Adaptive Gated Graph Convolutional Network for Explainable Diagnosis of Alzheimer's Disease using EEG Data. (arXiv:2304.05874v2 [q-bio.NC] UPDATED)

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 …

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