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EEGNet-based multi-source domain filter for BCI transfer learning

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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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Funding

This work was funded by the Natural Science Foundation of Hebei Province (F2021202003), the Technology Nova of Hebei University of Technology (JBKYXX2007), the State Key Laboratory of Reliability and Intelligence of Electrical Equipment (EERI_OY2020004, EERI_OY202000), the National Natural Science Foundation of China (51977060), and the Key Research and Development Foundation of Hebei (19277752D, 21372002D).

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Authors and Affiliations

  1. State Key Laboratory of Reliability and Intelligence of Electrical Equipment, School of Health Science and Biomedical Engineering, Hebei University of Technology, Tianjin, China

    Mengfan Li, Jundi Li, Zhiyong Song, Haodong Deng & Guizhi Xu

  2. Hebei Key Laboratory of Bioelectromagnetics and Neuroengineering, Tianjin, China

    Mengfan Li, Jundi Li, Zhiyong Song, Haodong Deng & Guizhi Xu

  3. Tianjin Key Laboratory of Bioelectromagnetic Technology and Intelligent Health, Tianjin, China

    Mengfan Li, Jundi Li, Zhiyong Song, Haodong Deng & Guizhi Xu

  4. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Jiaming Xu

  5. School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, China

    Jiaming Xu

  6. School of Artificial Intelligence, Hebei University of Technology, Tianjin, China

    Wenzhe Liao

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  1. Mengfan Li
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Correspondence to Mengfan Li.

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Li, M., Li, J., Song, Z. et al. EEGNet-based multi-source domain filter for BCI transfer learning. Med Biol Eng Comput 62, 675–686 (2024). https://doi.org/10.1007/s11517-023-02967-z

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