Frequency bands EEG Biomarkers for Dementia using Graph Neural Networks

Abstract

We introduce a simple and interpretable model for classification of electroencephalography (EEG) signals. Our focus essentially is on using deep learning to study how connectivity patterns that are integrated to classify the EEG signals and highlight the important discriminative features used by the model in predictions. In this study, we utilize the connectivity features across different frequency bands in multi edge Graph Neural Networks (GNN) and showed that edge features are complimentary. We use a simple GNN model to predict Frontotemporal Dementia (FTD) in EEG. Our model is capable of achieving average accuracy of approximately 76% using Leave-One-Subject-Out-subject for FTD predictions which are better than the baselines and comparable to State of the arts models. In this article, we study the importance of the connectivity edges, nodes and frequency bands in the prediction of the model, focusing in explainable AI methods through saliency maps to interpret the model both locally and globally. The Saliency maps highlight the importance of Occipital and anterior temporal regions in the prediction of FTD. Furthermore, our results highlight the importance of Alpha and Theta bands in the prediction of FTD. Our observations align with previous research done using classical statistical methods. We argue that there are complimentary information in each each connectivity feature and frequency band brain networks. The impacts of each connectivity metrics on the prediction of the model are quantified to highlight the complimentary information in each connectivity measure.