Multi-granularity cross-dimensional hypergraph network with domain-partitioned clustering for EEG-based depression detection

Abstract

Major Depressive Disorder (MDD) affects people worldwide, although electroencephalography (EEG) provides an objective neurophysiological basis for depression assessment, existing graph-based EEG frameworks suffer from several limitations. These include single-threshold brain network construction, decoupled spatiotemporal modeling, noise-sensitive population graph formation, and inadequate handling of inter-subject domain heterogeneity. To address these challenges, a Multi-Granularity Domain-Partitioned Hypergraph Spatiotemporal Network (MG-DPHSN), a novel unified end-to-end framework for EEG-based depression detection, is proposed. MG-DPHSN incorporates three complementary modules. First, a Multi-Granularity Masked Relational Learning (MG-MRL) module constructs a hierarchy of Binary Brain Networks across progressively refined threshold levels, preserving weak yet functionally relevant cortical connections. Second, an Attention-Gated 3D Graph Convolution (AG-G3D) module integrates a learnable channel-frequency attention matrix into a unified graph convolution operation, enabling simultaneous aggregation of spatial, temporal, and spectral information. Third, a Hypergraph-Embedded Secondary Subject Partitioning (H-SSP) module employs Maximum Mean Discrepancy (MMD)-based clustering to partition subjects into biologically coherent sub-domains, thereby restricting hypergraph message passing to distributionally similar subjects and mitigating inter-subject domain shift. Experiments conducted on the OpenNeuro (ds003478) demonstrate the effectiveness of the proposed framework. MG-DPHSN achieves 87.50% accuracy and consistently outperforms eight state-of-the-art methods across multiple evaluation metrics.