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This paper presents Conv-VaDE, a variational deep embedding model for interpretable EEG microstate discovery that jointly learns topographic reconstruction and probabilistic soft clustering. It includes a systematic architecture search evaluated on resting-state EEG data to determine optimal model configurations for stability and interpretability.
This paper introduces STDA-Net, a domain adaptation framework for cross-dataset sleep stage classification using 2D spectrograms and adversarial learning. It demonstrates improved accuracy and stability over existing 1D EEG baseline methods on public datasets.
EEG-tGAT is a temporally augmented Graph Attention Network that improves affordance classification from interaction sequences by incorporating temporal attention and dropout mechanisms. The model enhances GATv2 for sequential data where temporal dimensions are semantically non-uniform.