stationary artifacts [17].
Canonical Correlation Analysis (CCA)	A statistical method used to find relationships between two sets of variables. In WPD-CCA, it identifies correlated artifact components across the wavelet nodes created from a single channel [17].
Independent Component Analysis (ICA)	A blind source separation technique used primarily with multi-channel data to statistically isolate neural signals from artifacts (e.g., eye blinks, muscle activity) into independent components [69] [18].
fNIRS Source-Detector Pair	The core hardware for fNIRS measurements. The placement geometry (distance) between the light source and detector determines the depth of sensitivity within the brain tissue.
Electrooculogram (EOG) Electrodes	Electrodes placed around the eyes to specifically record eye movements and blinks. This signal can be used as a reference to improve the regression-based removal of ocular artifacts from EEG [69].
High-Impedance EEG Amplifiers	Modern amplifiers that allow for higher electrode impedances, reducing the sensitivity to certain types of motion artifacts and electrode "pops" [18].

Benchmarking Performance: Validating and Comparing Correction Techniques for fNIRS and EEG

Frequently Asked Questions

What are the most effective metrics for quantifying motion artifact correction? The most established performance metrics are the difference in Signal-to-Noise Ratio ((\Delta SNR)) and the percentage reduction in motion artifacts ((\eta)) [28] [17]. These metrics are used to benchmark the performance of various artifact removal algorithms by measuring the improvement in signal quality and the specific amount of noise reduced.

How do I choose a motion artifact correction method for my single-channel data? For single-channel EEG or fNIRS data, algorithmic solutions like Wavelet Packet Decomposition (WPD) and its combination with Canonical Correlation Analysis (WPD-CCA) are highly effective [28] [17]. The choice can depend on your desired balance between performance and computational complexity. The table below summarizes the performance of different methods to guide your selection.

My fNIRS data is contaminated by motion. What are my options beyond basic filtering? A range of specialized methods exists, from hardware-based solutions to advanced algorithmic approaches [3]. Your choice should consider the type of signal, the availability of auxiliary hardware, and whether your application requires real-time processing. The following table categorizes the primary solutions available.

Why is it important to use a standardized dataset for validating my processing pipeline? Using open-access, standardized datasets with synthetic ground truth (like added Hemodynamic Response Functions) allows for the objective validation and benchmarking of novel artifact removal methods against established ones [70]. This ensures that reported performance is reliable and comparable across different studies.

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Performance Metrics for Motion Artifact Correction

The following table summarizes quantitative results from a study evaluating Wavelet Packet Decomposition (WPD) and a combined WPD-CCA method for motion artifact removal. The data provides a benchmark for the performance you can expect from these techniques [28] [17].

Table 1: Performance of WPD and WPD-CCA Methods

Signal Modality	Method	Best Performing Wavelet	Average (\Delta SNR)	Average (\eta)
EEG	Single-Stage (WPD)	db2	29.44 dB	53.48%
EEG	Two-Stage (WPD-CCA)	db1	30.76 dB	59.51%
fNIRS	Single-Stage (WPD)	fk4	16.11 dB	26.40%
fNIRS	Two-Stage (WPD-CCA)	db1 / fk8	16.55 dB	41.40%

(\Delta SNR): Difference in Signal-to-Noise Ratio; (\eta): Percentage reduction in motion artifacts. Data sourced from [28] [17].

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This table categorizes common motion artifact removal techniques, their requirements, and key limitations to help you select an appropriate strategy for your experiment [3] [71].

Table 2: Common Motion Artifact Removal Techniques

Method Category	Examples	Compatible Signal Types	Key Limitations
Algorithmic (Single-Channel)	Wavelet Filtering (WPD), Spline Interpolation, Denoising Autoencoder (DAE)	Single-channel EEG/fNIRS	Often requires tuning of parameters (e.g., probability thresholds for Wavelets) [28] [71].
Algorithmic (Multi-Channel)	Principal Component Analysis (PCA), Canonical Correlation Analysis (CCA), Independent Component Analysis (ICA)	Multi-channel EEG/fNIRS	Requires multiple data channels; performance depends on probe geometry [71].
Hardware-Based	Accelerometer, Inertial Measurement Unit (IMU), Camera	fNIRS (primarily)	Requires additional equipment; data synchronization is needed [3].
Deep Learning	Denoising Autoencoder (DAE)	fNIRS	Requires a large, high-quality training dataset [71].

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Experimental Protocol: Validating with Synthetic Ground Truth

Using a dataset with a known, added signal is a robust way to validate the performance of your motion artifact correction pipeline. The following workflow is based on established methodologies [70].

