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Related Experiment Video

Updated: Oct 26, 2025

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NIRS-ICA: A MATLAB Toolbox for Independent Component Analysis Applied in fNIRS Studies.

Yang Zhao1, Pei-Pei Sun1, Fu-Lun Tan1

  • 1State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China.

Frontiers in Neuroinformatics
|August 2, 2021
PubMed
Summary

Independent component analysis (ICA) is a powerful tool for analyzing functional near-infrared spectroscopy (fNIRS) data. We developed NIRS-ICA, an open-source MATLAB toolbox, to simplify ICA application and enhance reproducibility in fNIRS research.

Keywords:
MATLAB toolboxblind source separationdata processingfunctional near-infrared spectroscopyindependent component analysismultivariate analysissoftware

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Area of Science:

  • Neuroimaging
  • Biomedical Engineering
  • Signal Processing

Background:

  • Independent Component Analysis (ICA) is a key multivariate technique for brain imaging data analysis.
  • ICA shows promise in functional near-infrared spectroscopy (fNIRS) for noise reduction and neuronal activity extraction.
  • Current ICA application in fNIRS is hindered by complexity and lack of dedicated user-friendly tools.

Purpose of the Study:

  • To introduce NIRS-ICA, an open-source MATLAB toolbox designed to simplify ICA for fNIRS studies.
  • To provide researchers with an accessible tool for noise removal and neuronal source extraction in fNIRS data.
  • To promote reproducible ICA practices within the fNIRS research community.

Main Methods:

  • Development of NIRS-ICA, an open-source MATLAB toolbox.
  • Integration of common ICA algorithms for source separation.
  • Inclusion of a user-friendly graphical user interface (GUI) and quantitative evaluation metrics for source selection.
  • Validation using both simulated and real-world fNIRS datasets.

Main Results:

  • NIRS-ICA successfully facilitates noise reduction and extraction of neuronal activity-related sources in fNIRS data.
  • The toolbox provides a user-friendly interface and reporting features to enhance reproducibility.
  • Validation on diverse datasets confirms the toolbox's effectiveness.

Conclusions:

  • NIRS-ICA significantly lowers the barrier to applying ICA in fNIRS research.
  • The toolbox is expected to broaden the adoption and effective utilization of ICA within the fNIRS community.
  • NIRS-ICA promotes more accessible and reproducible neuroimaging analysis.