EEG-based classification of alzheimer’s disease and frontotemporal dementia using functional connectivity
Tjaša Mlinarič; Arne Van Den Kerchove; Zoe I. Barinaga; Marc M. Van Hulle · 2026 · Scientific Reports
WASTE classifies this as Negative / Null Result Report · AI classification, approximate
The study found no significant effect — useful as a negative control or null benchmark for your own design.
Abstract
Abstract Alzheimer’s disease (AD) and frontotemporal dementia (FTD) are two major causes of dementia, with overlapping clinical and pathophysiological characteristics. We investigated whether resting-state EEG functional connectivity could distinguish AD, FTD, and healthy controls (HC) using a stacked ensemble learning approach. A publicly available dataset (openneuro.org/datasets/ds004504) was used to extract multiple connectivity metrics across frequency bands. Base classifiers were trained using the Fisher’s Geodesic Minimum Distance to Mean (FgMDM) approach with Euclidean or Riemannian dis
Abstract by Tjaša Mlinarič; Arne Van Den Kerchove; Zoe I. Barinaga; Marc M. Van Hulle, Scientific Reports (2026) — licensed CC BY 4.0.
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Metadata source: OpenAlex · DOI 10.1038/s41598-026-35316-9