Towards Explainable Graph Neural Networks for Neurological Evaluation on EEG Signals

TL;DR

This paper introduces a graph neural network approach to predict stroke severity from EEG data, revealing brain reorganization patterns and aiding clinical decision-making.

Contribution

It presents a novel GNN-based method for stroke severity prediction using EEG-derived brain connectivity graphs, enhancing interpretability and clinical relevance.

Findings

GNN accurately predicts NIH Stroke Scale scores from EEG data.

Attention coefficients reveal key brain reorganization patterns post-stroke.

Method demonstrates potential for personalized neurorehabilitation planning.

Abstract

After an acute stroke, accurately estimating stroke severity is crucial for healthcare professionals to effectively manage patient's treatment. Graph theory methods have shown that brain connectivity undergoes frequency-dependent reorganization post-stroke, adapting to new conditions. Traditional methods often rely on handcrafted features that may not capture the complexities of clinical phenomena. In this study, we propose a novel approach using Graph Neural Networks (GNNs) to predict stroke severity, as measured by the NIH Stroke Scale (NIHSS). We analyzed electroencephalography (EEG) recordings from 71 patients at the time of hospitalization. For each patient, we generated five graphs weighted by Lagged Linear Coherence (LLC) between signals from distinct Brodmann Areas, covering $\delta$ (2-4 Hz), $\theta$ (4-8 Hz), $\alpha_1$ (8-10.5 Hz), $\alpha_2$ (10.5-13 Hz), and $\beta_1$ (13-20 Hz) frequency bands. To emphasize key neurological connections and maintain sparsity, we applied a sparsification process based on structural and functional brain network properties. We then trained a graph attention model to predict the NIHSS. By examining its attention coefficients, our model reveals insights into brain reconfiguration, providing clinicians with a valuable tool for diagnosis, personalized treatment, and early intervention in neurorehabilitation.