The AHEPA EEG benchmark: setting the standard for machine learning in dementia diagnosis, a scoping review

Topic: Amyloid, tau and blood-test diagnosis

Accurate and reproducible electroencephalography (EEG)-based classification of dementia remains a key challenge in computational neurodiagnostics. The open-access AHEPA dataset has become the most commonly used benchmark for Alzheimer's disease (AD) and Frontotemporal dementia (FTD) classification, yet reported results vary widely due to methodological inconsistencies. This study presents the first systematic and quantitative benchmark review of all published machine learning approaches applied to the AHEPA dataset. Forty-six studies were reviewed and stratified into three validity tiers, with Validity 1 representing the highest methodological rigor and Validity 3 the lowest.According to their evaluation rigor: (1) subject-level validation (e.g., Leave-One-Subject-Out cross-validation, LOSO-CV), (2) subject-level train/test splits, and (3) epoch-level k-fold cross-validation. Performance metrics were normalized across classification problems. The analysis revealed that methodological rigor is inversely correlated with reported accuracy: for AD versus Cognitively Normal controls, mean accuracy decreased from 90.81% overall to 82.11% in Validity-1 studies; for FTD versus controls, accuracy dropped from 86.53% to 75.18%. Linear regression analyses demonstrated that weaker validation protocols were associated with systematic increases of 7-10% points in reported accuracy, explaining more than half of the observed performance variance. Deep and hybrid models reported the highest nominal accuracies, but under proper validation, traditional algorithms performed comparably, indicating that data leakage often drives apparent improvements. The review also highlights the lack of cross-configuration generalization and the urgent need for adaptive, montage-independent methodologies. Overall, this benchmark establishes the first reproducible reference framework for EEG-based dementia classification on the AHEPA dataset, providing quantitative baselines and validity criteria against which all future studies should be evaluated.

Key findings

• The open-access AHEPA dataset has become the most commonly used benchmark for Alzheimer's disease (AD) and Frontotemporal dementia (FTD) classification, yet reported results vary widely due to methodological inconsistencies.
• The analysis revealed that methodological rigor is inversely correlated with reported accuracy: for AD versus Cognitively Normal controls, mean accuracy decreased from 90.81% overall to 82.11% in Validity-1 studies; for FTD versus controls, accuracy dropped from 86.53% to 75.18%.
• Linear regression analyses demonstrated that weaker validation protocols were associated with systematic increases of 7-10% points in reported accuracy, explaining more than half of the observed performance variance.
• Overall, this benchmark establishes the first reproducible reference framework for EEG-based dementia classification on the AHEPA dataset, providing quantitative baselines and validity criteria against which all future studies should be evaluated.

Why it matters

This review found that reported accuracy for dementia classification drops when stricter validation methods are used, from 90.81% to 82.11% for Alzheimer's versus controls. It is a methodological analysis of existing studies, so it does not offer a treatment, cure or prevention.

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Study details

• Effect size90.81%; 82.11%; 86.53% to 75.18%
• Evidence strengthLow

Read the original

• DOI: 10.1007/s11571-026-10464-w
• PubMed: 42165009
• Original source →
• Free full text: Cognitive Neurodynamics →

Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).

@article{miltiadous2026-42165009,
  author  = {Miltiadous, Andreas and Ntetska, Aimilia and Aspiotis, Vasileios and Moustakli, Efthalia and Tsipouras, Markos G. and Tzallas, Alexandros T. and Γιαννακέας, Νικόλαος and Glavas, Euripidis and others},
  title   = {{The AHEPA EEG benchmark: setting the standard for machine learning in dementia diagnosis, a scoping review}},
  journal = {Cognitive Neurodynamics},
  year    = {2026},
  doi     = {10.1007/s11571-026-10464-w},
  pmid    = {42165009},
  url     = {https://doi.org/10.1007/s11571-026-10464-w}
}

Studies this one cites

Referenced by this study and also in the alzhub library, so you can read them here.

• Missed and delayed diagnosis of dementia in primary care: prevalence and contributing factors Alzheimer disease and associated disorders · 2009-10-01 · usefulness score 67/100 · Getting a diagnosis
• Genetic predisposition, modifiable-risk-factor profile and long-term dementia risk in the general population Nature medicine · 2019-08-26 · usefulness score 63/100 · Prevention and risk
• 2024 Alzheimer's disease facts and figures Alzheimer's & dementia : the journal of the Alzheimer's Association · 2024-04-30 · usefulness score 62/100 · Caregiving
• Multi Layered Omics Approaches Reveal Glia Specific Alterations in Alzheimer's Disease: A Systematic Review and Future Prospects Glia · 2024-12-09 · usefulness score 57/100 · Inflammation and the immune system
• The neuropathological diagnosis of Alzheimer's disease Molecular neurodegeneration · 2019-08-02 · usefulness score 56/100 · Getting a diagnosis
• Exploring the Effectiveness of Machine Learning and Deep Learning Techniques for EEG Signal Classification in Neurological Disorders IEEE Access · 2025-01-01 · usefulness score 51/100 · Digital health tools
• Alzheimer's Disease and Frontotemporal Dementia: A Robust Classification Method of EEG Signals and a Comparison of Validation Methods Diagnostics (Basel, Switzerland) · 2021-08-09 · usefulness score 50/100 · Digital health tools
• An explainable and efficient deep learning framework for EEG-based diagnosis of Alzheimer's disease and frontotemporal dementia Frontiers in Medicine · 2025-07-15 · usefulness score 47/100 · Digital health tools
• Investigating Brain Lobe Biomarkers to Enhance Dementia Detection Using EEG Data Cognitive Computation · 2025-04-01 · usefulness score 42/100 · Getting a diagnosis
• Diagnosis of Alzheimer’s Disease and Frontotemporal Dementia From Electroencephalography Signals IEEE Transactions on Neural Systems and Rehabilitation Engineering · 2025-01-01 · usefulness score 40/100 · Getting a diagnosis
• Standardizing Number of EEG Sensors for AI-Driven Dementia Detection IEEE Sensors Letters · 2025-04-14 · usefulness score 38/100 · Digital health tools

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