A Relevant Prototype Domain Gradient Projection Continual Learning Method for Cross-Subject P300 Brain-Computer Interfaces
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-97-5591-2_34
Reference20 articles.
1. Lee, P.-L., Chen, S.-H., Chang, T.-C., et al.: Continual learning of a transformer-based deep learning classifier using an initial model from action observation EEG data to online motor imagery classification. Bioengineering 10(2), 186 (2023)
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3. Chen, N.C., Li, J., Lu, X.: Multiscale entropy-based analysis and processing of EEG signal during watching 3DTV. Measurement 125, 432–437 (2018)
4. Gao, W., Yu, T., Yu, J.G.: Learning invariant patterns based on a convolutional neural network and big electroencephalography data for subject-independent P300 brain-computer interfaces. IEEE Trans. Neural Syst. Rehabil. Eng. 29, 1047–1057 (2021)
5. Wei Gao, Weichen Huang, Man Li, et al.: Eliminating or shortening the calibration for a P300 brain-computer interface based on a convolutional neural network and big electroencephalography data: an online study. IEEE Trans. Neural Syst. Rehabil. Eng. 31, 1754–1763 (2023). https://doi.org/10.1109/TNSRE.2023.3259991
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