{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,13]],"date-time":"2026-08-13T13:35:28Z","timestamp":1786628128760,"version":"build-2736575974"},"reference-count":50,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2026,8,13]],"date-time":"2026-08-13T00:00:00Z","timestamp":1786579200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004731","name":"Zhejiang Provincial Natural Science Foundation of China","doi-asserted-by":"publisher","award":["LZ25F010006"],"award-info":[{"award-number":["LZ25F010006"]}],"id":[{"id":"10.13039\/501100004731","id-type":"DOI","asserted-by":"publisher"}]},{"award":["LZ25F010006"],"award-info":[{"award-number":["LZ25F010006"]}],"id":[{"id":"https:\/\/ror.org\/04aqat463","id-type":"ROR","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62371171"],"award-info":[{"award-number":["62371171"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62371178"],"award-info":[{"award-number":["62371178"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62271181"],"award-info":[{"award-number":["62271181"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"award":["62371171"],"award-info":[{"award-number":["62371171"]}],"id":[{"id":"https:\/\/ror.org\/01h0zpd94","id-type":"ROR","asserted-by":"publisher"}]},{"award":["62371178"],"award-info":[{"award-number":["62371178"]}],"id":[{"id":"https:\/\/ror.org\/01h0zpd94","id-type":"ROR","asserted-by":"publisher"}]},{"award":["62271181"],"award-info":[{"award-number":["62271181"]}],"id":[{"id":"https:\/\/ror.org\/01h0zpd94","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Brain Sci.","Brain Sciences"],"abstract":"<jats:p>Background\/Objectives: Electroencephalography (EEG) connectivity and microstate analysis have shown great potential for Alzheimer\u2019s disease (AD) diagnosis; however, their clinical application remains limited by the low spatial resolution of EEG and the lack of standardized microstate analysis. To address these challenges, this study proposes a multi-domain feature fusion framework, namely Source-localized Microstate and Multi-frequency Synchronization (SMMS), which integrates EEG source localization (ESL)-based weighted phase lag index (wPLI) functional connectivity with EEG microstate features. Methods: Specifically, ESL was employed to improve the spatial resolution of EEG signals for constructing functional connectivity matrices, while multi-frequency-band wPLI features were extracted to characterize functional synchronization among cortical regions. Meanwhile, EEG microstate features were utilized to capture the temporal dynamics of brain functional states. The proposed framework was evaluated on a public OpenNeuro dataset comprising 36 AD patients, 23 frontotemporal dementia (FTD) patients, and 29 healthy controls (HCs), as well as an additional clinical dataset collected from 48 AD patients at Sir Run Run Shaw Hospital, Hangzhou, China. Results: Experimental results showed that the proposed SMMS framework achieved high classification performance on both datasets. Conclusions: These findings demonstrate its effectiveness for EEG-based Alzheimer\u2019s disease diagnosis.<\/jats:p>","DOI":"10.3390\/brainsci16080856","type":"journal-article","created":{"date-parts":[[2026,8,13]],"date-time":"2026-08-13T12:47:11Z","timestamp":1786625231000},"page":"856","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Alzheimer\u2019s Disease Detection Using Combined EEG Source Connectivity and Microstate Features"],"prefix":"10.3390","volume":"16","author":[{"given":"Lu","family":"Huang","sequence":"first","affiliation":[{"name":"HDU-ITMO Joint Institute, Hangzhou Dianzi University, Hangzhou 310005, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zheng","family":"Hu","sequence":"additional","affiliation":[{"name":"School of Automation, Hangzhou Dianzi University, Hangzhou 310005, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhengnan","family":"Zhang","sequence":"additional","affiliation":[{"name":"HDU-ITMO Joint Institute, Hangzhou Dianzi University, Hangzhou 310005, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2128-2185","authenticated-orcid":false,"given":"Yunyuan","family":"Gao","sequence":"additional","affiliation":[{"name":"School of Automation, Hangzhou Dianzi University, Hangzhou 310005, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,8,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"102417","DOI":"10.1016\/j.arr.2024.102417","article-title":"Neuropsychological and electrophysiological measurements for diagnosis and prediction of dementia: A review on machine learning approach","volume":"100","author":"Carrarini","year":"2024","journal-title":"Ageing Res. Rev."