# EEG-GCNN: Augmenting Electroencephalogram-based Neurological Disease Diagnosis using a Domain-guided Graph Convolutional Neural Network

arXiv: https://arxiv.org/abs/2011.12107 · HTML: https://mlsanity.com/paper/2011.12107/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Nov 2020.

## Built on by (most cited first, 18 of 18)

- [Graph-Based Deep Learning for Medical Diagnosis and Analysis: Past, Present and Future](https://mlsanity.com/paper/2105.13137/) (2021)
- [Graph Neural Networks in IoT: A Survey](https://mlsanity.com/paper/2203.15935/) (2022)
- [Self-Supervised Learning for Anomalous Channel Detection in EEG Graphs: Application to Seizure Analysis](https://mlsanity.com/paper/2208.07448/) (2022)
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- [Graph Adapter of EEG Foundation Models for Parameter Efficient Fine Tuning](https://mlsanity.com/paper/2411.16155/) (2024)
- [GEFM: Graph-Enhanced EEG Foundation Model](https://mlsanity.com/paper/2411.19507/) (2024)
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- [Improving self-supervised pretraining models for epileptic seizure detection from EEG data](https://mlsanity.com/paper/2207.06911/) (2022)
- [A Survey of Spatio-Temporal EEG data Analysis: from Models to Applications](https://mlsanity.com/paper/2410.08224/) (2024)
- [Time CNN and Graph Convolution Network for Epileptic Spike Detection in MEG Data](https://mlsanity.com/paper/2310.09236/) (2023)
- [EEG_GLT-Net: Optimising EEG Graphs for Real-time Motor Imagery Signals Classification](https://mlsanity.com/paper/2404.11075/) (2024)
- [How Much Data is Enough? Optimization of Data Collection for Artifact Detection in EEG Recordings](https://mlsanity.com/paper/2411.11886/) (2024)
- [Scope and Arbitration in Machine Learning Clinical EEG Classification](https://mlsanity.com/paper/2303.06386/) (2023)
- [Window Stacking Meta-Models for Clinical EEG Classification](https://mlsanity.com/paper/2401.10283/) (2024)
- [EEG_RL-Net: Enhancing EEG MI Classification through Reinforcement Learning-Optimised Graph Neural Networks](https://mlsanity.com/paper/2405.00723/) (2024)
- [Bayesian uncertainty-aware deep learning with noisy labels: Tackling annotation ambiguity in EEG seizure detection](https://mlsanity.com/paper/2410.19815/) (2024)
- [EEG-GMACN: Interpretable EEG Graph Mutual Attention Convolutional Network](https://mlsanity.com/paper/2412.17834/) (2024)
- [Graph Convolutional Neural Networks to Model the Brain for Insomnia](https://mlsanity.com/paper/2507.14147/) (2025)

## Abstract (arXiv)

This paper presents a novel graph convolutional neural network (GCNN)-based approach for improving the diagnosis of neurological diseases using scalp-electroencephalograms (EEGs). Although EEG is one of the main tests used for neurological-disease diagnosis, the sensitivity of EEG-based expert visual diagnosis remains at ~50%. This indicates a clear need for advanced methodology to reduce the false negative rate in detecting abnormal scalp-EEGs. In that context, we focus on the problem of distinguishing the abnormal scalp EEGs of patients with neurological diseases, which were originally classified as 'normal' by experts, from the scalp EEGs of healthy individuals. The contributions of this paper are three-fold: 1) we present EEG-GCNN, a novel GCNN model for EEG data that captures both the spatial and functional connectivity between the scalp electrodes, 2) using EEG-GCNN, we perform the first large-scale evaluation of the aforementioned hypothesis, and 3) using two large scalp-EEG databases, we demonstrate that EEG-GCNN significantly outperforms the human baseline and classical machine learning (ML) baselines, with an AUC of 0.90.