Step-by-Step Guide:

• Data Acquisition: Start by acquiring clean, multi-modal resting-state data. An ideal dataset should include:
  • fNIRS signals from long-separation (~3 cm) and short-separation (~1 cm) channels [70].
  • Auxiliary physiological measurements like Photoplethysmography (PPG), respiration (RESP), and a 3-axis accelerometer to monitor head motion [70].
• Introduce Ground Truth: To the resting-state data, add a synthetic Hemodynamic Response Function (HRF). A common approach is to use a gamma function with a time-to-peak of 6 seconds and a total duration of 16.5 seconds [70]. The HRF should be added at random onsets within the data windows to simulate spontaneous brain activity.
• Process the Data: Apply your chosen motion artifact correction algorithm (e.g., WPD-CCA, DAE) to the dataset containing the synthetic HRF.
• Extract and Compare: Extract the processed signal from the channels where the HRF was added.
• Quantify Performance: Calculate performance metrics like (\Delta SNR) and (\eta) by comparing the processed signal containing the synthetic HRF to the original, known HRF template. This directly measures your algorithm's ability to recover a true neural signal from noisy data [28] [70].

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Method Selection Workflow

Navigating the various correction methods can be challenging. The following diagram provides a logical pathway to select the most suitable technique based on your data and resources [28] [3] [71].

The Scientist's Toolkit

Table 3: Essential Research Reagents and Materials

Item	Function in Experiment
Multimodal fNIRS-EEG Cap	Integrated headgear that holds EEG electrodes and fNIRS optodes in a fixed configuration to ensure co-registration of signals and reduce motion artifacts [72] [73].
Auxiliary Biosignal Sensors (PPG, RESP)	Records physiological confounds (heartbeat, respiration) that are essential for denoising algorithms and improving the contrast-to-noise ratio in fNIRS data [70].
Accelerometer / Inertial Measurement Unit (IMU)	Directly measures head motion, providing a reference signal for hardware-based motion artifact correction algorithms like adaptive filtering [3].
Synthetic Ground Truth Dataset	A benchmark dataset with known, added neural signals (e.g., HRF) used for objective validation and performance comparison of novel artifact removal methods [70].
Wavelet Packet Decomposition (WPD) Code	Software implementation of WPD algorithms for decomposing non-stationary signals like EEG/fNIRS, forming the basis for single-channel artifact removal [28] [17].

Troubleshooting Guides & FAQs

Q1: Why does my motion-corrected fNIRS data show improved CNR but degraded SNR?

A: This occurs when motion correction algorithms partially remove physiological signals along with artifacts. In fNIRS, motion artifacts often share spectral characteristics with actual hemodynamic responses.

Troubleshooting Steps:

• Verify correction algorithm parameters
• Check if physiological signal components are being oversmoothed
• Compare pre- and post-correction power spectra
• Validate with known task paradigms

Q2: How do I determine if MSE values indicate successful artifact correction in EEG?

A: MSE alone is insufficient. Use this validation protocol:

Experimental Protocol:

• Synthetic Artifact Injection: Add known motion artifacts to clean segments
• Correction Application: Process with your chosen method
• MSE Calculation: Compare to original clean signal
• Threshold Setting: Establish acceptable MSE ranges for your equipment

Acceptable Ranges:

EEG Type	Acceptable MSE Range	Notes
Resting-state	0.001-0.01 μV²	Lower due to stable baseline
Task-based	0.01-0.05 μV²	Higher tolerance for dynamics
High-motion	0.05-0.1 μV²	Maximum acceptable correction

Q3: What causes paradoxical SNR improvement in EEG after motion correction?

A: This typically indicates overfitting where the algorithm models noise as signal.

Diagnosis Protocol:

• Component Analysis: Run ICA to identify residual components
• Cross-validation: Use separate datasets for training and testing
• Ground Truth Comparison: If available, compare with motion-free recordings

Q4: How do I optimize CNR for drug response studies in fNIRS?

A: Drug studies require special consideration for baseline shifts and physiological noise.

Optimization Protocol:

• Baseline Recording: Extended pre-drug baseline (5-10 minutes)
• Control Channel Monitoring: Use inactive regions as reference
• Temporal Windows: Calculate CNR over appropriate drug effect periods

CNR Calculation Parameters:

Study Phase	Signal Window	Control Window	Optimal CNR Range
Pre-drug	Task blocks	Pre-task rest	1.5-2.5
Peak effect	Task blocks	Pre-drug baseline	2.0-3.5
Post-effect	Task blocks	Pre-task rest	1.5-2.5

Q5: Why do my fNIRS and EEG show different motion correction efficacy using the same metrics?

A: Fundamental physiological differences affect metric performance.

Comparative Analysis Protocol:

• Simultaneous Recording: Collect fNIRS-EEG data
• Artifact Injection: Introduce controlled motion artifacts
• Parallel Processing: Apply identical correction logic
• Metric Normalization: Scale metrics by modality-specific limits

Research Reagent Solutions

Reagent/Equipment	Function	Application Context
Optical phantoms with controlled scattering	fNIRS ground truth validation	Motion artifact simulation and correction validation
Gel-based EEG caps with motion sensors	Simultaneous motion tracking	EEG motion artifact source identification
Hemodynamic response simulators	fNIRS signal validation	CNR calculation accuracy testing
Motion platform systems	Controlled artifact generation	Standardized metric evaluation across labs
ICA/PLS toolkits	Component analysis	Signal separation efficacy quantification

Technical Support: Troubleshooting Guides and FAQs

FAQ: Addressing Common Challenges in Motion Artifact Correction

Q1: My fNIRS data from pediatric participants is exceptionally noisy. Which motion correction method should I prioritize? A1: Research indicates that for pediatric data, which often contains more motion artifacts than adult data, Moving Average (MA) and Wavelet-based methods have been shown to yield the best outcomes. Pediatric participants have shorter attention spans, leading to smaller datasets, making trial rejection an impractical strategy. Therefore, robust correction techniques that retain data are essential [8].

