Student or educator? Sign up with your school email and get 6 months of Pro free.
CoChat
Sign Up
Cited by 15Dementia and Cognitive Impairment ResearchEEG and Brain-Computer InterfacesAlzheimer's disease research and treatments

The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.

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

Open access
Papaliagkas, Vasileios·2025·Diagnostics
DOI 10.3390/diagnostics15151965PMC PMC12346855
Open this paper in CoChat — free accountOpen PDF

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.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.3390/diagnostics15151965

Cites

0 citation errors · 0 not checkable

Statistics

Figures

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

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 3 references

Research works printed but not linked: 64

  1. Brookmeyer, 2007, Forecasting the global burden of Alzheimer’s disease, Alzheimer’s Dement., 3, 186, 10.1016/j.jalz.2007.04.381 · DOI · Paper page
  2. Hardy, 2002, The amyloid hypothesis of Alzheimer’s disease: Progress and problems, Science, 297, 353, 10.1126/science.1072994 · DOI · Paper page
  3. Jack, 2010, ypothetical model of dynamic biomarkers of the Alzheimer’s disease pathological cascade, Lancet Neurol., 9, 119, 10.1016/S1474-4422(09)70299-6 · DOI · Paper page
  4. McKhann, 2011, The diagnosis of dementia due to Alzheimer’s disease: Recommendations from the National Institute on Aging-Alzheimer’s Association workgroups, Alzheimer’s Dement., 7, 263, 10.1016/j.jalz.2011.03.005 · DOI · Paper page
  5. Blennow, 2010, Cerebrospinal fluid and plasma biomarkers in Alzheimer disease, Nat. Rev. Neurol., 6, 131, 10.1038/nrneurol.2010.4 · DOI · Paper page
  6. Hansson, 2019, Biomarkers of Alzheimer’s disease: Current status and future perspectives, Alzheimer’s Res. Ther., 11, 86
  7. Varesi, A., Carrara, A., Pires, V.G., Floris, V., Pierella, E., Savioli, G., Prasad, S., Esposito, C., Ricevuti, G., and Chirumbolo, S. (2022). Blood-Based Biomarkers for Alzheimer’s Disease Diagnosis and Progression: An Overview. Cells, 11. · DOI
  8. Dasari, 2025, Blood-Based β-Amyloid and Phosphorylated Tau (p-Tau) Biomarkers in Alzheimer’s Disease: A Systematic Review of Their Diagnostic Potential, Cureus, 17, e79881
  9. Li, 2022, Prospective biomarkers of Alzheimer’s disease: A systematic review and meta-analysis, Ageing Res. Rev., 81, 101699, 10.1016/j.arr.2022.101699 · DOI
  10. Niedermeyer, E., and Lopes da Silva, F.H. (2004). Electroencephalography: Basic Principles, Clinical Applications, and Related Fields, Lippincott Williams & Wilkins.
  11. Placidi, G., Cinque, L., and Polsinelli, M. (2021). A fast and scalable framework for automated artifact recognition from EEG signals represented in scalp topographies of Independent Components. Comput. Biol. Med., 132. · DOI
  12. Scheuer, 2021, Seizure Detection: Interreader Agreement and Detection Algorithm Assessments Using a Large Dataset, J. Clin. Neurophysiol., 38, 439, 10.1097/WNP.0000000000000709 · DOI
  13. Beniczky, 2013, Standardized computer-based organized reporting of EEG: SCORE, Epilepsia, 54, 1112, 10.1111/epi.12135 · DOI
  14. Jeong, 2004, EEG dynamics in patients with Alzheimer’s disease, Clin. Neurophysiol., 115, 1490, 10.1016/j.clinph.2004.01.001 · DOI
  15. Rossini, 2020, Early diagnosis of Alzheimer’s disease: The role of biomarkers including advanced EEG signal analysis. Report from the IFCN-sponsored panel of experts, Clin. Neurophysiol., 131, 1287, 10.1016/j.clinph.2020.03.003 · DOI
