A cross-session non-stationary attention-based motor imagery classification method with critic-free domain adaptation

Recent studies increasingly employ deep learning to decode electroencephalogram (EEG) signals. While deep learning has improved the performance of motor imagery (MI) classification to some extent, challenges remain due to significant variances in EEG data across sessions and the limitations of convolutional neural networks (CNNs). EEG signals are inherently non-stationary, traditional multi-head attention typically uses normalization methods to reduce non-stationarity and improve performance. However, non-stationary factors are crucial inherent properties of EEG signals and provide valuable guidance for decoding temporal dependencies in EEG signals. In this paper, we introduce a novel CNN combined with the Non-stationary Attention (NSA) and Critic-free Domain Adaptation Network (NSDANet), tailored for decoding MI signals. This network starts with temporal–spatial convolution devised to extract spatial–temporal features from EEG signals. It then obtains multi-modal information from average and variance perspectives. We devise a new self-attention module, the Non-stationary Attention (NSA), to capture the non-stationary temporal dependencies of MI-EEG signals. Furthermore, to align feature distributions between the source and target domains, we propose a critic-free domain adaptation network that uses the Nuclear-norm Wasserstein discrepancy (NWD) to minimize the inter-domain differences. NWD complements the original classifier by acting as a critic without a gradient penalty. This integration leverages discriminative information for feature alignment, thus enhancing EEG decoding performance. We conducted extensive cross-session experiments on both BCIC IV 2a and BCIC IV 2b dataset. Results demonstrate that the proposed method outperforms some existing approaches.