Depression signal correlation identification from different EEG channels based on CNN feature extraction

•This paper proposes a convolutional neural network (CNN) based correlation recognition method for depression signals.•The method uses labeled multi-channel Eeg signals as data.•Eeg signals of each channel were divided into neural network training data sets, and AlexNet network was used to train these data. Then, the depression channels were classified according to the training samples.•The accuracy function and loss function are used to evaluate the classification index.