Depression EEG classification based on multi-scale convolutional transformer network

Depression electroencephalograph (EEG) classification based on machine learning is helpful for the auxiliary diagnosis of major depression disorder (MDD). Multi-channel EEG has abundant spatial information because EEG electrodes are distributed in different brain regions. However, existing methods arrange EEG features as feature vectors, which destroys the spatial structure of the features and may affect the model's performance. To improve the accuracy of MDD classification, we propose a novel EEG classification method for depression based on the brain topographic map and multi-scale convolutional transformer network (MCTNet). First, the power spectral density (PSD) features are extracted from EEG, and the one-dimensional feature vectors are converted into high-dimensional brain topographic maps according to the location information of EEG channels. Then, a multi-scale convolution with three parallel branches is designed to convert the brain topographic map into a deep feature map representation. Finally, image segmentation (IS) and the transformer encoder (TE) are used to learn the local and global features of the feature map, and the feature is input into the fully connected layer for classification. In addition, a joint loss function based on cross-entropy and center loss (CL) is designed to enable MCTNet to extract features with larger inter-class and smaller intra-class distances. Complete experimental verification is carried out on an open dataset. The accuracy, sensitivity and specificity of MCTNet are 97.24%, 97.20%, and 97.46%, respectively. The results show that the proposed method can achieve high-precision depression EEG classification and is superior to the state-of-the-art models.