Resting-state cortical electroencephalogram rhythms and network in patients after chronic stroke

Jack Jiaqi Zhang (Corresponding Author), Zhongfei Bai (Corresponding Author), Kenneth N.K. Fong

Research output: Journal article publicationJournal articleAcademic researchpeer-review


Objective: To investigate the resting-state cortical electroencephalogram (EEG) rhythms and networks in patients with chronic stroke and examine their correlation with motor functions of the hemiplegic upper limb. Methods: Resting-state EEG data from 22 chronic stroke patients were compared to EEG data from 19 age-matched and 16 younger-age healthy controls. The EEG rhythmic powers and network metrics were analyzed. Upper limb motor functions were evaluated using the Fugl–Meyer assessment-upper extremity scores and action research arm test. Results: Compared with healthy controls, patients with chronic stroke showed hemispheric asymmetry, with increased low-frequency activity and decreased high-frequency activity. The ipsilesional hemisphere of stroke patients exhibited reduced alpha and low beta band node strength and clustering coefficient compared to the contralesional side. Low beta power and node strength in the delta band correlated with motor functions of the hemiplegic arm. Conclusion: The stroke-affected hemisphere showed low-frequency oscillations and decreased influence and functional segregation in the brain network. Low beta activity and redistribution of delta band network between hemispheres were correlated with motor functions of hemiplegic upper limb, suggesting a compensatory mechanism involving both hemispheres post-stroke.

Original languageEnglish
Article number32
JournalJournal of NeuroEngineering and Rehabilitation
Issue number1
Publication statusPublished - Dec 2024


  • Brain network
  • Connectivity
  • Electroencephalogram
  • Power spectrum
  • Stroke

ASJC Scopus subject areas

  • Rehabilitation
  • Health Informatics


Dive into the research topics of 'Resting-state cortical electroencephalogram rhythms and network in patients after chronic stroke'. Together they form a unique fingerprint.

Cite this