Bridging Accuracy and Explainability in EEG-based Graph Attention Network for Depression Detection

TL;DR

This paper introduces ExPANet, a graph attention network that combines EEG features and functional connectivity to accurately and transparently detect depression, outperforming existing methods and providing clinically relevant insights.

Contribution

The study presents a novel EEG-based graph attention network with explainability, integrating feature importance and connectivity analysis for depression detection.

Findings

Superior performance over existing EEG-based methods

Identification of clinically relevant brain features and connectivity patterns

Enhanced interpretability of deep learning in EEG analysis

Abstract

Depression is a major cause of global mental illness and significantly influences suicide rates. Timely and accurate diagnosis is essential for effective intervention. Electroencephalography (EEG) provides a non-invasive and accessible method for examining cerebral activity and identifying disease-associated patterns. We propose a novel graph-based deep learning framework, named Edge-gated, axis-mixed Pooling Attention Network (ExPANet), for differentiating major depressive disorder (MDD) patients from healthy controls (HC). EEG recordings undergo preprocessing to eliminate artifacts and are segmented into short periods of activity. We extract 14 features from each segment, which include time, frequency, fractal, and complexity domains. Electrodes are represented as nodes, whereas edges are determined by the phase-locking value (PLV) to represent functional connectivity. The generated brain graphs are examined utilizing an adapted graph attention network. This architecture acquires both localized electrode characteristics and comprehensive functional connectivity patterns. The proposed framework attains superior performance relative to current EEG-based approaches across two different datasets. A fundamental advantage of our methodology is its explainability. We evaluated the significance of features, channels, and edges, in addition to intrinsic attention weights. These studies highlight features, cerebral areas, and connectivity associations that are especially relevant to MDD, many of which correspond with clinical data. Our findings demonstrate a reliable and transparent method for EEG-based screening of MDD, using deep learning with clinically relevant results.