A spatio-temporal attention convolutional network with topology-preserving channel embedding and transformer for EEG...

EEG-based BCI has shown promising applications in motor rehabilitation and emotion recognition. Given the non-stationarity and variability of EEG signals, it is essential to develop classification models that can effectively extract spatio-temporal features while maintaining strong generalization capabilities. This paper proposes a spatio-temporal attention convolutional network for EEG decoding (E-STACNet) to encode the topological features and temporal characteristics of EEG signals within a unified network framework. It consists of channel-attention (CA) block, convolutional (CV) layers, multi-head self-attention (MSA) and classifier layer. Specifically, CA block encodes spatial topological feature via a topology-preserving channel embedding method. Then convolutional layers are employed to encode low-level temporal information and further extract spatial features, followed by MSA block to highlight the most important temporal information of EEG signals. Finally, the outputs are obtained through two fully-connected layers. We conducted experiments on three public EEG-based datasets including BCI Competition IV dataset 2a, Giga DB and DEAP. The results demonstrate E-STACNet achieves average accuracies of 88.00% and 80.62% on datasets 2a and Giga DB, respectively. On the DEAP, it reaches average accuracies of 96.49% and 96.46% for valence and arousal. The proposed model outperforms other state-of-the-art models on all three datasets. Additionally, the ablation study reveals the importance of topology-preserving channel embedding for the proposed network. Visualization analysis shows E-STACNet could obtain the importance of channels at different locations through the channel-attention mechanism, which demonstrates the interpretability of the proposed model. Overall, the accurate and generalization capabilities of E-STACNet will promote the development of EEG-based BCIs.