Abstract:

In the process of using the deep learning model to classify the EEG signals of depression,aiming at the problem of insufficient feature extraction in single-scale convolution and the limitation of the convolutional neural network in perceiving the global dependence of EEG signals,a multi-scale dynamic convolution network module and the gated transformer encoder module are designed respectively,which are combined with the temporal convolution network,and a hybrid network model MGTTCNet is proposed to classify the EEG signals of patients with depression and healthy controls.First,multi-scale dynamic convolution is used to capture the multi-scale time-frequency information of EEG signals from spatial and frequency domains.Second,the gated transformer encoder is used to learn global dependencies in EEG signals,which effectively enhances the ability of the network to express relevant EEG signal features using the multi-head attention mechanism.Third,the temporal convolution network is used to extract temporal features available for EEG signals.Finally,the extracted abstract features are fed into the classification module for classification.The proposed model is experimentally validated on the public data set MODMA using the Hold-out method and the 10-Fold Cross Validation method,with the classification accuracy being 98.51% and 98.53%,respectively.Compared with the baseline single-scale model EEGNet,the classification accuracy of the proposed model is increased by 1.89% and 1.93%,the F1 value is increased by 2.05% and 2.08%,and the kappa coefficient values are increased by 0.0381 and 0.0385,respectively.Meanwhile,the ablation experiments verify the effectiveness of each module designed in this paper.

Key words: electroencephalography, depression classification, deep learning, Transformer, temporal convolutional networks