  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 $\sim$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.
