A zero-shot machine learning decoder for handwriting BCIs achieves 64% hits@3 retrieval on unseen letters by exploiting conserved kinematic neural representations.
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DANCE frames EEG event identification as a set-prediction problem to jointly detect and classify events directly from raw, unaligned signals, outperforming existing methods on seizure monitoring and matching onset-informed models on BCI tasks across ten datasets.
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Conserved Kinematic Representations enable Zero-Shot Decoding in Handwriting BCIs
A zero-shot machine learning decoder for handwriting BCIs achieves 64% hits@3 retrieval on unseen letters by exploiting conserved kinematic neural representations.
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DANCE: Detect and Classify Events in EEG
DANCE frames EEG event identification as a set-prediction problem to jointly detect and classify events directly from raw, unaligned signals, outperforming existing methods on seizure monitoring and matching onset-informed models on BCI tasks across ten datasets.