A Multi-stream Deep Learning Model for EEG-based Depression Identification

Major depressive disorder (MDD) has been widely studied because it is one of the most common and severe mental health issues. EEG-based depression identification is receiving a lot of attention in research on mental health care. This paper presents a new deep learning method for depression identification that employs deep learning algorithms with functional connectivity graphs in different states. The features commonly used to analyze the depression EEG, differential entropy (DE), and power spectral density (PSD) are extracted. Then, the adjacency matrix of the EEG signals is constructed using the Pearson correlation coefficient (PCC), phase locking value (PLV), and phase lag index (PLI) matrices between EEG signal pairs. For the classifier, an ovel multi-network architecture consisting of spatial-temporal graph convolution networks with brain graphs based on three functional connectivity measurement methods is employed to improve the learning ability of spatio-temporal features. Compared with other methods, our method has the best accuracy of 99.03%. The combination of the functional connectivity matrices of brain networks associated with multi-stream ST-GCN can predict the occurrence of depression and assist in diagnosing depression in an early stage.