Work

Phase aligned EEG source space features for distinguishing Alzheimer’s disease from frontotemporal dementia

Year: 2026

Type: article

Abstract: Alzheimer’s disease (AD) and frontotemporal dementia (FTD) share similarities in cognitive deficits and behavioural changes, increasing the difficulty of their differential diagnosis. Electroencephalography (EEG), as a non-invasive, cost-effective and easy-to-use tool that reflects brain activity in real time, provides potential support for differentiating AD and FTD. We propose a source-space feature method based on EEG phase alignment, where dynamic time warping (DTW) enhances inter-trial phase synchrony in the θ, α, β, and γ bands, sLORETA extracts brain region energy features, and a random forest classifier performs classification. In a dataset comprising 36 AD cases, 23 FTD cases, and 29 CN, the classification accuracies for AD vs. FTD and FTD vs. CN reached 94.21% and 90.63%, respectively. Abnormalities in the AD group were primarily observed in the θ and γ bands, involving multiple cognition-related regions, whereas the FTD group exhibited β-band abnormalities predominantly affecting the parietal lobe and default mode network. These findings suggest that source-space features based on phase alignment can effectively distinguish the neural signatures of AD and FTD.

Source: Scientific Reports

DOI: 10.1038/s41598-026-79695-5