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

arXiv: https://arxiv.org/abs/2511.05537 · HTML: https://mlsanity.com/paper/2511.05537/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 61 references that are in the map.
- First posted on arXiv 29 Oct 2025.

## Builds on (most cited first, 6 of 6)

- [EEGNet: A Compact Convolutional Network for EEG-based Brain-Computer Interfaces](https://mlsanity.com/paper/1611.08024/) (2016)
- [Self-Supervised Learning for Anomalous Channel Detection in EEG Graphs: Application to Seizure Analysis](https://mlsanity.com/paper/2208.07448/) (2022)
- [Generalizable Sleep Staging via Multi-Level Domain Alignment](https://mlsanity.com/paper/2401.05363/) (2023)
- [SSGCNet: A Sparse Spectra Graph Convolutional Network for Epileptic EEG Signal Classification](https://mlsanity.com/paper/2203.12910/) (2022)
- [Phase Locking Value revisited: teaching new tricks to an old dog](https://mlsanity.com/paper/1710.08037/) (2017)
- [EEG machine learning with Higuchi fractal dimension and Sample Entropy as features for successful detection of depression](https://mlsanity.com/paper/1803.05985/) (2018)

## Abstract (arXiv)

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.