EEG-Based Brain Network Analysis of Chronic Stroke Patients After BCI Rehabilitation Training

Gege Zhan; Shugeng Chen; Yanyun Ji; Ying Xu; Zuoting Song; Junkongshuai Wang; Lan Niu; Jianxiong Bin; Xiaoyang Kang; Jie Jia

doi:10.3389/fnhum.2022.909610

EEG-Based Brain Network Analysis of Chronic Stroke Patients After BCI Rehabilitation Training

Front Hum Neurosci. 2022 Jun 27:16:909610. doi: 10.3389/fnhum.2022.909610. eCollection 2022.

Authors

Gege Zhan¹, Shugeng Chen², Yanyun Ji³, Ying Xu³, Zuoting Song¹, Junkongshuai Wang¹, Lan Niu⁴, Jianxiong Bin⁴, Xiaoyang Kang^{1

4

5

6}, Jie Jia^{2

7}

Affiliations

¹ Laboratory for Neural Interface and Brain Computer Interface, State Key Laboratory of Medical Neurobiology, Engineering Research Center of AI and Robotics, Ministry of Education, Shanghai Engineering Research Center of AI and Robotics, MOE Frontiers Center for Brain Science, Institute of AI and Robotics, Academy for Engineering and Technology, Fudan University, Shanghai, China.
² Department of Rehabilitation Medicine, National Clinical Research Center for Aging and Medicine, Huashan Hospital, Fudan University, Shanghai, China.
³ Shanghai Jinshan Zhongren Geriatric Nursing Hospital, Shanghai, China.
⁴ Ji Hua Laboratory, Foshan, China.
⁵ Yiwu Research Institute of Fudan University, Yiwu, China.
⁶ Research Center for Intelligent Sensing, Zhejiang Lab, Hangzhou, China.
⁷ National Center for Neurological Disorders, Shanghai, China.

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 (p < 0.05), and there was no significant difference in the FES group. (ii) Both the global and local graph theory measures of the brain network of patients with chronic stroke in the BCI-FES group were improved after rehabilitation training. (iii) The node strength in the contralesional hemisphere and central region of patients in the BCI-FES group was significantly higher than that in the FES group after the intervention (p < 0.05), and a significant increase in the node strength of C4 in the contralesional sensorimotor cortex region could be observed in the BCI-FES group (p < 0.05). These results suggest that BCI-FES rehabilitation training can induce clinically significant improvements in motor function of patients with chronic stroke. It can improve the functional integration and functional separation of brain networks and boost compensatory activity in the contralesional hemisphere to a certain extent. The findings of our study may provide new insights into understanding the plastic changes of brain activity in patients with chronic stroke induced by BCI-FES rehabilitation training.

Keywords: BCI therapy; EEG; brain network; chronic stroke; functional connectivity; motor function rehabilitation.