Clustered pattern projection for EEG dementia classification: evaluating the reliability of disorder patterns

Electroencephalography (EEG)-based classification of Alzheimer's disease (AD) and frontotemporal dementia (FTD) relative to cognitively normal (CN) controls is commonly interpreted through stable disease-related patterns. However, classification-relevant EEG responses in computational models may also appear fragmented or weakly preserved, making subject-level reliability difficult to assess. This study investigated whether recurrent disorder-like EEG patterns can provide exploratory evidence of subject-wise discriminative organization in dementia classification.

We analyzed a publicly available resting-state EEG dataset including AD, FTD, and CN subjects. Clustered Pattern Projection (CPP) was applied to Dynamic Mode Decomposition (DMD)-based epoch descriptors to construct prototype-based EEG representations. Classification was performed using a linear support vector machine under a nested leave-one-subject-out cross-validation (LOSO-CV) framework. Subject-level reliability was further examined using margin-based analysis.

CPP showed competitive subject-level performance, particularly in the FTD vs. CN classification task, a setting in which resting-state discriminative patterns are often less consistently preserved than in AD. In addition, task-dependent subject-level margin patterns were observed under strict subject-wise validation, suggesting that margin analysis may provide a useful exploratory tool for evaluating the reliability of learned EEG representations.

These findings suggest that EEG generalization in dementia classification should not be interpreted only through preserved canonical biomarkers. Instead, recurrent disorder-like patterns may contribute to computationally detectable decision structure, and CPP provides a framework for examining such patterns under strict subject-wise validation.