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:	Baoguo Xu, Leying Deng, Dalin Zhang, Muhui Xue, Huijun Li, Hong Zeng, Aiguo Song
Formato:	article
Lenguaje:	EN
Publicado:	Frontiers Media S.A. 2021
Materias:	natural reach-and-grasp decoding movement-related cortical potential EEG source imaging phase locking value brain network Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
Acceso en línea:	https://doaj.org/article/1ed49ff4671745819aae22f3fcc110e0
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id	oai:doaj.org-article:1ed49ff4671745819aae22f3fcc110e0
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spelling	oai:doaj.org-article:1ed49ff4671745819aae22f3fcc110e02021-12-01T16:55:44ZElectroencephalogram Source Imaging and Brain Network Based Natural Grasps Decoding1662-453X10.3389/fnins.2021.797990https://doaj.org/article/1ed49ff4671745819aae22f3fcc110e02021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fnins.2021.797990/fullhttps://doaj.org/toc/1662-453XStudying 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.Baoguo XuLeying DengDalin ZhangMuhui XueHuijun LiHong ZengAiguo SongFrontiers Media S.A.articlenatural reach-and-grasp decodingmovement-related cortical potentialEEG source imagingphase locking valuebrain networkNeurosciences. Biological psychiatry. NeuropsychiatryRC321-571ENFrontiers in Neuroscience, Vol 15 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	natural reach-and-grasp decoding movement-related cortical potential EEG source imaging phase locking value brain network Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
spellingShingle	natural reach-and-grasp decoding movement-related cortical potential EEG source imaging phase locking value brain network Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Baoguo Xu Leying Deng Dalin Zhang Muhui Xue Huijun Li Hong Zeng Aiguo Song Electroencephalogram Source Imaging and Brain Network Based Natural Grasps Decoding
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.
format	article
author	Baoguo Xu Leying Deng Dalin Zhang Muhui Xue Huijun Li Hong Zeng Aiguo Song
author_facet	Baoguo Xu Leying Deng Dalin Zhang Muhui Xue Huijun Li Hong Zeng Aiguo Song
author_sort	Baoguo Xu
title	Electroencephalogram Source Imaging and Brain Network Based Natural Grasps Decoding
title_short	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_sort	electroencephalogram source imaging and brain network based natural grasps decoding
publisher	Frontiers Media S.A.
publishDate	2021
url	https://doaj.org/article/1ed49ff4671745819aae22f3fcc110e0
work_keys_str_mv	AT baoguoxu electroencephalogramsourceimagingandbrainnetworkbasednaturalgraspsdecoding AT leyingdeng electroencephalogramsourceimagingandbrainnetworkbasednaturalgraspsdecoding AT dalinzhang electroencephalogramsourceimagingandbrainnetworkbasednaturalgraspsdecoding AT muhuixue electroencephalogramsourceimagingandbrainnetworkbasednaturalgraspsdecoding AT huijunli electroencephalogramsourceimagingandbrainnetworkbasednaturalgraspsdecoding AT hongzeng electroencephalogramsourceimagingandbrainnetworkbasednaturalgraspsdecoding AT aiguosong electroencephalogramsourceimagingandbrainnetworkbasednaturalgraspsdecoding
_version_	1718404738320433152

Electroencephalogram Source Imaging and Brain Network Based Natural Grasps Decoding

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