Zhao Yang, Sun Pei-Pei, Tan Fu-Lun, Hou Xin, Zhu Chao-Zhe
State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China.
IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China.
Front Neuroinform. 2021 Jul 14;15:683735. doi: 10.3389/fninf.2021.683735. eCollection 2021.
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.
独立成分分析（ICA）是一种多变量方法，已广泛应用于脑成像数据分析。在功能近红外光谱（fNIRS）领域，它在去除噪声和提取与神经元活动相关的源方面已显示出有前景的有效性。由于ICA使用复杂，其应用仍然具有挑战性，并且fNIRS社区仍然缺乏专门用于ICA处理的易于使用的工具箱。在本研究中，我们提出了NIRS-ICA，这是一个开源的MATLAB工具箱，以减轻fNIRS研究中ICA应用的难度。NIRS-ICA包含用于源分离的常用ICA算法、用户友好的图形用户界面（GUI）以及辅助源选择的定量评估指标，这有助于去除噪声和提取与神经元活动相关的源。处理中使用的选项也可以轻松报告，这促进了以更可重复的方式使用ICA。所提出的工具箱基于模拟和真实的fNIRS数据集进行了验证和演示。我们期望该工具箱的发布将扩展ICA在fNIRS社区中的应用。
