EEG-Based Classification of Alzheimer’s Disease and Frontotemporal Dementia via a Dynamic Threshold Graph Convolutional Network

Accurate differentiation of Alzheimer’s disease (AD) and frontotemporal dementia (FTD) is clinically important because their management strategies differ. This study aimed to develop and evaluate a Dynamic Threshold Graph Convolutional Network (DT-GCN) and to systematically compare four EEG-based functional connectivity (FC) measures—Pearson correlation, phase-locking value (PLV), Granger causality, and copula analysis—for classifying AD, FTD, and healthy controls (HCs). In contrast to fixed graph binarization, DT-GCN updates the connectivity threshold at each training epoch according to training-fold loss. The model is trained under a multi-task objective combining classification, reconstruction, and contrastive losses, with the loss weights adjusted across three stages of training. Resting-state 19-channel EEG recordings from a public dataset (DS004504) comprising 36 AD, 23 FTD, and 29 HC participants were segmented into non-overlapping 8 s epochs. FC estimates were aggregated to obtain a subject-level adjacency matrix for each measure, with one-hot node identity and node degree as node features. Classification was performed at the subject level using stratified five-fold cross-validation with normalization and hyperparameter selection confined to training folds. Under the broadband setting, copula-based FC with DT-GCN yielded the highest observed three-class accuracy of 0.82±0.07 (macro-F1 0.79±0.07), compared with 0.45±0.06 accuracy and 0.36±0.06 macro-F1 for the standard GCN baseline. However, the small single-center cohort (N=88, including 23 participants with FTD) and the resulting small test folds limit the precision of the performance estimates and preclude robust inferential comparisons among FC methods and classification scenarios. These exploratory, dataset-specific findings require independent external validation and should not be interpreted as generalizable clinical performance or validated clinical biomarkers.