AUTHOR=Zhan Gege , Chen Shugeng , Ji Yanyun , Xu Ying , Song Zuoting , Wang Junkongshuai , Niu Lan , Bin Jianxiong , Kang Xiaoyang , Jia Jie TITLE=EEG-Based Brain Network Analysis of Chronic Stroke Patients After BCI Rehabilitation Training JOURNAL=Frontiers in Human Neuroscience VOLUME=16 YEAR=2022 URL=https://www.frontiersin.org/journals/human-neuroscience/articles/10.3389/fnhum.2022.909610 DOI=10.3389/fnhum.2022.909610 ISSN=1662-5161 ABSTRACT=
Traditional rehabilitation strategies become difficult in the chronic phase stage of stroke prognosis. Brain–computer interface (BCI) combined with external devices may improve motor function in chronic stroke patients, but it lacks comprehensive assessments of neurological changes regarding functional rehabilitation. This study aimed to comprehensively and quantitatively investigate the changes in brain activity induced by BCI–FES training in patients with chronic stroke. We analyzed the EEG of two groups of patients with chronic stroke, one group received functional electrical stimulation (FES) rehabilitation training (FES group) and the other group received BCI combined with FES training (BCI–FES group). We constructed functional networks in both groups of patients based on direct directed transfer function (dDTF) and assessed the changes in brain activity using graph theory analysis. The results of this study can be summarized as follows: (i) after rehabilitation training, the Fugl–Meyer assessment scale (FMA) score was significantly improved in the BCI–FES group (