MDD-SSTNet: detecting major depressive disorder by exploring spectral-spatial-temporal information on resting-state electroencephalography data based on deep neural network

Major depressive disorder (MDD) is a psychiatric disorder characterized by persistent lethargy that can lead to suicide in severe cases. Hence, timely and accurate diagnosis and treatment are crucial. Previous neuroscience studies have demonstrated that major depressive disorder subjects exhibit topological brain network changes and different temporal electroencephalography (EEG) characteristics compared to healthy controls. Based on these phenomena, we proposed a novel model, termed as MDD-SSTNet, for detecting major depressive disorder by exploring spectral-spatial-temporal information from resting-state EEG with deep convolutional neural network. Firstly, MDD-SSTNet used the Sinc filter to obtain specific frequency band features from pre-processed EEG data. Secondly, two parallel branches were used to extract temporal and spatial features through convolution and other operations. Finally, the model was trained with a combined loss function of center loss and Binary Cross-Entropy Loss. Using leave-one-subject-out cross-validation on the HUSM dataset and MODMA dataset, the MDD-SSTNet model outperformed six baseline models, achieving average classification accuracies of 93.85% and 65.08%, respectively. These results indicate that MDD-SSTNet could effectively mine spatial-temporal difference information between major depressive disorder subjects and healthy control subjects, and it holds promise to provide an efficient approach for MDD detection with EEG data.