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This paper presents a novel graph convolutional neural network (GCNN)-basedapproach for improving the diagnosis of neurological diseases usingscalp-electroencephalograms (EEGs). Although EEG is one of the main tests usedfor neurological-disease diagnosis, the sensitivity of EEG-based expert visualdiagnosis remains at $\sim$50\%. This indicates a clear need for advancedmethodology to reduce the false negative rate in detecting abnormal scalp-EEGs.In that context, we focus on the problem of distinguishing the abnormal scalpEEGs of patients with neurological diseases, which were originally classifiedas 'normal' by experts, from the scalp EEGs of healthy individuals. Thecontributions of this paper are three-fold: 1) we present EEG-GCNN, a novelGCNN model for EEG data that captures both the spatial and functionalconnectivity between the scalp electrodes, 2) using EEG-GCNN, we perform thefirst large-scale evaluation of the aforementioned hypothesis, and 3) using twolarge scalp-EEG databases, we demonstrate that EEG-GCNN significantlyoutperforms the human baseline and classical machine learning (ML) baselines,with an AUC of 0.90.

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