Unsupervised EEG-Based Subgroup Discovery in Alzheimer’s Disease Using Spectral and Temporal Features

This study contributes an unsupervised, subject-level EEG framework for identifying reproducible electrophysiological subgroups within Alzheimer’s Disease. Unlike conventional AD classification approaches, it integrates multi-domain EEG features, PCA-based dimensionality reduction, K-Means clustering, bootstrap consensus stability analysis, and split-half validation to ensure that the discovered subgroups are both meaningful and reproducible. This provides a systematic approach for characterizing intra-AD heterogeneity and may support future research on disease progression and personalized clinical strategies.