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Multimodal Neural Response and Effect Assessment During a BCI-Based Neurofeedback Training After Stroke
Stroke caused by cerebral infarction or hemorrhage can lead to motor dysfunction. The recovery of motor function is vital for patients with stroke in daily activities. Traditional rehabilitation of stroke generally depends on physical practice under passive affected limbs movement. Motor imagery-bas...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9247245/ https://www.ncbi.nlm.nih.gov/pubmed/35784834 http://dx.doi.org/10.3389/fnins.2022.884420 |
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author | Wang, Zhongpeng Cao, Cong Chen, Long Gu, Bin Liu, Shuang Xu, Minpeng He, Feng Ming, Dong |
author_facet | Wang, Zhongpeng Cao, Cong Chen, Long Gu, Bin Liu, Shuang Xu, Minpeng He, Feng Ming, Dong |
author_sort | Wang, Zhongpeng |
collection | PubMed |
description | Stroke caused by cerebral infarction or hemorrhage can lead to motor dysfunction. The recovery of motor function is vital for patients with stroke in daily activities. Traditional rehabilitation of stroke generally depends on physical practice under passive affected limbs movement. Motor imagery-based brain computer interface (MI-BCI) combined with functional electrical stimulation (FES) is a potential active neural rehabilitation technology for patients with stroke recently, which complements traditional passive rehabilitation methods. As the predecessor of BCI technology, neurofeedback training (NFT) is a psychological process that feeds back neural activities online to users for self-regulation. In this work, BCI-based NFT were proposed to promote the active repair and reconstruction of the whole nerve conduction pathway and motor function. We designed and implemented a multimodal, training type motor NFT system (BCI-NFT-FES) by integrating the visual, auditory, and tactile multisensory pathway feedback mode and using the joint detection of electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS). The results indicated that after 4 weeks of training, the clinical scale score, event-related desynchronization (ERD) of EEG patterns, and cerebral oxygen response of patients with stroke were enhanced obviously. This study preliminarily verified the clinical effectiveness of the long-term NFT system and the prospect of motor function rehabilitation. |
format | Online Article Text |
id | pubmed-9247245 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-92472452022-07-02 Multimodal Neural Response and Effect Assessment During a BCI-Based Neurofeedback Training After Stroke Wang, Zhongpeng Cao, Cong Chen, Long Gu, Bin Liu, Shuang Xu, Minpeng He, Feng Ming, Dong Front Neurosci Neuroscience Stroke caused by cerebral infarction or hemorrhage can lead to motor dysfunction. The recovery of motor function is vital for patients with stroke in daily activities. Traditional rehabilitation of stroke generally depends on physical practice under passive affected limbs movement. Motor imagery-based brain computer interface (MI-BCI) combined with functional electrical stimulation (FES) is a potential active neural rehabilitation technology for patients with stroke recently, which complements traditional passive rehabilitation methods. As the predecessor of BCI technology, neurofeedback training (NFT) is a psychological process that feeds back neural activities online to users for self-regulation. In this work, BCI-based NFT were proposed to promote the active repair and reconstruction of the whole nerve conduction pathway and motor function. We designed and implemented a multimodal, training type motor NFT system (BCI-NFT-FES) by integrating the visual, auditory, and tactile multisensory pathway feedback mode and using the joint detection of electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS). The results indicated that after 4 weeks of training, the clinical scale score, event-related desynchronization (ERD) of EEG patterns, and cerebral oxygen response of patients with stroke were enhanced obviously. This study preliminarily verified the clinical effectiveness of the long-term NFT system and the prospect of motor function rehabilitation. Frontiers Media S.A. 2022-06-17 /pmc/articles/PMC9247245/ /pubmed/35784834 http://dx.doi.org/10.3389/fnins.2022.884420 Text en Copyright © 2022 Wang, Cao, Chen, Gu, Liu, Xu, He and Ming. 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 | Neuroscience Wang, Zhongpeng Cao, Cong Chen, Long Gu, Bin Liu, Shuang Xu, Minpeng He, Feng Ming, Dong Multimodal Neural Response and Effect Assessment During a BCI-Based Neurofeedback Training After Stroke |
title | Multimodal Neural Response and Effect Assessment During a BCI-Based Neurofeedback Training After Stroke |
title_full | Multimodal Neural Response and Effect Assessment During a BCI-Based Neurofeedback Training After Stroke |
title_fullStr | Multimodal Neural Response and Effect Assessment During a BCI-Based Neurofeedback Training After Stroke |
title_full_unstemmed | Multimodal Neural Response and Effect Assessment During a BCI-Based Neurofeedback Training After Stroke |
title_short | Multimodal Neural Response and Effect Assessment During a BCI-Based Neurofeedback Training After Stroke |
title_sort | multimodal neural response and effect assessment during a bci-based neurofeedback training after stroke |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9247245/ https://www.ncbi.nlm.nih.gov/pubmed/35784834 http://dx.doi.org/10.3389/fnins.2022.884420 |
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