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Data Processing in Functional Near-Infrared Spectroscopy (fNIRS) Motor Control Research.

Patrick W Dans1, Stevie D Foglia2, Aimee J Nelson1,2

  • 1Department of Kinesiology, McMaster University, Hamilton, ON L8S 4K1, Canada.

Brain Sciences
|June 2, 2021
PubMed
Summary

Choosing the right functional near-infrared spectroscopy (fNIRS) data processing methods is crucial for accurate human motor control research. This review guides researchers on selecting appropriate fNIRS techniques to ensure reliable experimental outcomes.

Keywords:
data processingfNIRSfunctional near-infrared spectroscopymotor controlprocessing techniques

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Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Human Motor Control Research

Background:

  • Functional near-infrared spectroscopy (fNIRS) is a non-invasive neuroimaging technique.
  • Data processing choices significantly impact fNIRS experimental results.
  • Standardized methodologies are needed for reliable human motor control studies.

Purpose of the Study:

  • To provide a comprehensive guide on fNIRS pre-processing and processing techniques.
  • To focus on methodologies relevant to human motor control research.
  • To inform researchers about common and alternative data analysis approaches.

Main Methods:

  • Systematic review of 123 articles in the human motor control field.
  • Analysis of fNIRS pre-processing and processing methodologies employed.
  • Identification of frequently used techniques and general processing considerations.

Main Results:

  • Commonly used techniques include frequency cutoff filters, wavelet filters, smoothing filters, and the general linear model (GLM).
  • Variations in processing methods can alter experimental outcomes.
  • The review details methodologies and considerations for these techniques.

Conclusions:

  • Appropriate fNIRS data processing is critical for valid human motor control research.
  • Understanding different techniques, including GLM and filtering methods, enhances study reliability.
  • This review serves as a resource for optimizing fNIRS data analysis in motor control.