ArticleBrain sciences2023
Machine Learning on Visibility Graph Features Discriminates the Cognitive Event-Related Potentials of Patients with Early Alzheimer's Disease from Healthy Aging.
Article in Brain sciences, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
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Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Biomarkers for Alzheimer's disease and mild cognitive impairment: Recent advances in task-based EEG.Journal of Alzheimer's disease : JAD · 2026Pooled it
- Unveiling Early Signs of Preclinical Alzheimer's Disease Through ERP Analysis with Weighted Visibility Graphs and Ensemble Learning.Bioengineering (Basel, Switzerland) · 2025Article
- Event-related potential markers of subjective cognitive decline and mild cognitive impairment during a sustained visuo-attentive task.NeuroImage. Clinical · 2025Article
- EEG-based deception detection using weighted dual perspective visibility graph analysis.Cognitive neurodynamics · 2024Article
- Amortization Transformer for Brain Effective Connectivity Estimation from fMRI Data.Brain sciences · 2023Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
We present a framework for electroencephalography (EEG)-based classification between patients with Alzheimer's Disease (AD) and robust normal elderly (RNE) via a graph theory approach using visibility graphs (VGs). This EEG VG approach is motivated by research that has demonstrated differences between patients with early stage AD and RNE using various features of EEG oscillations or cognitive event-related potentials (ERPs). In the present study, EEG signals recorded during a word repetition experiment were wavelet decomposed into 5 sub-bands (δ,θ,α,β,γ). The raw and band-specific signals were then converted to VGs for analysis. Twelve graph features were tested for differences between the AD and RNE groups, and
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.