Abstract: At present,the mainstream diagnosis of depression is through the communication between doctors and patients,filling in the relevant questionnaire,which needs corresponding clinical knowledge and is subjective.It brings a lot of challenges to the diagnosis of depression.Using information processing technology to analyze physiological signals and build an accurate and objective auxiliary diagnosis model is of great value.However,the sample size of the public data set of depression auxiliary diagnosis is generally small,which makes the accuracy of auxiliary diagnosis is generally low.On this basis,this paper proposes a graph neural network (GNN) method for depression recognition based on data augmentation and model ensemble strategy.The method uses 128 channel EEG signals of 53 subjects and segments the collected EEG data.After data augmentation,Pearson correlation coefficient is used to calculate the correlation between different channels to construct a brain network,graph neural network is used to learn the features of brain network,and the final prediction results are obtained by majority voting with model ensemble strategy.Experimental results show that the proposed method improves the classification ability of the network and solves the problem of poor classification performance caused by small sample size.The proposed method achieves 77% classification accuracy on the MODMA data set(including 24 patients with depression and 29 normal controls) provided by the Pervasive Sensing and Intelligent Systems Laboratory of Lanzhou University.The classification accuracy of the proposed method is significantly improved compared to other methods.

Key words: Classification, Data augmentation, Depression recognition, EEG, Graph neural network, Model ensemble