Spectral features from frequency and time-frequency domains enable traditional ML to match or exceed attention-based DL performance in EEG disease diagnosis on small datasets, with attention unable to capture stable spectral signatures.
Selective cross-subject transfer learning based on riemannian tangent space for motor imagery brain-computer interface
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Spectral Priors vs. Attention: Investigating the Utility of Attention Mechanisms in EEG-Based Diagnosis
Spectral features from frequency and time-frequency domains enable traditional ML to match or exceed attention-based DL performance in EEG disease diagnosis on small datasets, with attention unable to capture stable spectral signatures.