ArticleFrontiers in neuroinformatics2021
NIRS-ICA: A MATLAB Toolbox for Independent Component Analysis Applied in fNIRS Studies.
Article in Frontiers in neuroinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed, 12 citations in OpenAlex.
- EBA-related body-preferring responses in the occipitotemporal cortex measured with functional near-infrared spectroscopy.Frontiers in human neuroscience · 2026Article
- Improved performance of fNIRS-BCI by stacking of deep learning-derived frequency domain features.PloS one · 2025Article
- Novel Feature Generation for Classification of Motor Activity from Functional Near-Infrared Spectroscopy Signals Using Machine Learning.Diagnostics (Basel, Switzerland) · 2024Article
- Optical imaging and spectroscopy for the study of the human brain: status report.Neurophotonics · 2022Article
- Motion artifacts removal and evaluation techniques for functional near-infrared spectroscopy signals: A review.Frontiers in neuroscience · 2022Review
- Two Independent Response Mechanisms to Auditory Stimuli Measured with Functional Near-Infrared Spectroscopy in Sleeping Infants.Trends in hearingArticle
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Authors and funding
5 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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