M-MDD: A multi-task deep learning framework for major depressive disorder diagnosis using EEG

Abstract

Major depressive disorder (MDD) is a common and destructive psychiatric disorder worldwide. Traditional MDD diagnosis relies heavily on subjective observation and questionnaires. Recently, a non-invasive method of recording the brain’s spontaneous activity called Electroencephalogram (EEG) has been a useful tool of MDD diagnosis. However, there are still some challenges to be addressed: (1) The model’s robustness to common EEG noise has to be improved, (2) The temporal, spectral and spatial features of EEG need to be extracted and fused appropriately. Learning both robust and powerful features for MDD diagnosis can improve the overall performance, and multi-task learning is a powerful solution. In this paper, we propose M-MDD, a multi-task deep learning framework for MDD diagnosis using EEG. First, we design the Contrastive Noise Robustness Task to learn noise-independent features. Then, we design the Supervised Feature Extraction Task to extract temporal, spectral and spatial features of EEG respectively, and then effectively combine them together. Finally, the above two modules share the same feature space and are trained jointly with the Multi-task Learning Module, improving the overall performance. Validated on two public MDD diagnosis datasets with subject-independent cross-validation, our model achieves the state-of-the-art performance.