{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T19:42:06Z","timestamp":1788982926843,"version":"build-2803163510"},"reference-count":108,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2025,6,13]],"date-time":"2025-06-13T00:00:00Z","timestamp":1749772800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ministry of Education","award":["2021R1A6A1A03038996"],"award-info":[{"award-number":["2021R1A6A1A03038996"]}]},{"name":"Ministry of Education","award":["RS-2024-00507796"],"award-info":[{"award-number":["RS-2024-00507796"]}]},{"name":"Ministry of Trade, Industry &amp; Energy","award":["2021R1A6A1A03038996"],"award-info":[{"award-number":["2021R1A6A1A03038996"]}]},{"name":"Ministry of Trade, Industry &amp; Energy","award":["RS-2024-00507796"],"award-info":[{"award-number":["RS-2024-00507796"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Biomarkers currently used to diagnose dementia, including Alzheimer\u2019s disease (AD), primarily detect molecular and structural brain changes associated with the condition\u2019s pathology. Although these markers are pivotal in detecting disease-specific neuropathological hallmarks, their association with the clinical manifestations of dementia frequently remains poorly defined and exhibits considerable variability. These biomarkers may show abnormalities in cognitively healthy individuals and frequently fail to accurately represent the severity of cognitive and functional impairments in individuals with dementia. Research indicates that synaptic degeneration and functional impairment occur early in the progression of AD and exhibit the strongest correlation with clinical symptoms. This identifies brain functional impairment measurements as promising early indicators for AD detection. Electroencephalography (EEG), a non-invasive and cost-effective method with high temporal resolution, is used as a biomarker for the early detection and diagnosis of AD through frequency-domain analysis of quantitative EEG (qEEG). Many researchers demonstrate that qEEG measures effectively identify disruptions in neuronal activity, including alterations in activity patterns, topographical distribution, and synchronization. Specific findings along the stages of AD include impaired neuronal synchronization, generalized EEG slowing, and an increase in lower-frequency bands accompanied by a decrease in higher-frequency bands of resting state EEG. Moreover, qEEG helps clinicians effectively correlate indicators of AD neuropathology and distinguish between various forms of dementia, positioning it as a promising, low-cost, non-invasive biomarker for dementia. However, additional clinical investigation is required to clarify the diagnostic and prognostic significance of qEEG measurements as early functional markers for AD. This narrative review examines time-frequency domain qEEG analysis as a potential biomarker across various types of dementia. Through a structured search of PubMed and Scopus, we identified studies assessing spectral and connectivity-based qEEG features. Consistent findings include EEG slowing, reduced functional connectivity, and network desynchronization. The review outlines key methodological challenges, such as lack of standardization and limited longitudinal validation, and recommends integrative, multimodal approaches to enhance diagnostic precision and clinical applicability.<\/jats:p>","DOI":"10.3390\/diagnostics15121509","type":"journal-article","created":{"date-parts":[[2025,6,13]],"date-time":"2025-06-13T09:51:24Z","timestamp":1749808284000},"page":"1509","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Time-Frequency Domain Analysis of Quantitative Electroencephalography as a Biomarker for Dementia"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2845-8016","authenticated-orcid":false,"given":"Chanda","family":"Simfukwe","sequence":"first","affiliation":[{"name":"Department of Bionano Technology, Gachon University, Seongnam-si 1342, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8123-6183","authenticated-orcid":false,"given":"Seong Soo A.","family":"An","sequence":"additional","affiliation":[{"name":"Department of Bionano Technology, Gachon University, Seongnam-si 1342, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Young Chul","family":"Youn","sequence":"additional","affiliation":[{"name":"Department of Neurology, College of Medicine, Chung-Ang University, Seoul 06974, Republic of Korea"},{"name":"Department of Medical Informatics, Chung-Ang University College of Medicine, Seoul 06974, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,6,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1016\/S0987-7053(01)00254-4","article-title":"Diagnostic value of quantitative EEG in Alzheimer\u2019s disease","volume":"31","author":"Bennys","year":"2001","journal-title":"Neurophysiol. Clin. Clin. Neurophysiol."