MPNet aggregates multi-view Riemannian nodes from EEG rhythms via a new manifold node pooling layer to achieve state-of-the-art accuracy with up to 10x faster runtime than comparable models on public datasets.
Eeg conformer: Convolutional transformer for eeg decoding and visualization,
2 Pith papers cite this work. Polarity classification is still indexing.
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StaFlowNet improves MI-EEG decoding by separating and coordinating global state vectors with temporal flow features via a dual-branch design and state-modulated flow module, outperforming prior methods on three public datasets.
citing papers explorer
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MPNet: A Robust and Efficient Manifold Pooling Network for Multi-Rhythm EEG Signal Decoding
MPNet aggregates multi-view Riemannian nodes from EEG rhythms via a new manifold node pooling layer to achieve state-of-the-art accuracy with up to 10x faster runtime than comparable models on public datasets.
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State-Flow Coordinated Representation for MI-EEG Decoding
StaFlowNet improves MI-EEG decoding by separating and coordinating global state vectors with temporal flow features via a dual-branch design and state-modulated flow module, outperforming prior methods on three public datasets.