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1262","DOI":"10.1016\/S0140-6736(06)68542-5","article-title":"Mild cognitive impairment","volume":"367","author":"Gauthier","year":"2006","journal-title":"Lancet"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1253","DOI":"10.3233\/JAD-200171","article-title":"Classification of Alzheimer\u2019s disease with respect to physiological aging with innovative EEG biomarkers in a machine learning implementation","volume":"75","author":"Vecchio","year":"2020","journal-title":"J. Alzheimer\u2019s Dis."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Wu, Z., Gao, Y., Potter, T., Benoit, J., Shen, J., Schulz, P.E., Zhang, Y., and The Alzheimer\u2019s Disease Neuroimaging Initiative (2021). Interactions between aging and Alzheimer\u2019s disease on structural brain networks. Front. Aging Neurosci., 13.","DOI":"10.3389\/fnagi.2021.639795"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"204","DOI":"10.1136\/jnnp-2020-323520","article-title":"Fluid biomarkers in frontotemporal dementia: Past, present and future","volume":"92","author":"Swift","year":"2021","journal-title":"J. Neurol. Neurosurg. Psychiatry"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"108618","DOI":"10.1016\/j.jneumeth.2020.108618","article-title":"An EEG-fNIRS hybridization technique in the four-class classification of alzheimer\u2019s disease","volume":"336","author":"Cicalese","year":"2020","journal-title":"J. Neurosci. Methods"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"83","DOI":"10.1016\/j.neubiorev.2019.07.021","article-title":"Depression biomarkers using non-invasive EEG: A review","volume":"105","author":"Rosa","year":"2019","journal-title":"Neurosci. Biobehav. Rev."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1109\/TNSRE.2015.2502323","article-title":"Improved neural signal classification in a rapid serial visual presentation task using active learning","volume":"24","author":"Marathe","year":"2016","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"17627","DOI":"10.1038\/s41598-020-74790-7","article-title":"EEG microstate complexity for aiding early diagnosis of Alzheimer\u2019s disease","volume":"10","author":"Tait","year":"2020","journal-title":"Sci. Rep."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"527","DOI":"10.1109\/TII.2018.2868431","article-title":"Brain network analysis of compressive sensed high-density EEG signals in AD and MCI subjects","volume":"15","author":"Mammone","year":"2019","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1016\/j.neubiorev.2014.12.010","article-title":"Microstates in resting-state EEG: Current status and future directions","volume":"49","author":"Khanna","year":"2015","journal-title":"Neurosci. Biobehav. Rev."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"120371","DOI":"10.1016\/j.neuroimage.2023.120371","article-title":"Towards a dynamical understanding of microstate analysis of M\/EEG data","volume":"281","author":"Jajcay","year":"2023","journal-title":"NeuroImage"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"2030","DOI":"10.1038\/s41386-020-0749-1","article-title":"Abnormalities in electroencephalographic microstates are state and trait markers of major depressive disorder","volume":"45","author":"Murphy","year":"2020","journal-title":"Neuropsychopharmacology"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"4779","DOI":"10.1007\/s11357-024-01181-5","article-title":"Abnormal EEG microstates in Alzheimer\u2019s disease: Predictors of \u03b2-amyloid deposition degree and disease classification","volume":"46","author":"Yan","year":"2024","journal-title":"GeroScience"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"bhae043","DOI":"10.1093\/cercor\/bhae043","article-title":"Functional connectivity key feature analysis of cognitive impairment patients based on microstate brain network","volume":"34","author":"Li","year":"2024","journal-title":"Cereb. Cortex"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"101622","DOI":"10.1016\/j.nicl.2018.101622","article-title":"Dynamic cortical connectivity alterations associated with Alzheimer\u2019s disease: An EEG and fNIRS integration study","volume":"21","author":"Li","year":"2019","journal-title":"NeuroImage Clin."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"066019","DOI":"10.1088\/1741-2552\/ab4024","article-title":"Computational modeling of the effects of EEG volume conduction on functional connectivity metrics. Application to Alzheimer\u2019s disease continuum","volume":"16","author":"Hornero","year":"2019","journal-title":"J. Neural Eng."