To address the challenges of extracting nonlinear features from motor imagery electroencephalogram(EEG)signals and effectively capturing functional connectivity between EEG channels,a classification and recognition method for motor imagery EEG signals is proposed based on mutual information and adaptive graph convolutional network.The proposed method extracts frequency domain information by sub-frequency banding on the original motor imagery EEG signals,uncovers the nonlinear relationships within EEG signals by an adjacency matrix constructed with mutual information neural estimation method,and finally achieve null-frequency feature extraction by capturing the dynamic correlation strength between channels with an adaptive graph convolutional network incorporating convolutional block attention module.On the BCI Competition Ⅳ 2a and BCI Competition Ⅲ 3a datasets,the proposed method has average accuracies of 83.14％and 88.19％,respectively,demonstrating that it can effectively reveal functional connectivity between EEG channels,providing a new approach for decoding motor imagery EEG signals.

To address the challenges of extracting nonlinear features from motor imagery electroencephalogram(EEG)signals and effectively capturing functional connectivity between EEG channels,a classification and recognition method for motor imagery EEG signals is proposed based on mutual information and adaptive graph convolutional network.The proposed method extracts frequency domain information by sub-frequency banding on the original motor imagery EEG signals,uncovers the nonlinear relationships within EEG signals by an adjacency matrix constructed with mutual information neural estimation method,and finally achieve null-frequency feature extraction by capturing the dynamic correlation strength between channels with an adaptive graph convolutional network incorporating convolutional block attention module.On the BCI Competition Ⅳ 2a and BCI Competition Ⅲ 3a datasets,the proposed method has average accuracies of 83.14％and 88.19％,respectively,demonstrating that it can effectively reveal functional connectivity between EEG channels,providing a new approach for decoding motor imagery EEG signals.