Frequency-Gated Prompting for Enhancing Transformer-based EEG Decoding

This is the authors' abstract. We don't add key points for this paper.

The fundamental problem in electroencephalogram (EEG) decoding centers on the extraction of meaningful neural patterns from complex spatio-temporal signals. Although Transformer models have garnered significant attention in this domain due to their exceptional temporal modeling capabilities, their lack of perception for critical frequency-domain features within EEG signals constrains further performance enhancement. To address this issue, this paper proposes a lightweight approach termed frequency-gated prompted Transformer (FGPT). FGPT adaptively represents global EEG rhythms by introducing learnable sparse frequency prompt tokens. Utilizing a gated fusion mechanism, it synergistically embeds these tokens with the original EEG sequence into the Transformer's self-attention computation. This enables joint modeling of spatio-temporal and frequency-domain features without compromising sequence continuity. Experiments conducted on three public EEG datasets show that FGPT improves the decoding performance and robustness of the evaluated Transformer-based models (EEG-ViT, EEG-Conformer, and EEG-Deformer) on the selected baselines. With its lightweight design and observed potential for generalization on the datasets used, this approach explores a prompt learning method that may contribute to developing more efficient EEG decoding systems.