Recognition of MI-EEG signals using extended-LSR-based inductive transfer learning
The proposed method not only promotes the transfer of valuable knowledge from the source domain to improve learning performance in the target domain when target domain training data are insufficient but also enhances application and generalization by incorporating multiple classic base models. The experimental results demonstrate the effectiveness of the proposed method in MI-EEG signal recognition.
Authors
- Jia Qu (ORCID: https://orcid.org/0000-0002-0595-3690)
- Zhibin Jiang (ORCID: https://orcid.org/0000-0002-8580-7609)
- Zekang Bian (ORCID: https://orcid.org/0000-0002-0512-9247)
- Keli Hu (ORCID: https://orcid.org/0000-0002-5628-7640)
- Jie Zhou (ORCID: https://orcid.org/0000-0001-7701-234X)
- Donghua Yu (ORCID: https://orcid.org/0000-0003-0464-9205)
Institutions
- Jiangnan University (CN)
- Shaoxing University (CN)
- Peking University (CN)
- Changzhou University (CN)
- Wuxi Taihu Hospital (CN)
Publication Details
- Journal
- Frontiers in Neuroinformatics
- Published
- 2025-04-09
- DOI
- https://doi.org/10.3389/fninf.2025.1559335
- Primary Topic
- EEG and Brain-Computer Interfaces
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Natural Science Foundation of China
- Government of Jiangsu Province
- Natural Science Foundation of Jiangsu Province
- Natural Science Foundation of Zhejiang Province