文献类型：期刊文献

英文题名：Reconstructing damaged fNIRS signals with a generative deep learning model

作者：Zhi, Yingxu[1]; Zhang, Baiqiang[1]; Xu, Bingxin[2]; Wan, Fei[2]; Niu, Peisong[1]; Niu, Haijing[1]

第一作者：Zhi, Yingxu

机构：[1] State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China; [2] Beijing Key Laboratory of Information Service Engineering, Beijing Union University, Beijing, 100101, China

第一机构：State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China

通讯机构：[1]State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, 100875, China

年份：2025

卷号：58

期号：2

外文期刊名：Artificial Intelligence Review

收录：EI(收录号：20245217581596);Scopus(收录号：2-s2.0-85212765866)

语种：英文

外文关键词：Deep reinforcement learning - Generative adversarial networks - Signal to noise ratio

摘要：Functional near-infrared spectroscopy (fNIRS) imaging offers a promising avenue for measuring brain function in both healthy and diseased cohorts. However, signal quality in fNIRS data frequently encounters challenges, such as low signal-to-noise ratio or substantial motion artifacts in one or multiple measurement channels, impeding the comprehensive exploitation of the data. Developing a valid method to improve the quality of damaged fNIRS signals is crucial, particularly given the extensive use of wearable fNIRS devices in natural settings where noise issues are even more unavoidable. Here, we proposed a generative deep learning approach to recover damaged fNIRS signals in one or more measurement channels. The model captured spatial and temporal variations in the time series of fNIRS data by integrating multiscale convolutional layers, gated recurrent units (GRUs), and linear regression analyses. We trained the model on a resting-state fNIRS dataset from healthy elderly individuals and evaluated its performance in terms of reconstruction accuracy and functional connectivity matrix similarity. Collectively, the proposed model exhbited an excellent performance for the reconstruction of damaged fNIRS time series. In individual channel-level, the model can accurately reconstruct damaged fNIRS time series (mean correlation = 0.80 ± 0.14) while preserving intervariable relationships (correlation = 0.93). In multiple channel-level, the model maintained robust reconstruction accuracy and consistency in terms of functional connectivity. Our findings underscore the potential of generative deep learning techniques in reconstructing damaged fNIRS signals, providing a novel perspective for the efficient utilization of data in clinical diagnosis and brain research. ? The Author(s) 2024.