Chinese Imagined Speech EEG Classification Method Based on Topological and Frequency-Band Priors

Abstract

EEG-based imagined speech classification is an important topic in brain–computer interface research. However, Chinese imagined speech EEG sig-nals are typically characterized by low signal-to-noise ratio, strong non-stationarity, and subtle inter-class differences, which make stable modeling challenging. Existing convolutional methods are limited in capturing long-range dependencies, while Transformer-based models often fail to fully ex-ploit spatial topology and frequency-band information. To address these is-sues, this paper proposes a topology- and frequency-band-prior-guided con-volution–Transformer hybrid model, named TFP-CTNet. The model incorpo-rates a channel topology-aware embedding at the input stage to preserve elec-trode spatial relationships. A multi-scale convolutional module is then used to enhance local discriminative representations, and a frequency-band-aware gated residual MLP is introduced at the classification stage to selectively re-fine high-level features. Experiments on the Chisco dataset show that the proposed method consistently outperforms several competitive baselines in terms of accuracy and F1-score, while also achieving improved cross-subject consistency. These results demonstrate that incorporating structural and fre-quency-domain priors is beneficial for robust Chinese imagined speech EEG classification..