FUSE: FUsing EEG-MEG in a Shared Embedding via self-supervised learning for BCI
Combining complementary neurophysiological modalities offers a promising strategy for improving motor imagery (MI) brain-computer interfaces (BCIs), but learning shared representations across modalities remains largely unexplored. Here, we propose a two-phase deep learning framework for multimodal EEG-MEG decoding that explicitly decouples representation learning from downstream classification.…
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