A spatial-temporal convolutional neural network-based method is proposed for schizophrenia classification.Unlike the mainstream methods that only analyze the temporal frequency features in EEG and ignore the spatial features between brain regions,the model mainly obtains the spatial-frequency features by convolving the adjacency matrix composed of wavelet coherence coefficients between different channels and EEG sequences,and then extracts the temporal-frequency features through one-dimensional temporal convolution.The processed matrix is flattened after multiple convolutions and input to the classification model.Experimental results show that the method has a classification accuracy of 96.32%on the publicly available dataset Zenodo,demonstrating its effectiveness and exhibiting the advantages of fusing temporal-frequency and spatial-frequency features for schizophrenia diagnosis.
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