  Graph neural network (GNN) models are increasingly being used for the
classification of electroencephalography (EEG) data. However, GNN-based
diagnosis of neurological disorders, such as Alzheimer's disease (AD), remains
a relatively unexplored area of research. Previous studies have relied on
functional connectivity methods to infer brain graph structures and used simple
GNN architectures for the diagnosis of AD. In this work, we propose a novel
adaptive gated graph convolutional network (AGGCN) that can provide explainable
predictions. AGGCN adaptively learns graph structures by combining
convolution-based node feature enhancement with a correlation-based measure of
power spectral density similarity. Furthermore, the gated graph convolution can
dynamically weigh the contribution of various spatial scales. The proposed
model achieves high accuracy in both eyes-closed and eyes-open conditions,
indicating the stability of learned representations. Finally, we demonstrate
that the proposed AGGCN model generates consistent explanations of its
predictions that might be relevant for further study of AD-related alterations
of brain networks.
