Frequency-Resolved Connectivity Graphs for EEG-Based Classification of Alzheimer and Frontotemporal Dementia
- 1Oslo Metropolitan University
Abstract
In this study, we build EEG based graph model that uses frequency-resolved connectivity features, with Coherence, phase locking value (PLV) and phase lag index (PLI) in five frequency bands as multi-dimensional edge features. The model is used to classify Alzheimer disease (AD) and frontotemporal dementia (FTD) against healthy controls (CN). This paper extends our earlier work on FTD with AD vs CN as the main new task, a feature ablation for it, and a joint analysis of both tasks. With leave-one-subject-out (LOSO) validation, the model reaches an accuracy of 81.13\% for AD vs CN and 76.06\% for FTD vs CN, which is competitive with the baselines reported with the dataset with far fewer parameters (159 and 282 thousand versus 170.5 million). Under the random cross-validation setting used for the DICE-net model, our model has a comparable accuracy for FTD and a lower accuracy for AD. Node and edge saliency maps highlight occipital and temporal electrodes, the Alpha band for node features and the Theta band for edge features, which is consistent with earlier EEG studies. In an ablation on the AD task, using all 15 edge features performed better than using a single connectivity measure or edge features averaged over the bands.
Related
- Disease-specific intrinsic frequency alterations in electroencephalographic signals of Alzheimer’s disease, frontotemporal dementia, and Parkinson’s disease
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- Different functional connectivity gradients reflect aging and Alzheimer’s disease
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BibTeX
@misc{radwan2026frequencyresolved,
title = {{Frequency-Resolved Connectivity Graphs for EEG-Based Classification of Alzheimer and Frontotemporal Dementia}},
author = {M. Radwan and A. yazidi and P. G. lind},
howpublished = {bioRxiv},
year = {2026},
month = oct,
doi = {10.64898/2026.10.16.759776},
url = {https://www.biorxiv.org/content/10.64898/2026.10.16.759776v1},
note = {CC BY-NC},
}
RIS
TY - UNPB
TI - Frequency-Resolved Connectivity Graphs for EEG-Based Classification of Alzheimer and Frontotemporal Dementia
AU - M. Radwan
AU - A. yazidi
AU - P. G. lind
PY - 2026
DA - 2026/10/16
PB - bioRxiv
DO - 10.64898/2026.10.16.759776
UR - https://www.biorxiv.org/content/10.64898/2026.10.16.759776v1
AB - In this study, we build EEG based graph model that uses frequency-resolved connectivity features, with Coherence, phase locking value (PLV) and phase lag index (PLI) in five frequency bands as multi-dimensional edge features. The model is used to classify Alzheimer disease (AD) and frontotemporal dementia (FTD) against healthy controls (CN). This paper extends our earlier work on FTD with AD vs CN as the main new task, a feature ablation for it, and a joint analysis of both tasks. With leave-one-subject-out (LOSO) validation, the model reaches an accuracy of 81.13\% for AD vs CN and 76.06\% for FTD vs CN, which is competitive with the baselines reported with the dataset with far fewer parameters (159 and 282 thousand versus 170.5 million). Under the random cross-validation setting used for the DICE-net model, our model has a comparable accuracy for FTD and a lower accuracy for AD. Node and edge saliency maps highlight occipital and temporal electrodes, the Alpha band for node features and the Theta band for edge features, which is consistent with earlier EEG studies. In an ablation on the AD task, using all 15 edge features performed better than using a single connectivity measure or edge features averaged over the bands.
ER -