  16. Babiloni, 2011, Resting state cortical rhythms in mild cognitive impairment and Alzheimer’s disease: Electroencephalographic evidence, J. Alzheimer’s Dis., 26, 201, 10.3233/JAD-2011-0051 · DOI
  17. Saini, M., Rohilla, S., and Kumar, R. (Curr. Aging Sci., 2025). Revolutionizing Therapeutic Approaches Against Pathophysiology of Alzheimer’s Disease: A Therapeutic Review, Curr. Aging Sci., online ahead of print. · DOI
  18. Greicius, 2008, Resting-state functional connectivity in neuropsychiatric disorders, Curr. Opin. Neurol., 21, 424, 10.1097/WCO.0b013e328306f2c5 · DOI · Paper page
  19. Sheline, 2013, Resting state functional connectivity in preclinical Alzheimer’s disease, Biol. Psychiatry, 74, 340, 10.1016/j.biopsych.2012.11.028 · DOI · Paper page
  20. 2004, Analisis espectral de la coherencia cerebral en la enfermedad de Alzheimer [Spectral analysis of EEG coherence in Alzheimer’s disease], Rev. Neurol., 38, 422, 10.33588/rn.3805.2003535 · DOI
  21. Li, Q., Song, J.L., Li, S.H., Westover, M.B., and Zhang, R. (2020). Effects of Cholinergic Neuromodulation on Thalamocortical Rhythms During NREM Sleep: A Model Study. Front. Comput. Neurosci., 23. · DOI
  22. Fide, 2021, Resting-state EEG alpha/theta power ratio discriminates early-onset Alzheimer’s disease from healthy controls, Clin. Neurophysiol., 132, 2019, 10.1016/j.clinph.2021.05.012 · DOI
  23. Dauwels, 2010, Diagnosis of Alzheimer’s disease from EEG signals using graph-theoretic features, Ann. Biomed. Eng., 38, 2974
  24. Sakkalis, 2011, Review of advanced techniques for the estimation of brain connectivity measured with EEG/MEG, Comput. Biol. Med., 41, 1110, 10.1016/j.compbiomed.2011.06.020 · DOI · Paper page
  25. Babiloni, 2010, Reactivity of cortical alpha rhythms to eye opening in mild cognitive impairment and Alzheimer’s disease: An EEG study, J. Alzheimer’s Dis., 22, 1047, 10.3233/JAD-2010-100798 · DOI
  26. Yuan, Y., and Zhao, Y. (2025). The role of quantitative EEG biomarkers in Alzheimer’s disease and mild cognitive impairment: Applications and insights. Front. Aging Neurosci., 17. · DOI
  27. Ahmadlou, 2011, Fractality and a wavelet-chaos-methodology for EEG-based diagnosis of Alzheimer disease, Alzheimer Dis. Assoc. Disord., 25, 85, 10.1097/WAD.0b013e3181ed1160 · DOI
  28. Bugdol, 2024, Quantitative EEG Spectral and Connectivity Analysis for Cognitive Decline in Amnestic Mild Cognitive Impairment, J. Alzheimer’s Dis., 97, 1235, 10.3233/JAD-230485 · DOI
  29. Knyazeva, 2013, Evolution of source EEG synchronization in early Alzheimer’s disease, Neurobiol. Aging, 34, 694, 10.1016/j.neurobiolaging.2012.07.012 · DOI
  30. Simfukwe, 2023, qEEG as Biomarker for Alzheimer’s Disease: Investigating Relative PSD Difference and Coherence Analysis, Neuropsychiatr Dis. Treat., 19, 2423, 10.2147/NDT.S433207 · DOI
  31. Baik, K., Jung, J.H., Jeong, S.H., Chung, S.J., Yoo, H.S., Lee, P.H., Sohn, Y.H., Kang, S.W., and Ye, B.S. (2022). Implication of EEG theta/alpha and theta/beta ratio in Alzheimer’s and Lewy body disease. Sci. Rep., 12. · DOI
  32. Yeo, 2011, The organization of the human cerebral cortex estimated by intrinsic functional connectivity, J. Neurophysiol., 106, 1125, 10.1152/jn.00338.2011 · DOI · Paper page
  33. Blinowska, 2017, Functional and effective brain connectivity for discrimination between Alzheimer’s patients and healthy individuals: A study on resting state EEG rhythms, Clin. Neurophysiol., 128, 667, 10.1016/j.clinph.2016.10.002 · DOI