Q2: For my EEG-based BCI system, which motion artifact correction technique provides the highest signal-to-noise ratio improvement? A2: A novel two-stage technique, Wavelet Packet Decomposition in combination with Canonical Correlation Analysis (WPD-CCA), has demonstrated superior performance for single-channel EEG. Studies report it can achieve an average increase in signal-to-noise ratio (ΔSNR) of 30.76 dB and a 59.51% reduction in motion artifacts, outperforming many existing state-of-the-art methods [17] [28].

Q3: I am designing a real-time fNIRS neurofeedback study. What should I consider regarding motion artifact correction? A3: The choice between hardware and software solutions is critical. Hardware-based methods (e.g., using accelerometers) improve the feasibility of real-time artifact rejection. In contrast, many advanced algorithmic solutions are designed for offline post-processing. Furthermore, few studies discuss the filtering delay introduced by correction algorithms, a vital parameter for real-time applications where timing is crucial [3].

Q4: I am working with fNIRS data that contains motion artifacts correlated with the task (e.g., from speaking). Which correction method is most effective? A4: For challenging, task-correlated motion artifacts (e.g., low-frequency, low-amplitude artifacts from jaw movement during speaking), Wavelet filtering has been identified as the most effective approach. One study found it reduced the area under the curve where the artifact was present in 93% of cases, outperforming techniques like spline interpolation, PCA, and Kalman filtering [1].

Q5: Is it better to discard data segments with motion artifacts or to correct them? A5: Systematic evidence suggests that correcting for motion artifacts is almost always better than rejecting trials. Trial rejection can severely compromise statistical power, especially in studies with challenging populations or limited trials. Applying a robust correction technique allows for the retention of valuable data and improves the accuracy of the recovered hemodynamic response [1].

The following tables consolidate performance metrics from multiple systematic reviews and comparative studies to provide a clear overview of algorithm efficacy across EEG and fNIRS modalities.

Table 1: Performance of Novel WPD-based Techniques on Single-Channel EEG and fNIRS Signals

Modality	Correction Technique	Key Parameter	Performance Metric 1 (ΔSNR)	Performance Metric 2 (η)
EEG	WPD (Single-Stage)	db2 Wavelet Packet	29.44 dB (Average)	-
EEG	WPD (Single-Stage)	db1 Wavelet Packet	-	53.48% (Average)
EEG	WPD-CCA (Two-Stage)	db1 Wavelet Packet	30.76 dB (Average)	59.51% (Average)
fNIRS	WPD (Single-Stage)	fk4 Wavelet Packet	16.11 dB (Average)	26.40% (Average)
fNIRS	WPD-CCA (Two-Stage)	db1 Wavelet Packet	16.55 dB (Average)	-
fNIRS	WPD-CCA (Two-Stage)	fk8 Wavelet Packet	-	41.40% (Average)

Table 2: Comparative Efficacy of Established fNIRS Motion Correction Techniques (Based on Systematic Reviews)

Correction Technique	Key Finding / Performance	Context / Study Details
Spline Interpolation	Produced the largest average reduction in Mean-Squared Error (MSE): 55% [29].	Systematic comparison using real NIRS data with simulated activation.
Wavelet Analysis	Produced the highest average increase in Contrast-to-Noise Ratio (CNR): 39% [29].	Systematic comparison using real NIRS data with simulated activation.
Moving Average (MA)	One of the best-performing methods for real pediatric fNIRS data [8].	Comparison of six techniques on child participants during a language task.
Wavelet Filtering	Most effective for task-correlated artifacts; reduced artifact area in 93% of cases [1].	Tested on real cognitive data with motion artifacts from vocalization.
Trial Rejection	Outperformed by all major correction techniques; not recommended as a primary strategy [1].	Comparing correction vs. rejection on functional data accuracy.

Experimental Protocols: Methodologies from Key Studies

Protocol: Benchmarking Motion Correction Techniques Using Real Data with Simulated Hemodynamic Response

This established protocol allows for objective comparison of correction algorithms when the ground-truth hemodynamic response is known [29] [1].

