Alzheimer’s disease (AD) and related neurodegenerative diseases (NDDs) are entering the “Fourth paradigm” of data-driven science, yet a “data void” persists because AD pathology progresses silently for years. This perspective review argues that large-scale electroencephalography (EEG), combined with clinical and biological validation, can provide a scalable functional window onto this gap. Current EEG resources remain fragmented by sampling rates (100–1,000 Hz), channel configurations, file formats, and access restrictions, and AD-focused public datasets are mostly small. The authors propose a dual strategy: prospective standardization through a comprehensive SOP covering ethics, clinical common data elements, acquisition parameters, and BIDS format, plus retrospective harmonization of legacy data. They further examine Transformer-based AI models and explainable AI to avoid “shortcut learning,” and propose a collaborative ecosystem ensuring Robustness, Interpretability, and Scalability by linking standardized EEG with longitudinal cohorts, human brain banks, and post-mortem neuropathology. The framework aims to transform EEG from a clinical tool into a scalable discovery engine for AD and NDDs.

This work, entitled “A Novel Paradigm for Neurodegenerative Diseases: Building an AI-Driven Large-Scale Electroencephalography Database Research,” was published on Human Brain(published on Sep. 20, 2026).
DOI:10.15302/HB.2026.0004