Frequency-Resolved Connectivity Graphs for EEG-Based Classification of Alzheimer and Frontotemporal Dementia

Abstract

In this study, we build EEG based graph model that uses frequency-resolved connectivity features, with Coherence, phase locking value (PLV) and phase lag index (PLI) in five frequency bands as multi-dimensional edge features. The model is used to classify Alzheimer disease (AD) and frontotemporal dementia (FTD) against healthy controls (CN). This paper extends our earlier work on FTD with AD vs CN as the main new task, a feature ablation for it, and a joint analysis of both tasks. With leave-one-subject-out (LOSO) validation, the model reaches an accuracy of 81.13\% for AD vs CN and 76.06\% for FTD vs CN, which is competitive with the baselines reported with the dataset with far fewer parameters (159 and 282 thousand versus 170.5 million). Under the random cross-validation setting used for the DICE-net model, our model has a comparable accuracy for FTD and a lower accuracy for AD. Node and edge saliency maps highlight occipital and temporal electrodes, the Alpha band for node features and the Theta band for edge features, which is consistent with earlier EEG studies. In an ablation on the AD task, using all 15 edge features performed better than using a single connectivity measure or edge features averaged over the bands.