  Parkinson's disease (PD) is a debilitating neurodegenerative disease that has
severe impacts on an individual's quality of life. Compared with structural and
functional MRI-based biomarkers for the disease, electroencephalography (EEG)
can provide more accessible alternatives for clinical insights. While deep
learning (DL) techniques have provided excellent outcomes, many techniques fail
to model spatial information and dynamic brain connectivity, and face
challenges in robust feature learning, limited data sizes, and poor
explainability. To address these issues, we proposed a novel graph neural
network (GNN) technique for explainable PD detection using resting state EEG.
Specifically, we employ structured global convolutions with contrastive
learning to better model complex features with limited data, a novel multi-head
graph structure learner to capture the non-Euclidean structure of EEG data, and
a head-wise gradient-weighted graph attention explainer to offer neural
connectivity insights. We developed and evaluated our method using the UC San
Diego Parkinson's disease EEG dataset, and achieved 69.40% detection accuracy
in subject-wise leave-one-out cross-validation while generating intuitive
explanations for the learnt graph topology.
