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

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 functio...

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Autores principales: Zhan, Gege, Chen, Shugeng, Ji, Yanyun, Xu, Ying, Song, Zuoting, Wang, Junkongshuai, Niu, Lan, Bin, Jianxiong, Kang, Xiaoyang, Jia, Jie
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9271662/
https://www.ncbi.nlm.nih.gov/pubmed/35832876
http://dx.doi.org/10.3389/fnhum.2022.909610
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author Zhan, Gege
Chen, Shugeng
Ji, Yanyun
Xu, Ying
Song, Zuoting
Wang, Junkongshuai
Niu, Lan
Bin, Jianxiong
Kang, Xiaoyang
Jia, Jie
author_facet Zhan, Gege
Chen, Shugeng
Ji, Yanyun
Xu, Ying
Song, Zuoting
Wang, Junkongshuai
Niu, Lan
Bin, Jianxiong
Kang, Xiaoyang
Jia, Jie
author_sort Zhan, Gege
collection PubMed
description 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.
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spelling pubmed-92716622022-07-12 EEG-Based Brain Network Analysis of Chronic Stroke Patients After BCI Rehabilitation Training Zhan, Gege Chen, Shugeng Ji, Yanyun Xu, Ying Song, Zuoting Wang, Junkongshuai Niu, Lan Bin, Jianxiong Kang, Xiaoyang Jia, Jie Front Hum Neurosci Human Neuroscience 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. Frontiers Media S.A. 2022-06-27 /pmc/articles/PMC9271662/ /pubmed/35832876 http://dx.doi.org/10.3389/fnhum.2022.909610 Text en Copyright © 2022 Zhan, Chen, Ji, Xu, Song, Wang, Niu, Bin, Kang and Jia. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Human Neuroscience
Zhan, Gege
Chen, Shugeng
Ji, Yanyun
Xu, Ying
Song, Zuoting
Wang, Junkongshuai
Niu, Lan
Bin, Jianxiong
Kang, Xiaoyang
Jia, Jie
EEG-Based Brain Network Analysis of Chronic Stroke Patients After BCI Rehabilitation Training
title EEG-Based Brain Network Analysis of Chronic Stroke Patients After BCI Rehabilitation Training
title_full EEG-Based Brain Network Analysis of Chronic Stroke Patients After BCI Rehabilitation Training
title_fullStr EEG-Based Brain Network Analysis of Chronic Stroke Patients After BCI Rehabilitation Training
title_full_unstemmed EEG-Based Brain Network Analysis of Chronic Stroke Patients After BCI Rehabilitation Training
title_short EEG-Based Brain Network Analysis of Chronic Stroke Patients After BCI Rehabilitation Training
title_sort eeg-based brain network analysis of chronic stroke patients after bci rehabilitation training
topic Human Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9271662/
https://www.ncbi.nlm.nih.gov/pubmed/35832876
http://dx.doi.org/10.3389/fnhum.2022.909610
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