EEG-based brain network analysis in stroke patients during a motor execution task

Chunli Zhao, Rihui Li, Chushan Wang, Weitian Huang, Yingchun Zhang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Post-stroke survivors often suffer motor function disorders, which are usually associated with anatomical and functional alterations of brain network. Previous EEG-based brain network analyses mainly focused on stroke-linked brain network in resting state and single aspect (globally or regionally), leaving the pattern of functional connectivity (FC) in stroke patients during specific motion task uncovered yet. In this study, we investigated stroke specific FC patterns in patients who suffered unilateral hemispheric stroke during a motor execution task. Partial correlation coefficients between multiple electroencephalography (EEG) channels were computed to construct the functional networks for healthy controls and stroke patients. The graph-based analysis was then performed to characterize specific FC patterns in stroke patients. Results suggested that brain networks were characterized in stroke patients by lower global efficiency and clustering coefficient in alpha and beta band, compared to healthy controls. Regionally, stroke patients exhibited weaker local connection in motor area of affected hemisphere during motor execution, which may explain their motor deficits. The findings of our study may offer new insight to study the neural plasticity and brain reorganization after stroke.

Original languageEnglish (US)
Title of host publication9th International IEEE EMBS Conference on Neural Engineering, NER 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages887-890
Number of pages4
ISBN (Electronic)9781538679210
DOIs
StatePublished - May 16 2019
Event9th International IEEE EMBS Conference on Neural Engineering, NER 2019 - San Francisco, United States
Duration: Mar 20 2019Mar 23 2019

Publication series

NameInternational IEEE/EMBS Conference on Neural Engineering, NER
Volume2019-March
ISSN (Print)1948-3546
ISSN (Electronic)1948-3554

Other

Other9th International IEEE EMBS Conference on Neural Engineering, NER 2019
CountryUnited States
CitySan Francisco
Period3/20/193/23/19

ASJC Scopus subject areas

  • Artificial Intelligence
  • Mechanical Engineering

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