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1287","DOI":"10.1016\/j.clinph.2020.03.003","article-title":"Early diagnosis of Alzheimer\u2019s disease: The role of biomarkers including advanced EEG signal analysis. Report from the IFCN-sponsored panel of experts","volume":"131","author":"Rossini","year":"2020","journal-title":"Clin. Neurophysiol. Off. J. Int. Fed. Clin. Neurophysiol."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Safi, M.S.S., and Safi, S.M.M. (2021). Early detection of Alzheimer\u2019s disease from EEG signals using Hjorth parameters. Biomed. Signal Process. Control, 65.","DOI":"10.1016\/j.bspc.2020.102338"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"6290","DOI":"10.1007\/s00415-022-11337-y","article-title":"Recent applications of quantitative electroencephalography in adult intensive care units: A comprehensive review","volume":"269","author":"Hwang","year":"2022","journal-title":"J. Neurol."},{"key":"ref_5","first-page":"75","article-title":"Quantitative EEG in early Alzheimer\u2019s disease patients\u2014Power spectrum and complexity features","volume":"68","author":"Czigler","year":"2008","journal-title":"Int. J. Psychophysiol. Off. J. Int. Organ. Psychophysiol."},{"key":"ref_6","unstructured":"Alzheimer\u2019s Association (2024). Alzheimer\u2019s disease facts and figures. Alzheimer\u2019s Dement. J. Alzheimer\u2019s Assoc., 20, 3708\u20133821."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1337","DOI":"10.2105\/AJPH.88.9.1337","article-title":"Projections of Alzheimer\u2019s disease in the United States and the public health impact of delaying disease onset","volume":"88","author":"Brookmeyer","year":"1998","journal-title":"Am. J. Public Health"},{"key":"ref_8","first-page":"a006189","article-title":"Neuropathological alterations in Alzheimer disease","volume":"1","author":"Frosch","year":"2011","journal-title":"Cold Spring Harb. Perspect. Med."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Meftah, S., and Gan, J. (2023). Alzheimer\u2019s disease as a synaptopathy: Evidence for dysfunction of synapses during disease progression. Front. Synaptic Neurosci., 15.","DOI":"10.3389\/fnsyn.2023.1129036"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Rao, Y.L., Ganaraja, B., Murlimanju, B.V., Joy, T., Krishnamurthy, A., and Agrawal, A. (2022). Hippocampus and its involvement in Alzheimer\u2019s disease: A review. 3 Biotech, 12.","DOI":"10.1007\/s13205-022-03123-4"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"186","DOI":"10.1016\/j.pharmthera.2018.11.006","article-title":"Synaptic dysfunction in Alzheimer\u2019s disease: Mechanisms and therapeutic strategies","volume":"195","author":"Chen","year":"2019","journal-title":"Pharmacol. Ther."},{"key":"ref_12","unstructured":"GBD 2019 Dementia Forecasting Collaborators (2022). Estimation of the global prevalence of dementia in 2019 and forecasted prevalence in 2050: An analysis for the Global Burden of Disease Study 2019. Lancet. Public Health, 7, e105\u2013e125."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"490","DOI":"10.1007\/s11920-014-0490-8","article-title":"Mild cognitive impairment: Diagnosis, longitudinal course, and emerging treatments","volume":"16","author":"Vega","year":"2014","journal-title":"Curr. Psychiatry Rep."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"553","DOI":"10.1007\/s40120-022-00338-8","article-title":"Alzheimer\u2019s Disease: Epidemiology and Clinical Progression","volume":"11","author":"Byrnes","year":"2022","journal-title":"Neurol. Ther."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"e230173","DOI":"10.1148\/radiol.230173","article-title":"Neuroimaging in Dementia: More than Typical Alzheimer Disease","volume":"308","author":"Haller","year":"2023","journal-title":"Radiology"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Yu, T.W., Lane, H.Y., and Lin, C.H. (2021). Novel Therapeutic Approaches for Alzheimer\u2019s Disease: An Updated Review. Int. J. Mol. Sci., 22.","DOI":"10.3390\/ijms22158208"},{"key":"ref_17","first-page":"e334","article-title":"Cognitive reserve and clinical progression in Alzheimer disease: A paradoxical relationship","volume":"93","author":"Wink","year":"2019","journal-title":"Neurology"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Yuan, Y., and Zhao, Y. (2025). The role of quantitative EEG biomarkers in Alzheimer\u2019s disease and mild cognitive impairment: Applications and insights. Front. Aging Neurosci., 17.","DOI":"10.3389\/fnagi.2025.1522552"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"927573","DOI":"10.4061\/2011\/927573","article-title":"Electroencephalographic rhythms in Alzheimer\u2019s disease","volume":"2011","author":"Lizio","year":"2011","journal-title":"Int. J. Alzheimer\u2019s Dis."