AI-enhanced EEG and source localization for retrospective analysis of Alzheimer’s disease and mild cognitive impairment

Background Distinguishing Alzheimer’s disease (AD) and mild cognitive impairment (MCI) through electroencephalography (EEG) remains challenging due to subtle neurophysiological differences.Method This study establishes an AI-enhanced EEG framework integrating Random Forest classification with sLORETA/Brainstorm source localization to differentiate AD, MCI, and healthy controls (HC) using 80 age-matched participants.Results Our model achieved significant diagnostic accuracy improvements by automating statistically valid data screening (p ≤ 0.05), increasing recognition rates to 93% for AD (vs. 48% manual) and 91% for MCI (vs. 44%). Critically, spectral and spatial analysis revealed distinct neurophysiological signatures: MCI exhibited prefrontal hyperactivity in the β3 frequency band, while AD showed temporal lobe hyperactivation in the β2 band, with both abnormalities preceding structural atrophy.Conclusion These spatial-spectral biomarkers reflect stage-specific pathophysiology with prefrontal network overcompensation in MCI and pathological temporal synchronization in AD, thus providing a noninvasive tool for early detection.