NIRS-ICA: A MATLAB Toolbox for Independent Component Analysis Applied in fNIRS Studies - Zhao, Yang; Sun, Pei-Pei; Tan, Fu-Lun; Hou, Xin; Zhu, Chao-Zhe (Frontiers in Neuroinformatics, 2021)
Independent component analysis is a multivariate approach for 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 components of interest. However, the application of ICA remains challenging due to its complexity in usage, and a public-available, easy-to-use toolbox dedicated to ICA processing is still not available in fNIRS community. In this study, we propose NIRS-ICA, a 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 sources of interest. The options used in the processing can also be reported easily, which promotes using ICA in a more reproducible way. The algorithms involved in developing NIRS-ICA are introduced and the implementation and usages are demonstrated based on a representative fNIRS data set. We expect the release of the toolbox will extent the application for ICA analysis in fNIRS community.