Constructs hypergraphs from caCOH multivariate connectivity for EEG/MEG, recovering simulated coupling frequencies better than MSC graphs with reduction from 610 edges to 10 or 1 hyperedges.
Where does EEG come from and what does it mean?,
2 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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2026 2verdicts
UNVERDICTED 2representative citing papers
nASR is an end-to-end trainable Keras layer for channel-level EEG artifact subspace reconstruction that outperforms traditional ASR with 6-8x faster inference on BCI Competition IV data.
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Hypergraphs from multivariate connectivity: caCOH-based EEG/MEG representation
Constructs hypergraphs from caCOH multivariate connectivity for EEG/MEG, recovering simulated coupling frequencies better than MSC graphs with reduction from 610 edges to 10 or 1 hyperedges.
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nASR: An End-to-End Trainable Neural Layer for Channel-Level EEG Artifact Subspace Reconstruction in Real-Time BCI
nASR is an end-to-end trainable Keras layer for channel-level EEG artifact subspace reconstruction that outperforms traditional ASR with 6-8x faster inference on BCI Competition IV data.