  34. Babiloni, 2021, Measures of resting state EEG rhythms for clinical trials in Alzheimer’s disease: Recommendations of an expert panel, Alzheimers Dement., 17, 1528, 10.1002/alz.12311 · DOI
  35. 2018, Multimodal Neuroimaging in Alzheimer’s Disease: Early Diagnosis, Physiopathological Mechanisms, and Impact of Lifestyle, J. Alzheimer’s Dis., 64, S199, 10.3233/JAD-179920 · DOI
  36. Saleem, T.J., Zahra, S.R., Wu, F., Alwakeel, A., Alwakeel, M., Jeribi, F., and Hijji, M. (2022). Deep Learning-Based Diagnosis of Alzheimer’s Disease. J. Pers. Med., 12. · DOI
  37. Al-Nuaimi, A.H., Blūma, M., Al-Juboori, S.S., Eke, C.S., Jammeh, E., Sun, L., and Ifeachor, E. (2021). Robust EEG Based Biomarkers to Detect Alzheimer’s Disease. Brain Sci., 11. · DOI
  38. Malik, I., Iqbal, A., Gu, Y.H., and Al-antari, M.A. (2024). Deep Learning for Alzheimer’s Disease Prediction: A Comprehensive Review. Diagnostics, 14. · DOI
  39. Bairagi, 2018, EEG signal analysis for early diagnosis of alzheimer disease using spectral and wavelet based features, Int. J. Inf. Technol., 10, 403
  40. Basaia, 2019, Automated classification of Alzheimer’s disease and mild cognitive impairment using a single MRI and deep neural networks, NeuroImage Clin., 21, 101645, 10.1016/j.nicl.2018.101645 · DOI · Paper page
  41. Nayana, B.R., Pavithra, M.N., Chaitra, S., Bhuvana Mohini, T.N., Stephan, T., Mohan, V., and Agarwal, N. (2025). EEG-based neurodegenerative disease diagnosis: Comparative analysis of conventional methods and deep learning models. Sci. Rep., 15. · DOI
  42. Lee, M.W., Kim, H.W., Choe, Y.S., Yang, H.S., Lee, J., Lee, H., Yong, J.H., Kim, D., Lee, M., and Kang, D.W. (2024). A multimodal machine learning model for predicting dementia conversion in Alzheimer’s disease. Sci. Rep., 14. · DOI
  43. Ganapathi, 2022, Differentiation of Subjective Cognitive Decline, Mild Cognitive Impairment, and Dementia Using qEEG/ERP-Based Cognitive Testing and Volumetric MRI in an Outpatient Specialty Memory Clinic, J. Alzheimer’s Dis., 90, 1761, 10.3233/JAD-220616 · DOI
  44. Aviles, 2024, Machine and Deep Learning Trends in EEG-Based Detection and Diagnosis of Alzheimer’s Disease: A Systematic Review, Eng, 5, 1464, 10.3390/eng5030078 · DOI
  45. Wang, 2021, Multi-modal neuroimaging and EEG biomarkers for early Alzheimer’s diagnosis: Progress and perspectives, J. Neuroimaging, 31, 626
  46. Briels, 2020, Reproducibility of EEG functional connectivity in Alzheimer’s disease, Alzheimer’s Res. Ther., 12, 68, 10.1186/s13195-020-00632-3 · DOI
  47. Schmidt, M.T., Kanda, P.A.M., Basile, L.F.H., da Silva Lopes, H.F., Baratho, R., Demario, J.L.C., Jorge, M.S., Nardi, A.E., Machado, S., and Ianof, J.N. (2013). Index of alpha/theta ratio of the electroencephalogram: A new marker for Alzheimer’s disease. Front. Aging Neurosci., 5. · DOI
  48. Siuly, 2020, A new framework for automatic detection of patients with mild cognitive impairment using resting-state EEG signals, IEEE Trans. Neural Syst. Rehabil. Eng., 28, 1966, 10.1109/TNSRE.2020.3013429 · DOI
  49. Babiloni, 2018, Abnormalities of resting state cortical EEG rhythms in subjects with mild cognitive impairment due to alzheimer’s and lewy body diseases, J. Alzheimer’s Dis., 62, 247, 10.3233/JAD-170703 · DOI
  50. Dottori, M., Sedeño, L., Caro, M.M., Alifano, F., Hesse, E., Mikulan, E., García, A.M., Ruiz-Tagle, A., Lillo, P., and Slachevsky, A. (2017). Towards affordable biomarkers of frontotemporal dementia: A classification study via network’s information sharing. Sci. Rep., 7. · DOI · Paper page