• Data Acquisition: Collect real resting-state EEG or fNIRS datasets that are known to contain natural motion artifacts.
• Artifact Identification: Use automated algorithms (e.g., hmrMotionArtifact in Homer2 for fNIRS) or expert visual inspection to identify and mark periods of motion artifacts in the data.
• Simulated Activation: Add a known, synthetic Hemodynamic Response Function (HRF) to the resting-state data. This simulates a functional activation signal superimposed on the noisy background.
• Application of Correction Techniques: Apply various motion artifact correction algorithms (e.g., Spline, Wavelet, PCA, WPD-CCA) to the dataset containing the simulated HRF.
• Performance Quantification: Attempt to recover the average HRF from the corrected data. Calculate quantitative metrics by comparing the recovered HRF to the original, known simulated HRF. Key metrics include:
  • Mean-Squared Error (MSE): Measures the accuracy of the recovered HRF shape and amplitude.
  • Contrast-to-Noise Ratio (CNR): Measures the strength of the recovered signal relative to the background noise.
  • Pearson's Correlation Coefficient (R²): Quantifies how well the recovered HRF shape matches the simulated HRF.

Protocol: Novel WPD-CCA for Single-Channel Signal Denoising

This protocol details a recently proposed two-stage method for robust motion artifact correction [17] [28].

• Signal Decomposition: The motion-corrupted single-channel EEG or fNIRS signal is decomposed into a set of coefficients (nodes) using Wavelet Packet Decomposition (WPD). This creates a comprehensive time-frequency representation of the signal.
• Node Reconstruction for CCA: The WPD nodes are used to reconstruct a multi-channel dataset, which is a prerequisite for Canonical Correlation Analysis. This is a crucial step to extend CCA to single-channel applications.
• Canonical Correlation Analysis (CCA): CCA is applied to the reconstructed multi-channel dataset. CCA is a multivariate statistical method that separates signal components based on their correlation with a reference signal or by identifying maximally correlated components within the dataset itself. It effectively isolates and removes motion-related components that are non-correlated with the signal of interest.
• Signal Reconstruction: The artifact-free signal is reconstructed from the components identified by CCA, resulting in a cleaned version of the original single-channel signal.

WPD-CCA Signal Processing Workflow

The Scientist's Toolkit: Research Reagent Solutions

Table 3: Essential Tools for Motion Artifact Correction Research

Tool / Solution	Type	Function / Application	Example Use Case
Homer2 Software Package	Software Toolbox	A standard MATLAB-based toolbox for fNIRS data processing, including motion artifact detection and several correction algorithms (e.g., PCA, spline) [29] [8].	Preprocessing and initial testing of motion correction on fNIRS data.
Wavelet Toolbox (MATLAB)	Software Library	Provides functions for performing Discrete Wavelet Transform (DWT), Wavelet Packet Decomposition (WPD), and other wavelet-based analyses essential for implementing denoising algorithms [17].	Implementing custom wavelet-based motion artifact filters for EEG/fNIRS.
Accelerometer / IMU	Hardware Sensor	An auxiliary hardware device that provides a reference signal highly correlated with motion artifacts but not with neural activity. Used as an input for adaptive filtering and regression-based correction methods [3] [1].	Active Noise Cancellation (ANC) and motion artifact regression in mobile experiments.
Canonical Correlation Analysis (CCA)	Statistical Method	A multivariate technique that separates signal sources based on their correlation. It is often combined with decomposition methods (e.g., WPD, EMD) for artifact removal in single-channel signals [17] [28].	Creating hybrid two-stage correction methods like WPD-CCA for enhanced denoising.
Deep Neural Networks (DNNs)	Algorithmic Framework	Learning-based models (e.g., CNN, U-Net, Denoising Autoencoders) trained to reconstruct clean signals or HRFs from artifact-corrupted data. Promising for handling complex, non-linear artifacts [30].	Correcting severe motion artifacts in challenging recording environments where traditional methods fail.

Decision Pathway for Motion Artifact Correction

The following diagram synthesizes insights from the reviewed literature to provide a logical workflow for selecting an appropriate motion artifact correction strategy.

Algorithm Selection Decision Pathway

The Role of Semi-Simulated Data and Real-World Benchmarks in Validation

Troubleshooting Guides and FAQs

Frequently Asked Questions

Q1: What is the fundamental difference between motion artifacts in fNIRS versus EEG, and why does it matter for correction strategies?

Motion artifacts manifest differently in fNIRS and EEG due to their distinct measurement principles. In fNIRS, artifacts primarily arise from optode-skin decoupling, causing sudden changes in measured light intensity classified as spikes, baseline shifts, and low-frequency variations [74]. These are often large-amplitude disturbances that corrupt the hemodynamic response. In contrast, EEG artifacts typically involve electrical potentials from skin stretch, electrode movement, or cable motion. This distinction matters because effective fNIRS correction often targets hemodynamic response preservation, while EEG focuses on electrophysiological signal fidelity. Consequently, a method perfect for one modality may fail for the other.

Q2: My motion correction pipeline works perfectly on semi-simulated data but fails with real experimental data. What could be wrong?

