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Cross-association analysis of EEG and EMG signals according to movement intention state

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dc.contributor.author유선국-
dc.date.accessioned2018-07-20T08:13:18Z-
dc.date.available2018-07-20T08:13:18Z-
dc.date.issued2017-
dc.identifier.urihttps://ir.ymlib.yonsei.ac.kr/handle/22282913/160983-
dc.description.abstractRehabilitation within three months plays a significant role in the recovery of damaged motor functions following the onset of a stroke. To increase the effectiveness of rehabilitation, it is important to perform rehabilitative exercises with movement intention. This study analyzed the association between electroencephalogram (EEG) and electromyogram (EMG) signals in healthy individuals in an attempt to verify the differences between the two signals in corticomuscular connectivity as well as the time delay in the flow of information in accordance with the presence of movement intention. To examine the relationship between the brain and muscles, coherence and mutual information analyses were performed on the EEG signals in the motor cortex and EMG signals in the flexor digitorum superficialis muscle during grasping training. Coherence and mutual information between EEG and EMG signals were significantly higher and the time delay of information flow was shorter when subjects performed active exercise with movement intention than when they performed passive exercise without movement intention. These findings could be applied to the rehabilitation of stroke patients to develop a rehabilitative training system with heightened effectiveness through verification of the presence of movement intention in the patients.-
dc.description.statementOfResponsibilityrestriction-
dc.languageNetherlands-
dc.publisher1389-0417-
dc.relation.isPartOfCOGNITIVE SYSTEMS RESEARCH-
dc.rightsCC BY-NC-ND 2.0 KR-
dc.rightshttps://creativecommons.org/licenses/by-nc-nd/2.0/kr/-
dc.titleCross-association analysis of EEG and EMG signals according to movement intention state-
dc.typeArticle-
dc.contributor.collegeCollege of Medicine-
dc.contributor.departmentDept. of Medical Engineering-
dc.contributor.googleauthorByeongnam Kim-
dc.contributor.googleauthorLaehyun Kim-
dc.contributor.googleauthorYun-Hee Kim-
dc.contributor.googleauthorSun K. Yoo-
dc.identifier.doi10.1016/j.cogsys.2017.02.001-
dc.contributor.localIdA02471-
dc.relation.journalcodeJ03334-
dc.identifier.eissn1389-0417-
dc.identifier.urlhttps://www.sciencedirect.com/science/article/pii/S1389041716301371-
dc.subject.keywordElectroencephalogram (EEG)-
dc.subject.keywordElectromyogram (EMG)-
dc.subject.keywordMovement Intention-
dc.subject.keywordCoherence-
dc.subject.keywordMutual information-
dc.contributor.alternativeNameYoo, Sun Kook-
dc.contributor.affiliatedAuthorYoo, Sun Kook-
dc.citation.volume44-
dc.citation.number1-
dc.citation.startPage1-
dc.citation.endPage9-
dc.identifier.bibliographicCitationCOGNITIVE SYSTEMS RESEARCH, Vol.44(1) : 1-9, 2017-
dc.identifier.rimsid60875-
dc.type.rimsART-
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Medical Engineering (의학공학교실) > 1. Journal Papers

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