Clinical feasibility of motor hotspot localization based on electroencephalography using convolutional neural networks in stroke
We demonstrate that our EEG-based deep-learning approach can effectively identify individual motor hotspots, and the clinical feasibility of our algorithm by successfully applying the proposed approach to stroke patients. It can be used as an alternative to TMS for identifying motor hotspots, potentially enhancing the effectiveness of rehabilitation strategies.
Authors
- Ga-Young Choi (ORCID: https://orcid.org/0000-0003-2209-5517)
- Won Kee Chang (ORCID: https://orcid.org/0000-0001-9756-6817)
- Han-Jeong Hwang (ORCID: https://orcid.org/0000-0002-1183-1219)
- Nam‐Jong Paik (ORCID: https://orcid.org/0000-0002-5193-8678)
- Jeong-Kweon Seo (ORCID: https://orcid.org/0000-0003-0887-0062)
- S.W. Yoon (ORCID: https://orcid.org/0000-0002-7202-2837)
- Won‐Seok Kim (ORCID: https://orcid.org/0000-0002-1199-5707)
- Kyoung Tae Kim (ORCID: https://orcid.org/0000-0001-9355-8326)
Institutions
- Hallym University (KR)
- Korea University (KR)
- Seoul National University Bundang Hospital (KR)
- Sejong University (KR)
- Ulsan National Institute of Science and Technology (KR)
- Keimyung University (KR)
Publication Details
- Journal
- Journal of NeuroEngineering and Rehabilitation
- Published
- 2025-09-26
- DOI
- https://doi.org/10.1186/s12984-025-01736-3
- Primary Topic
- Transcranial Magnetic Stimulation Studies
- Type
- article
- Field-Weighted Citation Impact
- 0.00
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