Prior-Informed Normative EEG Scoring for Alzheimer's Detection: Framework and Cross-Paradigm Boundary Analysis
EEG-based screening for Alzheimer's disease (AD) is attractive in principle but not deployed in practice, largely because end-to-end classifiers trained on small clinical cohorts generalize poorly. We propose a prior-weighted deviation scorer that fits only the healthy distribution and scores new recordings against directional priors drawn from the AD biomarker literature. Our choice of EEG paradigm was a crucial decision: in a controlled simulation with synthetic AD and synthetic depressionlike cohorts, the between-emotion variance of the deviation score separates simulated AD from simulated depression at AUC 1.00 while a resting-state analog (mean score only) sits at chance (AUC 0.49), since AD's spectral signature stays invariant across emotional conditions while affective pathologies do not. We therefore implement the approach on the SEED-VII emotion-task dataset $(n=20)$ using seven directional priors (no AD-labeled data in training). A deviation score is produced and algebraically decomposed into per-band and per-region contributions. As a pipeline-wiring check, simulated AD recordings constructed along the same prior directions are separable from healthy windows, while sign-flipped and undirected baselines score near chance - confirming that the scorer implements the literature priors as written. Within paradigm, a second healthy cohort (SEED-V, $n=16)$ is not distinguishable from SEED-VII at this sample size (Mann-Whitney $U, p=0.162)$. The headline result is across paradigm: zero-shot transfer to a resting-state clinical EEG dataset (ds004504, $n=88)$ returns AUC $=0.478$, with the three diagnostic groups clustering within 0.02 of each other, even though the raw band powers carry the expected AD signature (delta and theta elevation, alpha reduction). The framework holds within paradigm and fails across it; prior-weighted EEG scorers are paradigm-conditional.