Graph Attention Networks for Detecting Epilepsy from EEG Signals Using Accessible Hardware in Low-Resource Settings
eess.SP, cs.AI, cs.LG, cs.NE
Submitted: 2025-07-20
Updated: 2025-07-20
Journal ref: IEEE Open Journal of Engineering in Medicine and Biology, Volume: 7, 2026
DOI: 10.1109/OJEMB.2025.3642070
Code: https://github.com/alecrimi/eeg
Project page: https://nilearn.github.io
License: http://creativecommons.org/licenses/by/4.0/
The gist: Goal: Epilepsy remains under-diagnosed in low-income countries due to scarce neurologists and costly diagnostic tools.
Terminology
Abstract
Goal: Epilepsy remains under-diagnosed in low-income countries due to scarce neurologists and costly diagnostic tools. We propose a graph-based deep learning framework to detect epilepsy from low-cost Electroencephalography (EEG) hardware, tested on recordings from Nigeria and Guinea-Bissau. Our focus is on fair, accessible automatic assessment and explainability to shed light on epilepsy biomarkers. Methods: We model EEG signals as spatio-temporal graphs, classify them, and identify interchannel relationships and temporal dynamics using graph attention networks (GAT). To emphasize connectivity biomarkers, we adapt the inherently node-focused GAT to analyze edges. We also designed signal preprocessing for low-fidelity recordings and a lightweight GAT architecture trained on Google Colab and deployed on RaspberryPi devices. Results: The approach achieves promising classification performance, outperforming a standard classifier based on random forest and graph convolutional networks in terms of accuracy and robustness over multiple sessions, but also highlighting specific connections in the fronto-temporal region. Conclusions: The results highlight the potential of GATs to provide insightful and scalable diagnostic support for epilepsy in underserved regions, paving the way for affordable and accessible neurodiagnostic tools.
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