Work

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

Year: 2025

Type: article

Abstract: 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.

Source: Brain-Apparatus Communication A Journal of Bacomics

DOI: 10.1080/27706710.2025.2604895