Convolutional Retentive Network for EEG Decoding

Abstract

The self-attention mechanism of Transformer has gained considerable attention for its potential in modeling long-term temporal dependencies in electroencephalogram (EEG) signals. Despite recent advancements, Transformer-based decoding methods often neglect the explicit temporal priors inherent in EEG signals, i.e., the dependency between tokens tends to diminish as their relative temporal distance increases, which limits the efficacy of these decoding approaches. Inspired by the recent Retentive Network (RetNet), we develop a novel convolutional retentive network for EEG decoding (RetEEG), which integrates temporal priors into the self-attention mechanism to tackle the above challenge. Specifically, RetEEG incorporates a convolution module to capture local EEG features and cascades multiple bidirectional retention modules to learn global correlations within these local features. The bidirectional retention module introduces a temporal decay matrix, which imparts prior knowledge to the self-attention mechanism by accounting for variations in relative temporal distances. To validate the superiority of RetEEG, we conducted experiments on two publicly available EEG datasets. The experimental results empirically demonstrate that the proposed RetEEG achieves the state-of-the-art EEG decoding performance. Code is available at: https://github.com/kfhss/RetEEG.