Unsupervised Adaptive Deep Learning Method For BCI Motor Imagery Decoding

• URL: http://arxiv.org/abs/2403.15438v1
• Date: Fri, 15 Mar 2024 22:22:10 GMT
• Title: Unsupervised Adaptive Deep Learning Method For BCI Motor Imagery Decoding
• Authors: Yassine El Ouahidi, Giulia Lioi, Nicolas Farrugia, Bastien Pasdeloup, Vincent Gripon,
• Abstract summary: We propose an adaptive method that reaches offline performance level while being usable online without requiring supervision. We demonstrate its efficiency for Motor Imagery brain decoding from electroencephalography data, considering challenging cross-subject scenarios.
• Score: 2.0039413639026917
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: In the context of Brain-Computer Interfaces, we propose an adaptive method that reaches offline performance level while being usable online without requiring supervision. Interestingly, our method does not require retraining the model, as it consists in using a frozen efficient deep learning backbone while continuously realigning data, both at input and latent spaces, based on streaming observations. We demonstrate its efficiency for Motor Imagery brain decoding from electroencephalography data, considering challenging cross-subject scenarios. For reproducibility, we share the code of our experiments.

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