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.
Subject-aware contrastive learning for biosignals
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PhysioLite delivers Transformer-comparable ECG/EMG performance using learnable wavelet filters and hardware-aware design at ~370KB quantized size on μNPUs.
Microstate tokenizer from clustered EEG signals provides universal representations that outperform traditional time- and frequency-domain features across sleep staging, emotion recognition, and motor imagery tasks.
citing papers explorer
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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.
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Towards Real-Time ECG and EMG Modeling on $\mu$NPUs
PhysioLite delivers Transformer-comparable ECG/EMG performance using learnable wavelet filters and hardware-aware design at ~370KB quantized size on μNPUs.
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Atoms of Thought: Universal EEG Representation Learning with Microstates
Microstate tokenizer from clustered EEG signals provides universal representations that outperform traditional time- and frequency-domain features across sleep staging, emotion recognition, and motor imagery tasks.