Functional connectivity associated with severe upper limb impairment in resting-state electroencephalography among chronic stroke survivors: a machine learning approach
Our research highlights the association between alterations in connectivity within low-frequency bands and severe ULI across widespread brain regions, including areas outside the sensorimotor cortex and bilateral intrahemispheric and interhemispheric regions. Further research utilizing larger longitudinal datasets from early stroke survivors employing ML approaches could contribute to the development of more accurate predictive models for motor recovery and rehabilitation responses.
Authors
- Hyunji Kim (ORCID: https://orcid.org/0000-0003-1168-8149)
- Paik Nam-Jong
- Hee-Mun Cho (ORCID: https://orcid.org/0000-0003-3982-0703)
- Miseon Shim (ORCID: https://orcid.org/0000-0001-9708-7613)
- Won-Seok Kim
- Won Kee Chang
- Ji-Yoon Lee
- Han-Jeong Hwang
- Ji-Soo Choi
- Bongwon Suh
Institutions
- Seoul National University of Science and Technology (KR)
- Korea University (KR)
- Seoul National University Bundang Hospital (KR)
- Sejong University (KR)
- Sejong Institute (KR)
Publication Details
- Journal
- Journal of NeuroEngineering and Rehabilitation
- Published
- 2025-12-22
- DOI
- https://doi.org/10.1186/s12984-025-01859-7
- Citations
- 1
- Primary Topic
- EEG and Brain-Computer Interfaces
- Type
- article
- Field-Weighted Citation Impact
- 1.43
Funders
- Seoul National University Bundang Hospital
- National Research Foundation
- National Research Foundation of Korea
- Iran Telecommunication Research Center
- Ministry of Science and ICT, South Korea
- Institute for Information and Communications Technology Promotion