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1753","DOI":"10.1212\/01.wnl.0000435558.98447.17","article-title":"CSF biomarker changes precede symptom onset of mild cognitive impairment","volume":"81","author":"Moghekar","year":"2013","journal-title":"Neurology"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Fingelkurts, A.A., and Fingelkurts, A.A. (2022). Quantitative Electroencephalogram (qEEG) as a Natural and Non-Invasive Window into Living Brain and Mind in the Functional Continuum of Healthy and Pathological Conditions. Appl. Sci., 12.","DOI":"10.3390\/app12199560"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1177\/1550059420962343","article-title":"Quantitative EEG in Childhood Attention Deficit Hyperactivity Disorder and Learning Disabilities","volume":"52","author":"Chiarenza","year":"2021","journal-title":"Clin. EEG Neurosci."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Zhang, H., Jiang, X., Ma, L., Wei, W., Li, Z., Chang, S., Wen, J., Sun, J., and Li, H. (2022). Role of A\u03b2 in Alzheimer\u2019s-related synaptic dysfunction. Front. Cell Dev. Biol., 10.","DOI":"10.3389\/fcell.2022.964075"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"6833","DOI":"10.1523\/JNEUROSCI.0379-23.2023","article-title":"Synaptic Compensatory Plasticity in Alzheimer\u2019s Disease","volume":"43","author":"Bhembre","year":"2023","journal-title":"J. Neurosci. Off. J. Soc. Neurosci."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1186\/1750-1326-4-48","article-title":"Alzheimer\u2019s disease: Synaptic dysfunction and Abeta","volume":"4","author":"Shankar","year":"2009","journal-title":"Mol. Neurodegener."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Pretel, J., Torres, J.J., and Marro, J. (2021). EEGs Disclose Significant Brain Activity Correlated with Synaptic Fickleness. Biology, 10.","DOI":"10.3390\/biology10070647"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1186\/s13195-022-01046-z","article-title":"Investigating the power of eyes open resting state EEG for assisting in dementia diagnosis","volume":"14","author":"Jennings","year":"2022","journal-title":"Alzheimer\u2019s Res. Ther."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"110195","DOI":"10.1016\/j.jneumeth.2024.110195","article-title":"Advanced qEEG analyses discriminate between dementia","volume":"409","author":"Burelo","year":"2024","journal-title":"J. Neurosci. Methods"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"100675","DOI":"10.37349\/en.2025.100675","article-title":"The role of neuroimaging in Alzheimer\u2019s disease: Implications for the diagnosis, monitoring disease progression, and treatment","volume":"4","author":"Mulumba","year":"2025","journal-title":"Explor. Neurosci."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"512","DOI":"10.3390\/make5020031","article-title":"Alzheimer\u2019s Disease Detection from Fused PET and MRI Modalities Using an Ensemble Classifier","volume":"5","author":"Shukla","year":"2023","journal-title":"Mach. Learn. Knowl. Extr."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Park, J., Kim, W.J., Jung, H.W., Kim, J.J., and Park, J.Y. (2025). Relationship between regional relative theta power and amyloid deposition in mild cognitive impairment: An exploratory study. Front. Neurosci., 19.","DOI":"10.3389\/fnins.2025.1510878"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Al-Ezzi, A., Arechavala, R.J., Butler, R., Nolty, A., Kang, J.J., Shimojo, S., Wu, D.A., Fonteh, A.N., Kleinman, M.T., and Kloner, R.A. (2024). Disrupted brain functional connectivity as early signature in cognitively healthy individuals with pathological CSF amyloid\/tau. Commun. Biol., 7.","DOI":"10.1038\/s42003-024-06673-w"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1153","DOI":"10.3233\/ADR-230159","article-title":"Assessing the Potential of EEG in Early Detection of Alzheimer\u2019s Disease: A Systematic Comprehensive Review (2000-2023)","volume":"8","author":"Ehteshamzad","year":"2024","journal-title":"J. Alzheimer\u2019s Dis. Rep."},{"key":"ref_34","first-page":"144","article-title":"Principles of magnetoencephalography","volume":"8","author":"Sato","year":"1991","journal-title":"J. Clin. Neurophysiol. Off. Publ. Am. Electroencephalogr. Soc."