Major Depressive Disorder Detection Using Graph Domain Adaptation With Global Message-Passing Based on EEG Signals

Abstract

Electroencephalography (EEG) has been widely used for the detection of major depressive disorder (MDD). Currently, several methods have been proposed to process EEG signals for MDD detection using deep learning algorithms, and some advantages have been achieved. However, the extraction of EEG features for MDD detection remains challenging and most methods have difficulty in extracting common features of EEG signals across subjects. We propose a graph domain adaptation with global message-passing (GMP-GDA) for MDD detection. In addition, an adjacency matrix weighting algorithm is designed in the global message-passing module to learn the weight combinations of different adjacency matrices instead of feature transformation matrices to achieve cross-domain global message-passing between multi-source and target domains. Experimental results demonstrate that the proposed method achieves the superior detection result in each frequency band compared to the baseline systems. Meanwhile, ablation experiments demonstrate the effectiveness of our proposed method.