MTSAN-MI: Multiscale Temporal-Spatial Convolutional Self-attention Network for Motor Imagery Classification

EEG signals are widely utilized in brain-computer interfaces, where motor imagery (MI) data plays a crucial role. The effective alignment of MI-based EEG signals for feature extraction, decoding, and classification has always been a significant challenge. Decoding methods based on convolution neural networks often encounter the issue of selecting the optimal receptive field, while convolution in the spatial domain cannot fully utilize the rich spatial topological information contained within EEG signals. In this paper, we propose a multiscale temporal-spatial convolutional self-attention network for motor imagery classification (MTSAN-MI). The proposed model starts with a multiscale temporal-spatial convolution module, in which temporal convolutional layers of varying scales across three different branches can extract corresponding features based on their receptive fields respectively, and graph convolution networks are better equipped to leverage the intrinsic relationships between channels. The multi-head self-attention module is directly connected to capture global dependencies within the temporal-spatial features. Evaluation experiments are conducted on two MI-based EEG datasets, which show that the state-of-the-art is achieved on one dataset, and the result is comparable to the best method on the other dataset. The ablation study also proves the importance of each component of the framework.