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Electroencephalogram Source Imaging and Brain Network Based Natural Grasps Decoding

Studying the decoding process of complex grasping movement is of great significance to the field of motor rehabilitation. This study aims to decode five natural reach-and-grasp types using sources of movement-related cortical potential (MRCP) and investigate their difference in cortical signal chara...

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Autores principales: Xu, Baoguo, Deng, Leying, Zhang, Dalin, Xue, Muhui, Li, Huijun, Zeng, Hong, Song, Aiguo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8669594/
https://www.ncbi.nlm.nih.gov/pubmed/34916905
http://dx.doi.org/10.3389/fnins.2021.797990
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author Xu, Baoguo
Deng, Leying
Zhang, Dalin
Xue, Muhui
Li, Huijun
Zeng, Hong
Song, Aiguo
author_facet Xu, Baoguo
Deng, Leying
Zhang, Dalin
Xue, Muhui
Li, Huijun
Zeng, Hong
Song, Aiguo
author_sort Xu, Baoguo
collection PubMed
description Studying the decoding process of complex grasping movement is of great significance to the field of motor rehabilitation. This study aims to decode five natural reach-and-grasp types using sources of movement-related cortical potential (MRCP) and investigate their difference in cortical signal characteristics and network structures. Electroencephalogram signals were gathered from 40 channels of eight healthy subjects. In an audio cue-based experiment, subjects were instructed to keep no-movement condition or perform five natural reach-and-grasp movements: palmar, pinch, push, twist and plug. We projected MRCP into source space and used average source amplitudes in 24 regions of interest as classification features. Besides, functional connectivity was calculated using phase locking value. Six-class classification results showed that a similar grand average peak performance of 49.35% can be achieved using source features, with only two-thirds of the number of channel features. Besides, source imaging maps and brain networks presented different patterns between each condition. Grasping pattern analysis indicated that the modules in the execution stage focus more on internal communication than in the planning stage. The former stage was related to the parietal lobe, whereas the latter was associated with the frontal lobe. This study demonstrates the superiority and effectiveness of source imaging technology and reveals the spread mechanism and network structure of five natural reach-and-grasp movements. We believe that our work will contribute to the understanding of the generation mechanism of grasping movement and promote a natural and intuitive control of brain–computer interface.
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spelling pubmed-86695942021-12-15 Electroencephalogram Source Imaging and Brain Network Based Natural Grasps Decoding Xu, Baoguo Deng, Leying Zhang, Dalin Xue, Muhui Li, Huijun Zeng, Hong Song, Aiguo Front Neurosci Neuroscience Studying the decoding process of complex grasping movement is of great significance to the field of motor rehabilitation. This study aims to decode five natural reach-and-grasp types using sources of movement-related cortical potential (MRCP) and investigate their difference in cortical signal characteristics and network structures. Electroencephalogram signals were gathered from 40 channels of eight healthy subjects. In an audio cue-based experiment, subjects were instructed to keep no-movement condition or perform five natural reach-and-grasp movements: palmar, pinch, push, twist and plug. We projected MRCP into source space and used average source amplitudes in 24 regions of interest as classification features. Besides, functional connectivity was calculated using phase locking value. Six-class classification results showed that a similar grand average peak performance of 49.35% can be achieved using source features, with only two-thirds of the number of channel features. Besides, source imaging maps and brain networks presented different patterns between each condition. Grasping pattern analysis indicated that the modules in the execution stage focus more on internal communication than in the planning stage. The former stage was related to the parietal lobe, whereas the latter was associated with the frontal lobe. This study demonstrates the superiority and effectiveness of source imaging technology and reveals the spread mechanism and network structure of five natural reach-and-grasp movements. We believe that our work will contribute to the understanding of the generation mechanism of grasping movement and promote a natural and intuitive control of brain–computer interface. Frontiers Media S.A. 2021-11-30 /pmc/articles/PMC8669594/ /pubmed/34916905 http://dx.doi.org/10.3389/fnins.2021.797990 Text en Copyright © 2021 Xu, Deng, Zhang, Xue, Li, Zeng and Song. 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
Xu, Baoguo
Deng, Leying
Zhang, Dalin
Xue, Muhui
Li, Huijun
Zeng, Hong
Song, Aiguo
Electroencephalogram Source Imaging and Brain Network Based Natural Grasps Decoding
title Electroencephalogram Source Imaging and Brain Network Based Natural Grasps Decoding
title_full Electroencephalogram Source Imaging and Brain Network Based Natural Grasps Decoding
title_fullStr Electroencephalogram Source Imaging and Brain Network Based Natural Grasps Decoding
title_full_unstemmed Electroencephalogram Source Imaging and Brain Network Based Natural Grasps Decoding
title_short Electroencephalogram Source Imaging and Brain Network Based Natural Grasps Decoding
title_sort electroencephalogram source imaging and brain network based natural grasps decoding
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8669594/
https://www.ncbi.nlm.nih.gov/pubmed/34916905
http://dx.doi.org/10.3389/fnins.2021.797990
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