  Motor imagery classification is of great significance to humans with mobility
impairments, and how to extract and utilize the effective features from motor
imagery electroencephalogram(EEG) channels has always been the focus of
attention. There are many different methods for the motor imagery
classification, but the limited understanding on human brain requires more
effective methods for extracting the features of EEG data. Graph neural
networks(GNNs) have demonstrated its effectiveness in classifying graph
structures; and the use of GNN provides new possibilities for brain structure
connection feature extraction. In this paper we propose a novel graph neural
network based on the mutual information of the raw EEG channels called
MutualGraphNet. We use the mutual information as the adjacency matrix combined
with the spatial temporal graph convolution network(ST-GCN) could extract the
transition rules of the motor imagery electroencephalogram(EEG) channels data
more effectively. Experiments are conducted on motor imagery EEG data set and
we compare our model with the current state-of-the-art approaches and the
results suggest that MutualGraphNet is robust enough to learn the interpretable
features and outperforms the current state-of-the-art methods.