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1237","DOI":"10.1136\/jnnp-2017-317879","article-title":"Transcranial magnetic stimulation predicts cognitive decline in patients with Alzheimer\u2019s disease","volume":"89","author":"Motta","year":"2018","journal-title":"J. Neurol. Neurosurg. Psychiatry"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1016\/j.mcn.2019.02.001","article-title":"Synaptic vesicle protein 2A as a potential biomarker in synaptopathies","volume":"97","author":"Heurling","year":"2019","journal-title":"Mol. Cell. Neurosci."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1016\/j.neurobiolaging.2017.05.017","article-title":"EEG spectral analysis as a putative early prognostic biomarker in nondemented, amyloid positive subjects","volume":"57","author":"Gouw","year":"2017","journal-title":"Neurobiol. Aging"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1007\/s40120-019-00169-0","article-title":"Neurophysiological Markers of Alzheimer\u2019s Disease: Quantitative EEG Approach","volume":"8","author":"Smailovic","year":"2019","journal-title":"Neurol. Ther."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"119190","DOI":"10.1016\/j.neuroimage.2022.119190","article-title":"Harmonized-Multinational qEEG norms (HarMNqEEG)","volume":"256","author":"Li","year":"2022","journal-title":"NeuroImage"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"2423","DOI":"10.2147\/NDT.S433207","article-title":"qEEG as Biomarker for Alzheimer\u2019s Disease: Investigating Relative PSD Difference and Coherence Analysis","volume":"19","author":"Simfukwe","year":"2023","journal-title":"Neuropsychiatr. Dis. Treat."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Ruiz-G\u00f3mez, S.J., G\u00f3mez, C., Poza, J., Mart\u00ednez-Zarzuela, M., Tola-Arribas, M.A., Cano, M., and Hornero, R. (2018). Measuring Alterations of Spontaneous EEG Neural Coupling in Alzheimer\u2019s Disease and Mild Cognitive Impairment by Means of Cross-Entropy Metrics. Front. Neuroinformatics, 12.","DOI":"10.3389\/fninf.2018.00076"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"539621","DOI":"10.4061\/2011\/539621","article-title":"Slowing and Loss of Complexity in Alzheimer\u2019s EEG: Two Sides of the Same Coin?","volume":"2011","author":"Dauwels","year":"2011","journal-title":"Int. J. Alzheimer\u2019s Dis."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Tomasello, L., Carlucci, L., Lagan\u00e0, A., Galletta, S., Marinelli, C.V., Raffaele, M., and Zoccolotti, P. (2023). Neuropsychological Evaluation and Quantitative EEG in Patients with Frontotemporal Dementia, Alzheimer\u2019s Disease, and Mild Cognitive Impairment. Brain Sci., 13.","DOI":"10.3390\/brainsci13060930"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/S0304-3940(98)00669-7","article-title":"Longitudinal changes in quantitative EEG during long-term tacrine treatment of patients with Alzheimer\u2019s disease","volume":"254","author":"Jelic","year":"1998","journal-title":"Neurosci. Lett."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"126","DOI":"10.12779\/dnd.2022.21.4.126","article-title":"Electroencephalography for Early Detection of Alzheimer\u2019s Disease in Subjective Cognitive Decline","volume":"21","author":"Shim","year":"2022","journal-title":"Dement. Neurocognitive Disord."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1159\/000017210","article-title":"A longitudinal quantitative EEG study of Alzheimer\u2019s disease: Relation to apolipoprotein E polymorphism","volume":"11","author":"Lehtovirta","year":"2000","journal-title":"Dement. Geriatr. Cogn. Disord."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Smailovic, U., Ferreira, D., Aus\u00e9n, B., Ashton, N.J., Koenig, T., Zetterberg, H., Blennow, K., and Jelic, V. (2022). Decreased Electroencephalography Global Field Synchronization in Slow-Frequency Bands Characterizes Synaptic Dysfunction in Amnestic Subtypes of Mild Cognitive Impairment. Front. Aging Neurosci., 14.","DOI":"10.3389\/fnagi.2022.755454"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1961","DOI":"10.1016\/S1388-2457(00)00454-5","article-title":"Discrimination of Alzheimer\u2019s disease and mild cognitive impairment by equivalent EEG sources: A cross-sectional and longitudinal study","volume":"111","author":"Huang","year":"2000","journal-title":"Clin. Neurophysiol. Off. J. Int. Fed. Clin. Neurophysiol."