This common issue often stems from limitations in your semi-simulation approach. Consider these factors:

• Over-simplified artifact modeling: Real motion artifacts contain complex signatures beyond simple spike or baseline-shift models. Ensure your semi-simulated artifacts capture the full variety found in your specific experimental context (e.g., jaw movements versus eyebrow-raising produce different artifacts) [74].
• Inadequate physiological noise incorporation: Real fNIRS signals contain physiological confounds (cardiac, respiratory, blood pressure oscillations) that interact with motion artifacts. Your semi-simulated data should include these elements, potentially using short-separation channel regression to better model systemic physiology [74].
• Validation gap: Verify your performance metrics align between semi-simulated and real-data contexts. A method showing 11.08 dB improvement on simulations should demonstrate comparable qualitative benefits with real data [41].

Q3: When should I choose to discard motion-contaminated segments versus applying correction algorithms?

The decision depends on your artifact type and contamination level:

• For datasets mostly free of baseline-shift artifacts, discarding contaminated frames after pre-processing often yields optimal results [74].
• When both spike and baseline-shift artifacts are present, discarding contaminated segments before pre-processing typically performs better [74].
• Consider the temporal requirements of your analysis. For event-related designs with sparse artifacts, discarding may be preferable. For continuous resting-state connectivity analysis, advanced correction like 1DCNNwP may be necessary to preserve data continuity [41].

Q4: How can I validate that my motion correction method isn't distorting the underlying neural signal?

Implement a multi-faceted validation strategy:

• Semi-simulated benchmarks: Add known artifacts to motion-free recordings and quantify signal recovery accuracy using metrics like signal-to-noise ratio (SNR) improvement and contrast-to-noise ratio (CNR) [41].
• Task-based verification: Apply your method to experimental data with known activation patterns (e.g., motor tasks) and verify that expected hemodynamic responses or electrophysiological features persist.
• Comparative analysis: Test multiple correction approaches (e.g., spline interpolation, wavelet filtering, TDDR, 1DCNNwP) and look for consensus in outcomes [74] [41].

Quantitative Comparison of Motion Correction Performance

Table 1: Performance Metrics of fNIRS Motion Correction Methods

Method	SNR Improvement (dB)	Computational Demand	Best Use Case	Key Limitations
1DCNNwP [41]	>11.08 dB (highest)	Medium (0.53 ms/sample)	Real-time processing, individual subject adaptation	Requires training, subject-specific adaptation
Spline Interpolation [74] [41]	Moderate	Low	Task-based studies with identifiable artifacts	Leaves residual high-frequency noise
Wavelet Filtering [74] [41]	Moderate	High	Removing high-frequency artifacts	Computationally expensive, modifies entire signal
TDDR [41]	Moderate	Low	Offline analysis with motion spikes	Less effective for baseline shifts
Signal Discarding [74]	Variable (depends on contamination)	Very Low	Low-motion environments, sparse artifacts	Data loss, problematic for continuous analysis

Table 2: Motion Artifact Characteristics in fNIRS versus EEG

Characteristic	fNIRS	EEG
Primary artifact sources	Optode-skin decoupling, head movements [41]	Electrode impedance changes, cable motion, skin stretch
Typical artifact morphology	Spikes, baseline shifts, low-frequency variations [74]	High-amplitude transients, drift, high-frequency noise
Impact on signal	Corrupts hemodynamic response (HbO/HbR)	Obscures neural oscillations, event-related potentials
Optimal correction approach	Spline, wavelet, 1DCNNwP [74] [41]	ICA, PCA, temporal filtering
Validation emphasis	Hemodynamic response preservation, SNR/CNR improvement [41]	Neural oscillatory power, topological consistency

Detailed Experimental Protocols

Protocol 1: Creating Semi-Simulated fNIRS Datasets for Benchmarking

This protocol generates semi-simulated data with controlled motion artifacts for method validation [74]:

• Collect motion-free baseline data: Record resting-state fNIRS from 35+ healthy adults during 15 minutes of stillness using your standard acquisition parameters.

• Characterize real motion artifacts: Extract representative motion artifacts from separate datasets with intentional movements or from highly contaminated segments. Categorize them as spike-like or baseline-shift artifacts [74].

• Create semi-simulated datasets:

  • Artificially add characterized motion artifacts to the motion-free baseline at controlled percentages (e.g., 5%, 10%, 20% contamination).
  • Systematically vary artifact types (spikes only, baseline shifts only, mixed).
  • Precisely document timing, amplitude, and duration of added artifacts to enable accurate performance quantification.

• Validation framework:

  • Apply multiple correction approaches to each semi-simulated dataset.
  • Compute correlation matrices between recovered and original clean signals.
  • Use quantitative metrics (SNR, CNR) to compare method performance across contamination levels.

Protocol 2: Real-Time Motion Artifact Correction Using 1DCNNwP

This protocol implements a convolutional neural network approach for real-time fNIRS processing [41]:

• Network architecture design:

  • Implement a one-dimensional convolutional neural network (1D CNN) with seven convolutional layers.
  • The first four layers should be followed by max-pooling layers, the subsequent three by up-sampling layers.
  • Add a parallel penalty network with fully connected layers to enhance robustness.
  • Final layer should match the input moving window size.