  51. Ieracitano, 2019, A convolutional neural network approach for classification of dementia stages based on 2D-spectral representation of EEG recordings, Neurocomputing, 323, 96, 10.1016/j.neucom.2018.09.071 · DOI
  52. Klepl, 2022, EEG-based graph neural network classification of Alzheimer’s disease: An empirical evaluation of functional connectivity methods, IEEE Trans. Neural Syst. Rehabil. Eng., 30, 2651, 10.1109/TNSRE.2022.3204913 · DOI
  53. Gkenios, G., Latsiou, K., Diamantaras, K., Chouvarda, I., and Tsolaki, M. (2022, January 11–15). Diagnosis of Alzheimer’s disease and mild cognitive impairment using EEG and recurrent neural networks. Proceedings of the 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), Glasgow, UK. · DOI
  54. Klepl, 2023, Adaptive gated graph convolutional network for explainable diagnosis of Alzheimer’s disease using EEG data, IEEE Trans. Neural Syst. Rehabil. Eng., 31, 3978, 10.1109/TNSRE.2023.3321634 · DOI
  55. Jiao, 2023, Neural biomarker diagnosis and prediction to mild cognitive impairment and Alzheimer’s disease using EEG technology, Alzheimer’s Res. Ther., 15, 32, 10.1186/s13195-023-01181-1 · DOI
  56. Hasan Saif, F., Al-Andoli, M.N., and Bejuri, W.M.Y.W. (2024). Explainable AI for Alzheimer Detection: A Review of Current Methods and Applications. Appl. Sci., 14. · DOI
  57. Ehteshamzad, 2024, Assessing the Potential of EEG in Early Detection of Alzheimer’s Disease: A Systematic Comprehensive Review (2000–2023), J. Alzheimer’s Dis. Rep., 8, 1153, 10.3233/ADR-230159 · DOI
  58. Akbar, 2025, Unlocking the potential of EEG in Alzheimer’s disease research: Current status and pathways to precision detection, Brain Res. Bull., 223, 111281, 10.1016/j.brainresbull.2025.111281 · DOI
  59. Zhang, T. (2022). Efficient deep learning approaches for EEG-based early Alzheimer’s detection: A review of recent progress. Front. Aging Neurosci., 14.
  60. Arya, 2023, A systematic review on machine learning and deep learning techniques in the effective diagnosis of Alzheimer’s disease, Brain Inform., 10, 17, 10.1186/s40708-023-00195-7 · DOI
  61. Del Percio, C., Lizio, R., Lopez, S., Noce, G., Carpi, M., Jakhar, D., Soricelli, A., Salvatore, M., Yener, G., and Güntekin, B. (2025). Resting-State EEG Alpha Rhythms Are Related to CSF Tau Biomarkers in Prodromal Alzheimer’s Disease. Int. J. Mol. Sci., 26. · DOI
  62. Jiang, X., Bian, G.-B., and Tian, Z. (2019). Removal of Artifacts from EEG Signals: A Review. Sensors, 19. · DOI
  63. Mostile, G., Terranova, R., Carlentini, G., Contrafatto, F., Terravecchia, C., Donzuso, G., Sciacca, G., Cicero, C.E., Luca, A., and Nicoletti, A. (2024). Differentiating neurodegenerative diseases based on EEG complexity. Sci. Rep., 14. · DOI
  64. Prichep, 2005, Use of normative databases and statistical methods in demonstrating clinical utility of QEEG: Importance and cautions, Clin. EEG Neurosci., 36, 82, 10.1177/155005940503600207 · DOI

Cited by

See all 15

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

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: The Role of Quantitative EEG in the Diagnosis of Alzheimer’s Disease. https://app.cochat.ai/papers/10.3390/diagnostics15151965
The paper
Papaliagkas, V. (2025). The Role of Quantitative EEG in the Diagnosis of Alzheimer’s Disease. Diagnostics. https://doi.org/10.3390/diagnostics15151965

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.