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arXiv · 2403.15438

Unsupervised Adaptive Deep Learning Method For BCI Motor Imagery Decoding

Yassine El Ouahidi·Giulia Lioi·Nicolas Farrugia·Bastien Pasdeloup·Vincent Gripon
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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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Yassine El Ouahidi, Giulia Lioi, Nicolas Farrugia, Bastien Pasdeloup, Vincent Gripon. 2024-03-15. Unsupervised Adaptive Deep Learning Method For BCI Motor Imagery Decoding. https://arxiv.org/abs/2403.15438

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