• Training strategy:

  • Generate training data using the balloon model for simulation validation.
  • Use semi-simulated data with known artifacts for experimental validation.
  • Implement a moving window and input data augmentation procedure.
  • Focus on subject-specific training with minimal prior experimental data requirements.

• Real-time implementation:

  • Process data with an average signal processing time of 0.53 ms per sample.
  • Validate performance visually and quantitatively against ground truth.
  • Compare with established methods (spline-interpolation, wavelet-based, TDDR) using statistical tests (t < -3.82, p < 0.01).

Research Reagent Solutions

Table 3: Essential Tools for fNIRS Motion Correction Research

Tool/Resource	Function	Application Context
1DCNNwP Architecture [41]	Neural network for real-time artifact suppression	Individual subject processing, minimal prior data requirements
Spline Interpolation (MARA) [74] [41]	Segment-based artifact correction	Offline analysis, identifiable motion artifacts
Wavelet Filtering [74] [41]	Frequency-domain artifact removal	High-frequency artifact contamination
Short-Separation Channels [74]	Regress out extra-cerebral physiological noise	Reducing systemic physiological contamination in resting-state FC
Accelerometer-based Detection [41]	Hardware-based motion tracking	Complementary motion detection for validation
Semi-Simulated Data Framework [74]	Controlled performance evaluation	Method benchmarking across contamination types and levels

Experimental Workflow Visualization

Semi-Simulated Data Validation Workflow

1DCNNwP Architecture for Real-Time Processing

Motion artifacts (MAs) represent a significant challenge in non-invasive neuroimaging, particularly for functional near-infrared spectroscopy (fNIRS) and electroencephalography (EEG). These artifacts arise from subject movement, leading to compromised data quality and potentially erroneous scientific conclusions [30] [33]. In fNIRS, motion causes imperfect contact between optodes and the scalp, resulting in signal artifacts such as high-frequency spikes, slow drifts, and baseline shifts [30]. EEG signals are equally vulnerable to movement, as electrode displacement or muscle activity introduces electrical noise that obscures genuine neural signals [75] [59]. The development of robust motion artifact correction techniques is therefore essential for advancing neurovascular and neuroelectrical research.

Despite numerous proposed algorithms for MA processing, the field lacks standardized evaluation criteria to assess their performance objectively [30] [15]. Researchers employ various metrics and validation approaches, making cross-method comparisons difficult and hindering scientific consensus on optimal correction strategies. This article proposes a universal evaluation framework for motion artifact correction methods, providing researchers with standardized protocols, troubleshooting guidance, and quantitative assessment tools to enhance methodological rigor and reproducibility in fNIRS and EEG studies.

Fundamental Differences: fNIRS vs. EEG Motion Artifacts

Nature and Origins of Artifacts

Understanding the distinct characteristics of motion artifacts in fNIRS versus EEG is fundamental to selecting appropriate correction strategies. The table below summarizes key differences:

Table 1: Characteristics of Motion Artifacts in fNIRS vs. EEG

Feature	fNIRS	EEG
Primary Signal Measured	Hemodynamic response (HbO/HbR concentration changes) [75]	Electrical activity from synchronized neuronal firing [59]
Physical Cause of MA	Optode displacement, non-orthogonal contact, pressure changes on scalp [33]	Electrode movement, cable motion, muscle activity [75]
Common MA Manifestations	High-frequency spikes, slow drifts, baseline shifts [30]	High-amplitude spikes, low-frequency drift, muscle artifact contamination
Typical MA Duration	Seconds to minutes (hemodynamic response time) [75]	Milliseconds to seconds (electrical signal time scale) [59]
Sensitivity to Movement	Moderate (more tolerant than EEG) [75]	High (very susceptible to movement) [75]

Troubleshooting Guide: Identifying Common Artifact Patterns

FAQ: How can I distinguish motion artifacts from true neural signals in my data?

Answer: Recognizing artifact patterns is the first step in effective correction:

• fNIRS Sudden Spike Artifacts: Abrupt, high-amplitude deviations in both HbO and HbR channels that occur simultaneously. These often result from head impacts or rapid optode displacement [33].
• fNIRS Baseline Shifts: Sustained signal drift where the post-artifact baseline differs significantly from the pre-artifact baseline. These typically arise from slow movements causing maintained optode pressure changes [30].
• EEG High-Frequency Noise: High-frequency, irregular patterns often caused by muscle movement or cable sway. These are particularly problematic in movement-intensive paradigms [75].
• EEG Slow Drifts: Very low-frequency signal changes resulting from perspiration or slow electrode displacement. These can obscure event-related potentials [59].

FAQ: Which modality is more suitable for studies involving significant movement?

Answer: fNIRS generally demonstrates superior tolerance to movement artifacts compared to EEG [75]. This makes fNIRS particularly advantageous for naturalistic studies involving walking, rehabilitation exercises, or research with pediatric populations. However, simultaneous fNIRS-EEG recordings can provide complementary information, with fNIRS offering better spatial localization and EEG providing superior temporal resolution [59] [26].

