A brain topography graph embedded convolutional neural network for EEG-based motor imagery classification

Abstract

Brain-computer interface (BCI) based on motor imagery EEG (MI-EEG) has been used extensively in health care, device control, entertainment, and other fields. MI-EEG signals consist of multiple channels that exhibit a specific topological relationship. However, many existing methods based on deep learning do not make good use of the multi-electrode characteristics of EEG signals. To further improve the decoding performance of MI-EEG, this study proposes a novel method combining the brain topology graph embedding and convolutional neural network (CNN) for MI-EEG classification. Specifically, mutual information is used to measure the relationship between EEG channels. A graph convolutional network (GCN) is employed to embed the topological relationship. Following this, a convolutional block using depth-wise convolution is built to extract spatial–temporal features, while a temporal convolutional network (TCN) is used to extract advanced temporal features. The model’s performance is evaluated on BCICIV-2a and HGD datasets. The experimental results show that the classification accuracy of the proposed model is 80.46% and 94.38%, respectively, outperforming existing methods. Moreover, ablation experiments and feature visualization show that graph embedding can significantly improve the decoding performance of MI-EEG.