Knowledge Commons of Institute of Automation,CAS
EEGNet-based multi-source domain filter for BCI transfer learning | |
Li, Mengfan1,2,3; Li, Jundi1,2,3; Song, Zhiyong1,2,3; Deng, Haodong1,2,3; Xu, Jiaming4,5; Xu, Guizhi1,2,3; Liao, Wenzhe6 | |
发表期刊 | MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING |
ISSN | 0140-0118 |
2023-11-20 | |
页码 | 12 |
通讯作者 | Li, Mengfan(mfli@hebut.edu.cn) |
摘要 | 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. |
关键词 | Brain-computer interface Multi-source domain filter Transfer learning Ensemble learning EEGNet |
DOI | 10.1007/s11517-023-02967-z |
关键词[WOS] | NEURAL-NETWORK ; SYSTEM |
收录类别 | SCI |
语种 | 英语 |
资助项目 | Natural Science Foundation of Hebei Province[F2021202003] ; Technology Nova of Hebei University of Technology[JBKYXX2007] ; State Key Laboratory of Reliability and Intelligence of Electrical Equipment[EERI_OY2020004] ; State Key Laboratory of Reliability and Intelligence of Electrical Equipment[EERI_OY202000] ; National Natural Science Foundation of China[51977060] ; Key Research and Development Foundation of Hebei[19277752D] ; Key Research and Development Foundation of Hebei[21372002D] |
项目资助者 | Natural Science Foundation of Hebei Province ; Technology Nova of Hebei University of Technology ; State Key Laboratory of Reliability and Intelligence of Electrical Equipment ; National Natural Science Foundation of China ; Key Research and Development Foundation of Hebei |
WOS研究方向 | Computer Science ; Engineering ; Mathematical & Computational Biology ; Medical Informatics |
WOS类目 | Computer Science, Interdisciplinary Applications ; Engineering, Biomedical ; Mathematical & Computational Biology ; Medical Informatics |
WOS记录号 | WOS:001104250600001 |
出版者 | SPRINGER HEIDELBERG |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/54977 |
专题 | 复杂系统认知与决策实验室_听觉模型与认知计算 |
通讯作者 | Li, Mengfan |
作者单位 | 1.Hebei Univ Technol, Sch Elect Engn, State Key Lab Reliabil & Intelligence Elect Equip, Tianjin 300401, Peoples R China 2.Hebei Key Lab Bioelectromagnet & Neuroengn, Tianjin 300132, Peoples R China 3.Hebei Univ Technol, Tianjin Key Lab Bioelect & Intelligent Hlth, Tianjin 300130, Peoples R China 4.Chinese Acad Sci, Inst Automat, Beijing, Peoples R China 5.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China 6.Hebei Univ Technol, Sch Artificial Intelligence, Tianjin, Peoples R China |
推荐引用方式 GB/T 7714 | Li, Mengfan,Li, Jundi,Song, Zhiyong,et al. EEGNet-based multi-source domain filter for BCI transfer learning[J]. MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING,2023:12. |
APA | Li, Mengfan.,Li, Jundi.,Song, Zhiyong.,Deng, Haodong.,Xu, Jiaming.,...&Liao, Wenzhe.(2023).EEGNet-based multi-source domain filter for BCI transfer learning.MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING,12. |
MLA | Li, Mengfan,et al."EEGNet-based multi-source domain filter for BCI transfer learning".MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING (2023):12. |
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