Universal Evaluation Metrics for Motion Artifact Correction

Quantitative Performance Metrics

A standardized framework for evaluating motion artifact correction methods requires multiple quantitative metrics that assess both noise suppression and signal fidelity. The following table summarizes essential metrics proposed for universal adoption:

Table 2: Universal Evaluation Metrics for Motion Artifact Correction Performance

Metric Category	Specific Metric	Calculation	Interpretation	Applicable Modalities
Noise Suppression	ΔSignal-to-Noise Ratio (ΔSNR) [28]	SNRpost - SNRpre	Higher positive values indicate better noise reduction	fNIRS & EEG
Noise Suppression	Percentage Reduction in Motion Artifacts (η) [28]	(MApre - MApost) / MApre × 100%	Higher percentage indicates greater artifact removal	fNIRS & EEG
Signal Distortion	Mean Squared Error (MSE) [30]	(1/n) × Σ(originali - correctedi)²	Lower values indicate less signal distortion	fNIRS & EEG
Signal Distortion	Contrast-to-Noise Ratio (CNR) [30]	(Signaltask - Signalrest) / σrest	Higher values indicate better preservation of physiological signals	Primarily fNIRS
Classification Accuracy	Confusion Matrix Metrics [30]	Accuracy, Precision, Recall, F1-score	Higher values indicate better restoration of brain-state discriminability	fNIRS & EEG (BCI contexts)

Troubleshooting Guide: Applying Evaluation Metrics

FAQ: How many metrics should I use to evaluate my motion correction algorithm?

Answer: Comprehensive evaluation requires multiple metrics from different categories. Relying on a single metric provides an incomplete picture. For example, an algorithm might show excellent noise reduction (high ΔSNR) but poor signal preservation (high MSE). We recommend using at least one metric from each category: noise suppression (e.g., ΔSNR), signal distortion (e.g., MSE), and task-performance (e.g., CNR or classification accuracy) [30] [15].

FAQ: What are common pitfalls in metric interpretation?

Answer: The most common pitfalls include:

• Over-optimizing for a single metric: This may improve one aspect of performance while degrading others. Balance across metrics is crucial.
• Ignoring task context: Metrics should align with your research goal. For brain-computer interface applications, classification accuracy after correction may be more relevant than ΔSNR [30].
• Inadequate baseline selection: For ΔSNR and η calculations, ensure baseline segments are truly artifact-free to avoid biased estimates.

Experimental Protocols for Method Validation

Benchmarking Framework

Validating motion artifact correction methods requires rigorous experimental protocols using both simulated and real datasets. The diagram below illustrates a comprehensive validation workflow:

Figure 1: Comprehensive Validation Workflow for Motion Artifact Correction Methods

Detailed Experimental Methodology

Simulated Data Generation: Create hybrid datasets by adding real motion artifacts (from motion-only recordings) to clean fNIRS/EEG baselines [15]. This approach provides ground truth for quantitative evaluation. For fNIRS, synthetic hemodynamic responses can be generated with varying shapes and amplitudes to test an algorithm's ability to recover known signals [30].

Experimental Data Collection: Implement protocols that induce controlled motion artifacts:

• Head movements: Systematic nodding, shaking, or tilting movements during resting-state recordings [33]
• Facial movements: Brow furrowing, jaw clenching, or talking [33]
• Body movements: Walking, arm movements, or posture adjustments that transmit motion to the head [33] [26]

Task Paradigms: Include both resting-state and task-active conditions (e.g., motor execution, observation, or imagery) to evaluate correction performance across different neural states [26].

Troubleshooting Guide: Experimental Validation

FAQ: How can I validate my correction method without ground truth data?

Answer: When pure ground truth is unavailable, employ these strategies:

• Use multiple complementary metrics to triangulate method performance [30] [15]
• Compare results across different motion paradigms to assess consistency
• Evaluate downstream analysis outcomes such as functional connectivity patterns or classification accuracy in brain-computer interfaces [15]
• Test robustness by applying the method to different signal-to-noise ratio conditions

FAQ: What is the minimum sample size for method validation?

Answer: While requirements vary by research question, studies with robust validation typically include:

• At least 20-30 participants for between-subject comparisons [26] [15]
• Multiple artifact types and intensities within subjects
• Both within-subject and between-subject replication analyses

Comparative Analysis of Correction Algorithms

Algorithm Performance Benchmarking

Recent comprehensive evaluations have compared the performance of various motion artifact correction algorithms. The table below synthesizes findings from multiple studies:

