SEDAT: a hybrid tokenizer for large EEG models
This study proposes the squeeze-and-excitation (SE)-data-adaptive Gaussian average filtering (DAGAF) adaptive tokenizer (SEDAT), a hybrid framework integrating SE-based spatial aggregation, DAGAF-based signal decomposition, instantaneous-frequency-guided adaptive segmentation, and Fourier-domain resampling into a singl...