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1016\/j.neurobiolaging.2017.11.005","article-title":"Quantitative EEG power and synchronization correlate with Alzheimer\u2019s disease CSF biomarkers","volume":"63","author":"Smailovic","year":"2018","journal-title":"Neurobiol. Aging"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Roshanaei, M., Norouzi, H., Onton, J., Makeig, S., and Mohammadi, A. (2025). EEG-based functional and effective connectivity patterns during emotional episodes using graph theoretical analysis. Sci. Rep., 15.","DOI":"10.1038\/s41598-025-86040-9"},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"1332","DOI":"10.1016\/j.clinph.2003.12.029","article-title":"EEG synchronization likelihood in mild cognitive impairment and Alzheimer\u2019s disease during a working memory task","volume":"115","author":"Pijnenburg","year":"2004","journal-title":"Clin. Neurophysiol. Off. J. Int. Fed. Clin. Neurophysiol."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"2583","DOI":"10.1111\/j.0953-816X.2004.03333.x","article-title":"Abnormal fronto-parietal coupling of brain rhythms in mild Alzheimer\u2019s disease: A multicentric EEG study","volume":"19","author":"Babiloni","year":"2004","journal-title":"Eur. J. Neurosci."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1016\/j.brainresbull.2005.10.013","article-title":"Fronto-parietal coupling of brain rhythms in mild cognitive impairment: A multicentric EEG study","volume":"69","author":"Babiloni","year":"2006","journal-title":"Brain Res. Bull."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Supekar, K., Menon, V., Rubin, D., Musen, M., and Greicius, M.D. (2008). Network analysis of intrinsic functional brain connectivity in Alzheimer\u2019s disease. PLoS Comput. Biol., 4.","DOI":"10.1371\/journal.pcbi.1000100"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"1178","DOI":"10.1002\/hbm.20346","article-title":"Phase lag index: Assessment of functional connectivity from multi channel EEG and MEG with diminished bias from common sources","volume":"28","author":"Stam","year":"2007","journal-title":"Hum. Brain Mapp."},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Engels, M.M., Stam, C.J., van der Flier, W.M., Scheltens, P., de Waal, H., and van Straaten, E.C. (2015). Declining functional connectivity and changing hub locations in Alzheimer\u2019s disease: An EEG study. BMC Neurol., 15.","DOI":"10.1186\/s12883-015-0400-7"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1016\/j.neures.2008.06.009","article-title":"Decreased EEG synchronization and its correlation with symptom severity in Alzheimer\u2019s disease","volume":"62","author":"Park","year":"2008","journal-title":"Neurosci. Res."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1177\/1550059413489669","article-title":"Electroencephalogram global field synchronization analysis: A new method for assessing the progress of cognitive decline in Alzheimer\u2019s disease","volume":"45","author":"Ma","year":"2014","journal-title":"Clin. EEG Neurosci."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1016\/j.neurobiolaging.2004.03.008","article-title":"Decreased EEG synchronization in Alzheimer\u2019s disease and mild cognitive impairment","volume":"26","author":"Koenig","year":"2005","journal-title":"Neurobiol. Aging"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"342","DOI":"10.1016\/j.jneumeth.2006.10.023","article-title":"Application and comparison of classification algorithms for recognition of Alzheimer\u2019s disease in electrical brain activity (EEG)","volume":"161","author":"Lehmann","year":"2007","journal-title":"J. Neurosci. Methods"},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1159\/000067973","article-title":"Quantitative EEG abnormalities and cognitive dysfunctions in frontotemporal dementia and Alzheimer\u2019s disease","volume":"15","author":"Lindau","year":"2003","journal-title":"Dement. Geriatr. Cogn. Disord."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"2255","DOI":"10.1016\/j.clinph.2008.07.216","article-title":"Alzheimer disease versus mixed dementias: An EEG perspective","volume":"119","author":"Rousson","year":"2008","journal-title":"Clin. Neurophysiol. Off. J. Int. Fed. Clin. Neurophysiol."},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1034\/j.1600-0404.2003.02067.x","article-title":"EEG synchronization in mild cognitive impairment and Alzheimer\u2019s disease","volume":"108","author":"Stam","year":"2003","journal-title":"Acta Neurol. Scand."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"fcae227","DOI":"10.1093\/braincomms\/fcae227","article-title":"Data-driven retrieval of population-level EEG features and their role in neurodegenerative diseases","volume":"6","author":"Li","year":"2024","journal-title":"Brain Commun."