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Cited by 15Dementia and Cognitive Impairment ResearchEEG and Brain-Computer InterfacesAlzheimer's disease research and treatments

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The Role of Quantitative EEG in the Diagnosis of Alzheimer’s Disease

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Papaliagkas, Vasileios·2025·Diagnostics
DOI 10.3390/diagnostics15151965PMC PMC12346855
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Abstract

Alzheimer’s disease is the most prevalent neurodegenerative disorder leading to progressive cognitive decline and functional impairment. Although advanced neuroimaging and cerebrospinal fluid biomarkers have improved early detection, their high costs, invasiveness, and limited accessibility restrict universal screening. Quantitative electroencephalography (qEEG) offers a non-invasive and cost-effective alternative for assessing neurophysiological changes associated with AD. This review critically evaluates current evidence on EEG biomarkers, including spectral, connectivity, and complexity measures, discussing their pathophysiological basis, diagnostic accuracy, and clinical utility in AD. Limitations and future perspectives, especially in developing standardized protocols and integrating machine learning techniques, are also addressed.

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Figure 1 Power Spectral Density Map for (a) AD patients and (b) Controls. Figure adapted by Simfukwe C et al. [32], CC License 3.0.
Figure 1 Power Spectral Density Map for (a) AD patients and (b) Controls. Figure adapted by Simfukwe C et al. [32], CC License 3.0.CC BY · PubMed Central

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Cited by

Metadata

Authors
Papaliagkas, V.
Affiliations
International Hellenic University (GR)
Corresponding author
Papaliagkas, Vasileios
ORCID
Papaliagkas, Vasileios
Venue
Diagnostics
Publisher
MDPI AG
Published
2025-08-05
Volume
15
Issue
15
Pages
1965
ISSN
2075-4418 (ISSN-L)
DOI
10.3390/diagnostics15151965
PMC
PMC12346855
OpenAlex
W4412974787
Cited by
14
References
64
Citations by year
2026: 9 · 2025: 5
FWCI
2.50
Type
journal article
Language
en
Open access
Licence
Locations
  • Diagnostics (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
  • Life Sciences › Neuroscience › Cognitive Neuroscience › EEG and Brain-Computer Interfaces
  • Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Alzheimer's disease research and treatments
Keywords
Alzheimer's disease, quantitative EEG, EEG biomarkers, spectral analysis, EEG connectivity, non-invasive diagnosis, neurodegeneration, machine learning, diagnostic accuracy

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