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.

中文翻译：

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NIRS-ICA：用于 fNIRS 研究中的独立分量分析的 MATLAB 工具箱

独立成分分析是一种用于分析脑成像数据的多变量方法。在功能近红外光谱 (fNIRS) 领域，其在去除噪声和提取感兴趣的成分方面都表现出有前途的有效性。然而，由于其使用的复杂性，ICA 的应用仍然具有挑战性，并且 fNIRS 社区中仍然没有一个专门用于 ICA 处理的公共可用、易于使用的工具箱。在这项研究中，我们提出了 NIRS-ICA，这是一个 MATLAB 工具箱，用于减轻 ICA 应用于 fNIRS 研究的难度。NIRS-ICA 结合了常用的 ICA 算法进行源分离、用户友好的 GUI 和辅助源选择的定量评估指标，这有助于去除噪声和提取感兴趣的源。处理中使用的选项也可以轻松报告，这有助于以更可重复的方式使用 ICA。介绍了开发 NIRS-ICA 所涉及的算法，并基于具有代表性的 fNIRS 数据集演示了实现和用法。我们预计工具箱的发布将扩大 fNIRS 社区中 ICA 分析的应用。