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Li M, Li J, Song Z, Deng H, Xu J, Xu G, Liao W. EEGNet-based multi-source domain filter for BCI transfer learning. Med Biol Eng Comput 2024; 62:675-686. [PMID: 37982955 DOI: 10.1007/s11517-023-02967-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Accepted: 11/06/2023] [Indexed: 11/21/2023]
Abstract
Deep learning has great potential on decoding EEG in brain-computer interface. While common deep learning algorithms cannot directly train models with data from multiple individuals because of the inter-individual differences in EEG. Collecting enough data for each subject to satisfy the training of deep learning would result in an increase in training cost. This study proposes a novel transfer learning, EEGNet-based multi-source domain filter for transfer learning (EEGNet-MDFTL), to reduce the amount of training data and improve the performance of BCI. The EEGNet-MDFTL uses bagging ensemble learning to learn domain-invariant features from the multi-source domain and utilizes model loss value to filter the multi-source domain. Compared with baseline methods, the accuracy of the EEGNet-MDFTL reaches 91.96%, higher than two state-of-the-art methods, which demonstrates source domain filter can select similar source domains to improve the accuracy of the model, and remains a high level even when the data amount is reduced to 1/8, proving that ensemble learning learns enough domain invariant features from the multi-source domain to make the model insensitive to data amount. The proposed EEGNet-MDFTL is effective in improving the decoding performance with a small amount of data, which is helpful to save the BCI training cost.
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Affiliation(s)
- Mengfan Li
- State Key Laboratory of Reliability and Intelligence of Electrical Equipment, School of Health Science and Biomedical Engineering, Hebei University of Technology, Tianjin, China.
- Hebei Key Laboratory of Bioelectromagnetics and Neuroengineering, Tianjin, China.
- Tianjin Key Laboratory of Bioelectromagnetic Technology and Intelligent Health, Tianjin, China.
| | - Jundi Li
- State Key Laboratory of Reliability and Intelligence of Electrical Equipment, School of Health Science and Biomedical Engineering, Hebei University of Technology, Tianjin, China
- Hebei Key Laboratory of Bioelectromagnetics and Neuroengineering, Tianjin, China
- Tianjin Key Laboratory of Bioelectromagnetic Technology and Intelligent Health, Tianjin, China
| | - Zhiyong Song
- State Key Laboratory of Reliability and Intelligence of Electrical Equipment, School of Health Science and Biomedical Engineering, Hebei University of Technology, Tianjin, China
- Hebei Key Laboratory of Bioelectromagnetics and Neuroengineering, Tianjin, China
- Tianjin Key Laboratory of Bioelectromagnetic Technology and Intelligent Health, Tianjin, China
| | - Haodong Deng
- State Key Laboratory of Reliability and Intelligence of Electrical Equipment, School of Health Science and Biomedical Engineering, Hebei University of Technology, Tianjin, China
- Hebei Key Laboratory of Bioelectromagnetics and Neuroengineering, Tianjin, China
- Tianjin Key Laboratory of Bioelectromagnetic Technology and Intelligent Health, Tianjin, China
| | - Jiaming Xu
- Institute of Automation, Chinese Academy of Sciences, Beijing, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China
| | - Guizhi Xu
- State Key Laboratory of Reliability and Intelligence of Electrical Equipment, School of Health Science and Biomedical Engineering, Hebei University of Technology, Tianjin, China
- Hebei Key Laboratory of Bioelectromagnetics and Neuroengineering, Tianjin, China
- Tianjin Key Laboratory of Bioelectromagnetic Technology and Intelligent Health, Tianjin, China
| | - Wenzhe Liao
- School of Artificial Intelligence, Hebei University of Technology, Tianjin, China
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