},{"key":"ref_65","first-page":"730218","article-title":"Methods of EEG signal features extraction using linear analysis in frequency and time-frequency domains","volume":"2014","year":"2014","journal-title":"ISRN Neurosci."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1016\/B978-0-7020-5307-8.00019-3","article-title":"Review of delta, theta, alpha, beta, and gamma response oscillations in neuropsychiatric disorders","volume":"62","year":"2013","journal-title":"Suppl. Clin. Neurophysiol."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"102386","DOI":"10.1016\/j.isci.2021.102386","article-title":"EEG alterations during wake and sleep in mild cognitive impairment and Alzheimer\u2019s disease","volume":"24","author":"Scarpelli","year":"2021","journal-title":"iScience"},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Halgren, M., Fab\u00f3, D., Ulbert, I., Madsen, J.R., Er\u0151ss, L., Doyle, W.K., Devinsky, O., Schomer, D., Cash, S.S., and Halgren, E. (2018). Superficial Slow Rhythms Integrate Cortical Processing in Humans. Sci. Rep., 8.","DOI":"10.1038\/s41598-018-20662-0"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"23772","DOI":"10.1073\/pnas.1913092116","article-title":"The generation and propagation of the human alpha rhythm","volume":"116","author":"Halgren","year":"2019","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Heck, D.H., Fox, M.B., Correia Chapman, B., McAfee, S.S., and Liu, Y. (2023). Cerebellar control of thalamocortical circuits for cognitive function: A review of pathways and a proposed mechanism. Front. Syst. Neurosci., 17.","DOI":"10.3389\/fnsys.2023.1126508"},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Tanaka, M., Yamada, E., and Mori, F. (2024). Neurophysiological markers of early cognitive decline in older adults: A mini-review of electroencephalography studies for precursors of dementia. Front. Aging Neurosci., 16.","DOI":"10.3389\/fnagi.2024.1486481"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"499","DOI":"10.1016\/S0013-4694(97)00066-7","article-title":"EEG coherency. I: Statistics, reference electrode, volume conduction, Laplacians, cortical imaging, and interpretation at multiple scales","volume":"103","author":"Nunez","year":"1997","journal-title":"Electroencephalogr. Clin. Neurophysiol."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"41","DOI":"10.1016\/j.jneumeth.2007.06.026","article-title":"EEG and MEG coherence: Measures of functional connectivity at distinct spatial scales of neocortical dynamics","volume":"166","author":"Srinivasan","year":"2007","journal-title":"J. Neurosci. Methods"},{"key":"ref_74","doi-asserted-by":"crossref","unstructured":"Lord, V., and Opacka-Juffry, J. (2016). Electroencephalography (EEG) Measures of Neural Connectivity in the Assessment of Brain Responses to Salient Auditory Stimuli in Patients with Disorders of Consciousness. Front. Psychol., 7.","DOI":"10.3389\/fpsyg.2016.00397"},{"key":"ref_75","doi-asserted-by":"crossref","unstructured":"Radinskaia, D., and Radinski, C. (2022). EEG coherence as a marker of Alzheimer\u2019s disease. medRxiv, preprint.","DOI":"10.1101\/2022.07.24.22277966"},{"key":"ref_76","first-page":"1261","article-title":"Systematic Review of EEG Coherence in Alzheimer\u2019s Disease","volume":"91","author":"Fischer","year":"2022","journal-title":"J. Alzheimer\u2019s Dis. JAD 2023"},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Fodor, Z., Horv\u00e1th, A., Hidasi, Z., Gouw, A.A., Stam, C.J., and Csukly, G. (2021). EEG Alpha and Beta Band Functional Connectivity and Network Structure Mark Hub Overload in Mild Cognitive Impairment During Memory Maintenance. Front. Aging Neurosci., 13.","DOI":"10.3389\/fnagi.2021.680200"},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"3946","DOI":"10.1002\/sim.2978","article-title":"Comparison of the effect of volume conduction on EEG coherence with the effect of field spread on MEG coherence","volume":"26","author":"Winter","year":"2007","journal-title":"Stat. Med."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1007\/BF01129173","article-title":"Topographic mapping of the EEG: An examination of accuracy and precision","volume":"1","author":"Koles","year":"1988","journal-title":"Brain Topogr."