TFAGL: A Novel Agent Graph Learning Method Using Time-Frequency EEG for Major Depressive Disorder Detection

Abstract

The abnormality in depression exhibits reciprocal imbalanced connectivity between brain regions rather than increased or decreased activity of one particular area. Current works primarily align the distributions of EEG electrodes with insufficient simulation of neurophysiological structures. Moreover, they neglect significant collaborative relationships among diverse brain regions, which limits the performance of MDD detection. Considering the comprehensive information across brain regions and domains, we propose a novel EEG-based MDD detection model named Time-Frequency Agent Graph Learning (TFAGL), to capture the specific whole-brain level collaborative mechanism of MDD. Specifically, we generate agent nodes adaptively to perform global interactions among regions to sufficiently simulate the function of principal neurons, thereby forming a dynamic local-global connectivity graph to capture connectivity patterns for intra- and inter-regions. Furthermore, interactive learning across different receptive fields through multi-scale graph convolution is applied for each domain and connectivity. Besides, we construct feature extractors for both time and frequency domains and apply intra- and inter-domain constraints to remove redundancy and enhance the discriminability, thus obtaining comprehensive information representations. Extensive experiments on the public EEG MDD detection datasets demonstrate the superiority of TFAGL compared with the state-of-the-art methods.