Classification of EEG signals in depression by fusing temporal convolution and feature recalibration

Aiming at the problem of insufficient feature extraction in single scale neural network model and the problem that convolutional neural network cannot process sequential tasks in the classification of EEG signals in depression, a hybrid model (BFTCNet) of dual-branch convolutional neural network (Bi_CNN) and temporal convolutional network (TCN) based on feature recalibration (FR) was proposed to classify EEG signals of depressed patients and healthy controls. Firstly, Bi_CNN module was used to extract the mixed EEG features between different frequency bands and different channels. Secondly, FR module was used to enhance the features extracted by Bi_CNN. Finally, TCN with dilated causal convolution was used for the sequence learning to capture the temporal dependency between features. In this study, 128 EEG channels of resting-state (closed-eye) EEG data from the public dataset MODMA were used as experimental data, including 29 healthy controls and 24 depression patients. The performance of the model was evaluated by the 10-fold cross validation method. The proposed BFTCNet achieves a classification accuracy of 95.98%, F1 score value of 95.47%, sensitivity and specificity of 94.21% and 97.50%, respectively. Compared with the single-scale network model EEGNet-8,2, the classification accuracy and F1 value are improved by 1.5% and 1.48%, respectively. Meanwhile, the ablation experiment proved that each sub-module had its contribution to the improvement of the model’s classification ability.