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Unlabeled physiological signals unlock multimodal EEG foundation model adaptation, matching supervised performance with zero-shot generation.
Compact student models can now achieve near EEG foundation model performance with significantly reduced model size and inference cost, thanks to a novel knowledge distillation framework.