Background: Dementia affects over 50 million individuals globally, predominantly due to Alzheimer's disease (AD). Effective early detection and intervention remain clinical challenges, as there is a lack of unified, portable solutions to assess multiple biomarkers.
Methods: We evaluated Evoked Potential Tomography (EPT), an EEG-based method using a novel visual evoked potential protocol. An automated pipeline for EEG preprocessing, ERP extraction, feature selection, optimization, and regression modeling was developed to estimate key AD biomarkers: PET-amyloid standardized uptake value ratio (SUVR), CSF phosphorylated tau (p-tau181), Free and Cued Selective Reminding Test (FCSRT), and Mini-Mental State Examination (MMSE) scores.
Results: Regression models using ERP features from dementia participants demonstrated strong correlations (r = 0.8-0.94, p < 0.01) between predicted and true PET-amyloid SUVR, p-tau181, FCSRT, and MMSE values. In an independent external cohort, PET-amyloid SUVR predictions remained significantly associated with true values (r = 0.60, p < 0.01).
Discussion: Despite limitations, these preliminary results support EPT's potential as a sensitive and non-invasive method for estimating AD-related biomarkers in a clinically enriched AD cohort. Further validation studies are ongoing.
Keywords: AD biomarkers; Alzheimer’s disease; Electroencephalography; Event-related potential; PET-amyloid SUVR; Regression modeling.
© 2026. The Author(s).