Table 3: Comparative Performance of Motion Artifact Correction Algorithms

Algorithm	Core Principle	Best For	Performance Highlights	Limitations
Temporal Derivative Distribution Repair (TDDR) [15]	Statistical repair of temporal derivatives based on normal distribution assumptions	Functional connectivity analysis	Superior recovery of original FC patterns; Best ROC performance	Requires parameter tuning
Wavelet Filtering Methods [28] [15]	Multiresolution analysis with thresholding of wavelet coefficients	Single-channel denoising; Combined with other methods	Effective for both fNIRS & EEG; Excellent noise suppression	Choice of wavelet basis affects performance
Wavelet-CCA (WPD-CCA) [28]	Wavelet decomposition followed by canonical correlation analysis	Single-channel EEG & fNIRS with prominent artifacts	Highest ΔSNR (30.76 dB for EEG; 16.55 dB for fNIRS)	Computationally intensive; Two-stage process
Kalman Filtering [15]	Autoregressive modeling with state estimation	Online, real-time applications	Suitable for real-time implementation	Requires noise covariance estimation
Spline Interpolation (MARA) [15]	Spline interpolation over detected artifact segments	Offline analysis with distinct artifacts	Supported in major toolboxes; Effective for sharp artifacts	Sensitive to accurate artifact detection
Correlation-Based Signal Improvement (CBSI) [15]	Leverages negative correlation between HbO and HbR	fNIRS with coupled HbO/HbR changes	No auxiliary hardware needed; Simple implementation	fNIRS-specific; Assumes negative correlation

Research Reagent Solutions: Essential Tools for Motion Artifact Research

Table 4: Essential Research Materials and Tools for Motion Artifact Investigation

Item Category	Specific Examples	Function in Research	Implementation Notes
Hardware Solutions	Accelerometers [33], 3D motion capture systems [33]	Direct measurement of head movement for reference-based artifact removal	Requires synchronization with fNIRS/EEG systems
Software Toolboxes	HOMER2 [30], NIRS-KIT [15]	Provide implemented algorithms for standardized processing	Facilitates method replication and comparison
Wavelet Packages	Daubechies (db1-db3), Symlets (sym4-sym6), Fejer-Korovkin (fk4-fk8) [28]	Basis functions for wavelet-based denoising methods	Wavelet choice affects performance; db1 often optimal for EEG [28]
Multimodal Platforms	Integrated EEG-fNIRS systems [59] [26]	Simultaneous acquisition for multimodal artifact analysis	Enables cross-validation of neural signals
Validation Datasets	Simulated data with ground truth [30] [15], Public repositories (e.g., OpenNeuro)	Benchmarking algorithm performance	Critical for method validation and comparison

Integrated Workflow for Motion Artifact Management

Implementing an effective motion artifact management strategy requires a systematic approach. The following workflow integrates prevention, correction, and validation:

Figure 2: Integrated Motion Artifact Management Workflow

Troubleshooting Guide: Algorithm Selection and Implementation

FAQ: How do I choose the right correction algorithm for my study?

Answer: Consider these factors when selecting an algorithm:

• Signal modality: fNIRS-specific methods (e.g., CBSI) won't work for EEG, while some wavelet methods are applicable to both [28] [15]
• Artifact characteristics: Sharp, transient artifacts vs. slow drifts may respond differently to various algorithms
• Computational requirements: Real-time applications need efficient methods like Kalman filtering, while offline analyses can use more computationally intensive approaches [15]
• Research objective: Functional connectivity studies may benefit most from TDDR, while brain-computer interfaces might prioritize classification accuracy post-correction [15]

FAQ: What are common implementation mistakes in motion artifact correction?

Answer: Frequent implementation errors include:

• Over-correction: Excessive denoising that removes physiological signals of interest
• Inadequate parameter tuning: Using default parameters without optimizing for your specific data characteristics
• Ignoring spatial aspects: For multi-channel data, failing to consider spatial patterns of artifacts
• Improper preprocessing: Applying correction algorithms without proper baseline correction or filtering

Establishing universal evaluation criteria for motion artifact correction in fNIRS and EEG research is crucial for advancing neuroimaging methodology. This proposed framework—incorporating standardized metrics, validation protocols, and algorithm benchmarking—provides a foundation for more rigorous and reproducible research practices. As the field evolves, future work should focus on developing modality-specific benchmarks, establishing reporting standards, and creating shared datasets with ground truth annotations.

The integration of machine learning approaches presents promising avenues for future development [30]. However, these methods must be evaluated using the same rigorous standards as traditional algorithms. By adopting a standardized framework for assessing motion artifact correction techniques, researchers can accelerate methodological advances and enhance the reliability of neuroimaging findings across both basic and clinical applications.

Conclusion

Motion artifact correction is not a one-size-fits-all endeavor but a critical, modality-specific consideration for high-quality neuroimaging. The choice between fNIRS and EEG, and the subsequent selection of a correction strategy, must be guided by the research question, with fNIRS offering greater motion tolerance for sustained cortical processes and EEG providing unparalleled temporal resolution for rapid neural dynamics. The future of artifact correction lies in the refinement of hybrid and learning-based methods, such as WPD-CCA and specialized neural networks like Motion-Net and AnEEG, which show significant promise in handling complex, real-world artifacts. For the biomedical and clinical research community, adopting robust, validated correction pipelines is paramount. This will not only enhance the reliability of neural data in drug development and clinical trials but also unlock the potential for more ecologically valid studies outside controlled laboratory settings, ultimately accelerating discoveries in neuroscience and improving patient outcomes.

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