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"4521","DOI":"10.1109\/TNSRE.2023.3329174","article-title":"Differentiating between Alzheimer\u2019s Disease and Frontotemporal Dementia Based on the Resting-State Multilayer EEG Network","volume":"31","author":"Si","year":"2023","journal-title":"IEEE Trans. Neural Syst. Rehabil. Eng."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"e089546","DOI":"10.1002\/alz.089546","article-title":"Association of sleep network functional connectivity with hyperexcitability and cognition in Alzheimer\u2019s disease","volume":"20","author":"Moguilner","year":"2025","journal-title":"Alzheimer\u2019s Dement."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1007\/s10548-023-00958-9","article-title":"The functional aspects of resting EEG microstates: A systematic review","volume":"37","author":"Tarailis","year":"2024","journal-title":"Brain Topogr."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1016\/j.neuroimage.2003.12.044","article-title":"Geometrical interpretation of fMRI-guided MEG\/EEG inverse estimates","volume":"22","author":"Ahlfors","year":"2004","journal-title":"NeuroImage"},{"key":"ref_22","first-page":"108287","article-title":"Deep learning techniques for automated Alzheimer\u2019s disease and mild cognitive impairment detection using EEG signals: A review","volume":"254","author":"Acharya","year":"2025","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Yuan, Y., and Zhao, J. (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_24","first-page":"158","article-title":"EEG biomarkers in Alzheimer\u2019s disease and prodromal Alzheimer\u2019s disease: A systematic review","volume":"16","author":"Chetty","year":"2024","journal-title":"Alzheimer\u2019s Res. Ther."},{"key":"ref_25","unstructured":"Jiao, B., Liu, Y., Li, X., Zhou, Y., Wang, J., Zhang, X., Chen, S., Yang, M., Zhao, Q., and Sun, Z. (2023). Neural Decoding and Biomarker Discovery for Alzheimer\u2019s Disease and Mild Cognitive Impairment Based on Resting-State EEG. Front. Neurosci., 17."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"036017","DOI":"10.1088\/1741-2552\/aa9fb2","article-title":"Characterization of dynamic changes of current source localization based on spatiotemporal fMRI constrained EEG source imaging","volume":"15","author":"Nguyen","year":"2018","journal-title":"J. Neural Eng."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"518","DOI":"10.1016\/j.bja.2020.05.068","article-title":"Alpha band frontal connectivity is a state-specific electroencephalographic correlate of unresponsiveness during exposure to dexmedetomidine and propofol","volume":"125","author":"Valli","year":"2020","journal-title":"Br. J. Anaesth."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Miltiadous, A., Tzimourta, K.D., Afrantou, T., Ioannidis, P., Grigoriadis, N., Tsalikakis, D.G., Angelidis, P., Tsipouras, M.G., Glavas, E., and Giannakeas, N. (2023). A dataset of scalp EEG recordings of Alzheimer\u2019s disease, frontotemporal dementia and healthy subjects from routine EEG. Data, 8.","DOI":"10.3390\/data8060095"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1186\/s13195-015-0161-y","article-title":"Neurogranin and YKL-40: Independent markers of synaptic degeneration and neuroinflammation in Alzheimer\u2019s disease","volume":"7","author":"Hellwig","year":"2015","journal-title":"Alzheimer\u2019s Res. Ther."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1114","DOI":"10.1109\/TBME.2019.2930186","article-title":"Evaluation of artifact subspace reconstruction for automatic artifact components removal in multi-channel EEG recordings","volume":"67","author":"Chang","year":"2020","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"287","DOI":"10.1016\/0165-1684(94)90029-9","article-title":"Independent component analysis, a new concept?","volume":"36","author":"Comon","year":"1994","journal-title":"Signal Process."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"1513","DOI":"10.1016\/j.neuroimage.2006.02.027","article-title":"A cortical potential imaging study from simultaneous extra- and intracranial electrical recordings by means of the finite element method","volume":"31","author":"Zhang","year":"2006","journal-title":"NeuroImage"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"2975","DOI":"10.1088\/0031-9155\/49\/13\/014","article-title":"A second-order finite element algorithm for solving the three-dimensional EEG forward problem","volume":"49","author":"Zhang","year":"2004","journal-title":"Phys. Med. Biol."