},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Paitel, E.R., Otteman, C.B.D., Polking, M.C., Licht, H.J., and Nielson, K.A. (2025). Functional and effective EEG connectivity patterns in Alzheimer\u2019s disease and mild cognitive impairment: A systematic review. Front. Aging Neurosci., 17.","DOI":"10.3389\/fnagi.2025.1496235"},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1111\/j.1749-6632.1991.tb51573.x","article-title":"Computerized EEG frequency analysis and topographic brain mapping in Alzheimer\u2019s disease","volume":"620","author":"Mody","year":"1991","journal-title":"Ann. New York Acad. Sci."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"828.e19","DOI":"10.1016\/j.neurobiolaging.2011.06.024","article-title":"Functional connectivity tracks clinical deterioration in Alzheimer\u2019s disease","volume":"33","author":"Damoiseaux","year":"2012","journal-title":"Neurobiol. Aging"},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1016\/S0920-9964(00)00154-7","article-title":"Decreased functional connectivity of EEG theta-frequency activity in first-episode, neuroleptic-na\u00efve patients with schizophrenia: Preliminary results","volume":"50","author":"Koenig","year":"2001","journal-title":"Schizophr. Res."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"82","DOI":"10.1186\/s13195-014-0082-1","article-title":"Prevalence of mixed pathologies in the aging brain","volume":"6","author":"Rahimi","year":"2014","journal-title":"Alzheimer\u2019s Res. Ther."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1186\/s13024-019-0333-5","article-title":"The neuropathological diagnosis of Alzheimer\u2019s disease","volume":"14","author":"DeTure","year":"2019","journal-title":"Mol. Neurodegener."},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1093\/jnen\/nlab122","article-title":"Advances in Deep Neuropathological Phenotyping of Alzheimer Disease: Past, Present, and Future","volume":"81","author":"Shakir","year":"2022","journal-title":"J. Neuropathol. Exp. Neurol."},{"key":"ref_87","doi-asserted-by":"crossref","unstructured":"G\u00f3mez-Lombardi, A., Costa, B.G., Guti\u00e9rrez, P.P., Carvajal, P.M., Rivera, L.Z., and El-Deredy, W. (2024). The cognitive triad network-oscillation-behaviour links individual differences in EEG theta frequency with task performance and effective connectivity. Sci. Rep., 14.","DOI":"10.1038\/s41598-024-72229-x"},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"459","DOI":"10.1016\/j.biopsych.2021.06.005","article-title":"Neuroimaging Biomarkers of New-Onset Psychiatric Disorders Following Traumatic Brain Injury","volume":"91","author":"Mayer","year":"2022","journal-title":"Biol. Psychiatry"},{"key":"ref_89","unstructured":"Rayi, A., Asuncion, R.M.D., and Mandalaneni, K. (2024). Encephalopathic EEG Patterns. StatPearls, StatPearls Publishing."},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"123","DOI":"10.4103\/0028-3886.198192","article-title":"How to recognize and treat metabolic encephalopathy in Neurology intensive care unit","volume":"65","author":"Berisavac","year":"2017","journal-title":"Neurol. India"},{"key":"ref_91","doi-asserted-by":"crossref","unstructured":"Snipes, S., Krugliakova, E., Jaramillo, V., Volk, C., Furrer, M., Studler, M., LeBourgeois, M., Kurth, S., Jenni, O.G., and Huber, R. (2024). Wake EEG oscillation dynamics reflect both sleep need and brain maturation across childhood and adolescence. bioRxiv Serv. Biol., preprint.","DOI":"10.1101\/2024.02.24.581878"},{"key":"ref_92","doi-asserted-by":"crossref","unstructured":"Shen, K., McFadden, A., and McIntosh, A.R. (2021). Signal complexity indicators of health status in clinical EEG. Sci. Rep., 11.","DOI":"10.1038\/s41598-021-99717-8"},{"key":"ref_93","doi-asserted-by":"crossref","unstructured":"Asher, E.E., Plotnik, M., G\u00fcnther, M., Moshel, S., Levy, O., Havlin, S., Kantelhardt, J.W., and Bartsch, R.P. (2021). Connectivity of EEG synchronization networks increases for Parkinson\u2019s disease patients with freezing of gait. Commun. Biol., 4.","DOI":"10.1038\/s42003-021-02544-w"},{"key":"ref_94","doi-asserted-by":"crossref","unstructured":"Poil, S.S., de Haan, W., van der Flier, W.M., Mansvelder, H.D., Scheltens, P., and Linkenkaer-Hansen, K. (2013). Integrative EEG biomarkers predict progression to Alzheimer\u2019s disease at the MCI stage. Front. Aging Neurosci., 5.","DOI":"10.3389\/fnagi.2013.00058"},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"1718","DOI":"10.1016\/j.clinph.2011.02.011","article-title":"Differences in quantitative EEG between frontotemporal dementia and Alzheimer\u2019s disease as revealed by LORETA","volume":"122","author":"Nishida","year":"2011","journal-title":"Clin. Neurophysiol. Off. J. Int. Fed. Clin. Neurophysiol."