Abstract: Depression is a serious mental disorder that negatively affects the patient’s quality of life and social functioning. In order to explore an electroencephalogram-based classification method for depression to improve the accuracy of early diagnosis of depression， this paper designes a deep learning model called MDATCNet， which exploits a multi-scale dynamic convolution module capturing the rich features of signals in both spatial and frequency dimensions. To further enhance the representation of the model， this paper integrates the multi-head self-attention mechanism， which allows the model to adaptively focus on the features that are most helpful for decision-making. Then， the time convolutional layer is responsible for mining the time series patterns in the time series data. Finally， the features are passed to a Softmax classifier to classify EEG signals. The feasibility of the model is evaluated on the public depression dataset using the ten-fold cross-validation method， and the recognition accuracy， sensitivity and specificity of the method based on MDATCNet in EEG can achieve 94.71%， 99.37%， and 90.34%， respectively， and the experimental results show that the proposed model can effectively help the early diagnosis of depression.

Key words: electroencephalogram, depression, deep learning, convolutional neural network, attention mechanisms