A mutli-scale spatial-temporal convolutional neural network with contrastive learning for motor imagery EEG classification

Motor imagery (MI) based Brain-computer interfaces (BCIs) have a wide range of applications in the stroke rehabilitation field. However, due to the low signal-to-noise ratio and high cross-subject variation of the electroencephalogram (EEG) signals generated by motor imagery, the classification performance of the existing methods still needs to be improved to meet the need of real practice. To overcome this problem, we propose a multi-scale spatial-temporal convolutional neural network called MSCNet. We introduce the contrastive learning into a multi-temporal convolution scale backbone to further improve the robustness and discrimination of embedding vectors. Experimental results of binary classification show that MSCNet outperforms the state-of-the-art methods, achieving accuracy improvement of 6.04%, 3.98%, and 8.15% on BCIC IV 2a, SMR-BCI, and OpenBMI datasets in subject-dependent manner, respectively. The results show that the contrastive learning method can significantly improve the classification accuracy of motor imagery EEG signals, which provides an important reference for the design of motor imagery classification algorithms.