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Updated: Jan 7, 2026
Electroencephalography Network Indices as Biomarkers of Upper Limb Impairment in Chronic Stroke
Published on: July 14, 2023
Automated source domain EEG analysis based on graph theory for healthy controls and stroke patients in different
Jinfeng Lu1, Gege Zhan1, Jie Jia2
1Laboratory for Neural Interface and Brain Computer Interface, Engineering Research Center of AI and Robotics, Ministry of Education, Shanghai Engineering Research Center of AI and Robotics, MOE Frontiers Center for Brain Science, State Key Laboratory of Brain Function and Disorders, Institute of AI and Robotics, College of Intelligent Robotics and Advanced Manufacturing (CIRAM), Fudan University, Shanghai, China.
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This study aimed to compare functional brain networks and identify recovery markers in 12 stroke patients (SG) and 14 healthy controls (HG) using EEG during three fist-task paradigms. Analyzing clustering coefficient (CC), characteristic path length (CPL), small-world index (SWI), and frontal node strength across frequency bands, passive task revealed significant alpha band differences in CC/CPL/SWI between groups. Lower SG strength in alpha/mu vs. controls predicted better recovery. An automated source imaging pipeline reduced volume conduction effects, providing new insights into stroke rehabilitation outcomes. Large-scale source imaging shows promise for broader disease applications.
