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

Independent component analysis (ICA) is a multivariate approach that has been widely used in analyzing brain imaging data. In the field of functional near-infrared spectroscopy (fNIRS), its promising effectiveness has been shown in both removing noise and extracting neuronal activity-related sources...

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Bibliographic Details
Published in:Frontiers in neuroinformatics 2021-07, Vol.15, p.683735-683735
Main Authors: Zhao, Yang, Sun, Pei-Pei, Tan, Fu-Lun, Hou, Xin, Zhu, Chao-Zhe
Format: Article
Language:English
Subjects:
Algorithms
blind source separation
Data analysis
data processing
Decomposition
functional near-infrared spectroscopy
independent component analysis
Infrared spectroscopy
Mathematical models
MATLAB toolbox
Methods
multivariate analysis
Neuroimaging
Neuroscience
Noise
Older people
Physiology
Principal components analysis
Random variables
Software
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Summary:Independent component analysis (ICA) is a multivariate approach that has been widely used in analyzing brain imaging data. In the field of functional near-infrared spectroscopy (fNIRS), its promising effectiveness has been shown in both removing noise and extracting neuronal activity-related sources. The application of ICA remains challenging due to its complexity in usage, and an easy-to-use toolbox dedicated to ICA processing is still lacking in the fNIRS community. In this study, we propose NIRS-ICA, an open-source MATLAB toolbox to ease the difficulty of ICA application for fNIRS studies. NIRS-ICA incorporates commonly used ICA algorithms for source separation, user-friendly GUI, and quantitative evaluation metrics assisting source selection, which facilitate both removing noise and extracting neuronal activity-related sources. The options used in the processing can also be reported easily, which promotes using ICA in a more reproducible way. The proposed toolbox is validated and demonstrated based on both simulative and real fNIRS datasets. We expect the release of the toolbox will extent the application for ICA in the fNIRS community.
ISSN:1662-5196
1662-5196
DOI:10.3389/fninf.2021.683735