LSDD-EEGNet: An efficient end-to-end framework for EEG-based depression detection

Depression is a mood disorder that causes negative effects on people's life and has become a leading health burden worldwide. But the effective and low-cost detection for depression is still a great challenge. Electroencephalogram (EEG) measures the brain activities and can be used for depression-related research. In this paper, we propose an effective end-to-end framework named LSDD-EEGNet for EEG-based depression detection. Specially, LSDD-EEGNet has two distinguishing characteristics for depression recognition: (1) Considering the superiority of convolution neural network (CNN) on feature extraction and the efficiency of long-short term memory (LSTM) for time-series signals, we combine both as the extractor for LSDD-EEGNet. (2) We apply the domain discriminator to modify the data representation space and eliminate the discrepancy between training and test dataset. In addition, we collected EEG signals from 40 depressed patients (DPs) and 40 healthy controls (HCs) to evaluate the performance of the proposed deep framework for depression detection. Compared to other typical machine learning (ML) methods and deep learning (DL) models, LSDD-EEGNet achieves superior performance on subject-independent evaluation that shows the LSDD-EEGNet can be a promising detection method for depression.