Enhancing Generalized EEG Classification with Decomposed Statistics-diverse Feature Augmentation

Learning a generalized EEG representation under limited data and subject variability is a long-standing challenge. Most studies utilized data augmentation to extend the distribution of training data, which may hinder the diversity of augmented samples to cover more subject variability. In this paper, we propose a decomposed statistics-diverse feature augmentation (DSFA) framework for generalized EEG learning. The wavelet-based decomposed representation module decomposes signals into approximation and detail features, thus deriving semantics from original signals into approximation features to avoid over-transformation. The statistics-diverse feature augmentation module augments features to extend beyond the original feature space by manipulating the statistics of features. Extensive experiments on three datasets demonstrate that our approach achieves state-of-the-art performance in different classification tasks. Our repository is public at https://github.com/1940653868/DSFA.