MSDB-Mam: Dual-Branch Mamba Network With Multi-Scale Features for EEG-Based Depression Detection.

Abstract

Depression is a widespread mental illness, and EEG-based detection offers a non-invasive means to reflect brain activity. However, the inherent complexity of EEG signals across spatial, temporal, and spectral domains poses major challenges for accurate detection, since brain activities vary dynamically over time, differ across brain regions, and are distributed across multiple frequency bands. To this end, we propose a novel Multi-Scale Dual-Branch Mamba network (MSDB-Mam) that efficiently extracts and fuses multi-dimensional EEG features. Specifically, a Multi-Scale Convolution (MSC) module captures diverse patterns along temporal and spatial-temporal axes using different kernel sizes. An Adaptive Reallocation (AR) unit dynamically adjusts feature weights to highlight informative patterns. To capture long-range and cross-domain dependencies, we introduce a Dual-Branch Mamba (DB-Mam) architecture, consisting of a Temporal-Spectral Mamba (TS-Mam) branch for modeling temporal-frequency correlations and a Spatial-Temporal-Spectral Mamba (STS-Mam) branch for learning richer interactions across spatial, temporal, and frequency. The features from both branches are subsequently fused to form a comprehensive and expressive EEG representation. Experiments on MODMA and PRED+CT datasets show that our method achieves 96.58% and 96.66% accuracy, respectively, surpassing existing approaches and demonstrating its effectiveness in EEG-based depression detection.