},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1212\/WNL.0000000000004058","article-title":"Diagnosis and management of dementia with Lewy bodies: Fourth consensus report of the DLB Consortium","volume":"89","author":"McKeith","year":"2017","journal-title":"Neurology"},{"key":"ref_97","doi-asserted-by":"crossref","unstructured":"Iannaccone, S., Houdayer, E., Spina, A., Nocera, G., and Alemanno, F. (2023). Quantitative EEG for early differential diagnosis of dementia with Lewy bodies. Front. Psychol., 14.","DOI":"10.3389\/fpsyg.2023.1150540"},{"key":"ref_98","doi-asserted-by":"crossref","unstructured":"van der Zande, J.J., Gouw, A.A., van Steenoven, I., Scheltens, P., Stam, C.J., and Lemstra, A.W. (2018). EEG Characteristics of Dementia With Lewy Bodies, Alzheimer\u2019s Disease and Mixed Pathology. Front. Aging Neurosci., 10.","DOI":"10.3389\/fnagi.2018.00190"},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"1767","DOI":"10.1093\/brain\/awz069","article-title":"Dysfunctional brain dynamics and their origin in Lewy body dementia","volume":"142","author":"Schumacher","year":"2019","journal-title":"Brain A J. Neurol."},{"key":"ref_100","doi-asserted-by":"crossref","first-page":"206","DOI":"10.1159\/000495620","article-title":"EEG Resting-State Networks in Dementia with Lewy Bodies Associated with Clinical Symptoms","volume":"77","author":"Aoki","year":"2019","journal-title":"Neuropsychobiology"},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1159\/000381016","article-title":"Quantitative EEG Applying the Statistical Recognition Pattern Method: A Useful Tool in Dementia Diagnostic Workup","volume":"40","author":"Engedal","year":"2015","journal-title":"Dement. Geriatr. Cogn. Disord."},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"80","DOI":"10.1159\/000448394","article-title":"Electroencephalography Is a Good Complement to Currently Established Dementia Biomarkers","volume":"42","author":"Ferreira","year":"2016","journal-title":"Dement. Geriatr. Cogn. Disord."},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"1209","DOI":"10.1016\/j.clinph.2018.03.013","article-title":"Quantitative electroencephalography as a marker of cognitive fluctuations in dementia with Lewy bodies and an aid to differential diagnosis","volume":"129","author":"Stylianou","year":"2018","journal-title":"Clin. Neurophysiol. Off. J. Int. Fed. Clin. Neurophysiol."},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"569","DOI":"10.1007\/s00702-017-1699-6","article-title":"Differential diagnosis between patients with probable Alzheimer\u2019s disease, Parkinson\u2019s disease dementia, or dementia with Lewy bodies and frontotemporal dementia, behavioral variant, using quantitative electroencephalographic features","volume":"124","author":"Garn","year":"2017","journal-title":"J. Neural Transm."},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"1464","DOI":"10.3390\/eng5030078","article-title":"Machine and Deep Learning Trends in EEG-Based Detection and Diagnosis of Alzheimer\u2019s Disease: A Systematic Review","volume":"5","author":"Aviles","year":"2024","journal-title":"Eng"},{"key":"ref_106","doi-asserted-by":"crossref","first-page":"851","DOI":"10.2147\/NDT.S404528","article-title":"CNN for a Regression Machine Learning Algorithm for Predicting Cognitive Impairment Using qEEG","volume":"19","author":"Simfukwe","year":"2023","journal-title":"Neuropsychiatr. Dis. Treat."},{"key":"ref_107","doi-asserted-by":"crossref","unstructured":"Singh, S.G., Das, D., Barman, U., and Saikia, M.J. (2024). Early Alzheimer\u2019s Disease Detection: A Review of Machine Learning Techniques for Forecasting Transition from Mild Cognitive Impairment. Diagnostics, 14.","DOI":"10.3390\/diagnostics14161759"},{"key":"ref_108","doi-asserted-by":"crossref","unstructured":"Kim, N.H., Park, U., Yang, D.W., Choi, S.H., Youn, Y.C., and Kang, S.W. (2023). PET-validated EEG-machine learning algorithm predicts brain amyloid pathology in pre-dementia Alzheimer\u2019s disease. Sci. Rep., 13.","DOI":"10.1038\/s41598-023-36713-0"}],"container-title":["Diagnostics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2075-4418\/15\/12\/1509\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:51:43Z","timestamp":1760032303000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2075-4418\/15\/12\/1509"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,6,13]]},"references-count":108,"journal-issue":{"issue":"12","published-online":{"date-parts":[[2025,6]]}},"alternative-id":["diagnostics15121509"],"URL":"https:\/\/doi.org\/10.3390\/diagnostics15121509","relation":{},"ISSN":["2075-4418"],"issn-type":[{"value":"2075-4418","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,6,13]]}}}