MST combines Morlet wavelet tokenization, long-context baseline removal, and frequency-specific spatial projections with a Transformer backbone to outperform pretrained EEG models on SEED datasets for cross-subject emotion decoding.
Fbcnet: An efficient multi-view convolutional neural network for brain-computer interface.arXiv preprint arXiv:2104.01233
4 Pith papers cite this work. Polarity classification is still indexing.
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NAKUL achieves 91.7% accuracy on motor imagery EEG with 28% fewer parameters than EEG-Conformer by using dynamic kernel generation, spectral context modeling, and graph-guided spatial attention.
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.
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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Dive into Waves: Morlet Spectral Transformer for Cross-Subject Emotion Decoding from EEG
MST combines Morlet wavelet tokenization, long-context baseline removal, and frequency-specific spatial projections with a Transformer backbone to outperform pretrained EEG models on SEED datasets for cross-subject emotion decoding.
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NAKUL-Med: Spectral-Graph State Space Models with Dynamics Kernels for Medical Signals
NAKUL achieves 91.7% accuracy on motor imagery EEG with 28% fewer parameters than EEG-Conformer by using dynamic kernel generation, spectral context modeling, and graph-guided spatial attention.
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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.