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1016\/j.neuroimage.2013.04.090","article-title":"Scalp and skull influence on near infrared photon propagation in the Colin27 brain template","volume":"85","author":"Strangman","year":"2014","journal-title":"Neuroimage"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"030802","DOI":"10.1115\/1.4005491","article-title":"Recent advances and emerging applications of the boundary element method","volume":"64","author":"Liu","year":"2011","journal-title":"Appl. Mech. Rev."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"45","DOI":"10.1186\/1475-925X-9-45","article-title":"OpenMEEG: Opensource software for quasistatic bioelectromagnetics","volume":"9","author":"Gramfort","year":"2010","journal-title":"Biomed. Eng. Online"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"25","DOI":"10.1186\/1743-0003-5-25","article-title":"Review on solving the inverse problem in EEG source analysis","volume":"5","author":"Grech","year":"2008","journal-title":"J. Neuroeng. Rehabil."},{"key":"ref_38","first-page":"5","article-title":"Standardized low-resolution brain electromagnetic tomography (sLORETA): Technical details","volume":"24","year":"2002","journal-title":"Methods Find. Exp. Clin. Pharmacol."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/S1361-8415(01)80026-8","article-title":"A survey of medical image registration","volume":"2","author":"Maintz","year":"1998","journal-title":"Med. Image Anal."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1016\/j.neuroimage.2004.09.036","article-title":"Estimation of the cortical functional connectivity with the multimodal integration of high-resolution EEG and fMRI data by directed transfer function","volume":"24","author":"Babiloni","year":"2005","journal-title":"Neuroimage"},{"key":"ref_41","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_42","doi-asserted-by":"crossref","first-page":"1548","DOI":"10.1016\/j.neuroimage.2011.01.055","article-title":"An improved index of phase-synchronization for electrophysiological data in the presence of volume-conduction, noise and sample-size bias","volume":"55","author":"Vinck","year":"2011","journal-title":"Neuroimage"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"116454","DOI":"10.1016\/j.neuroimage.2019.116454","article-title":"EEG microstates as a continuous phenomenon","volume":"208","author":"Mishra","year":"2020","journal-title":"Neuroimage"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"503","DOI":"10.1016\/j.datak.2007.03.016","article-title":"A k-mean clustering algorithm for mixed numeric and categorical data","volume":"63","author":"Ahmad","year":"2007","journal-title":"Data Knowl. Eng."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1007\/s004060050088","article-title":"A deviant EEG brain microstate in acute, neuroleptic-naive schizophrenics at rest","volume":"249","author":"Koenig","year":"1999","journal-title":"Eur. Arch. Psychiatry Clin. Neurosci."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1023\/A:1025667309714","article-title":"Theoretical and empirical analysis of ReliefF and RReliefF","volume":"53","author":"Kononenko","year":"2003","journal-title":"Mach. Learn."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Lal, U., Chikkankod, A.V., and Longo, L. (2024). A Comparative Study on Feature Extraction Techniques for the Discrimination of Frontotemporal Dementia and Alzheimer\u2019s Disease with Electroencephalography in Resting-State Adults. Brain Sci., 14.","DOI":"10.3390\/brainsci14040335"},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Chen, Y., Wang, H., Zhang, D., Zhang, L., and Tao, L. (2023). Multi-feature fusion learning for Alzheimer\u2019s disease prediction using EEG signals in resting state. Front. Neurosci., 17.","DOI":"10.3389\/fnins.2023.1272834"},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"1490","DOI":"10.1016\/j.clinph.2004.01.001","article-title":"EEG dynamics in patients with Alzheimer\u2019s disease","volume":"115","author":"Jeong","year":"2004","journal-title":"Clin. Neurophysiol."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"751","DOI":"10.1007\/s11357-023-01041-8","article-title":"The effect of aperiodic components in distinguishing Alzheimer\u2019s disease from frontotemporal dementia","volume":"46","author":"Wang","year":"2024","journal-title":"GeroScience"}],"container-title":["Brain Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2076-3425\/16\/8\/856\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,13]],"date-time":"2026-08-13T12:55:39Z","timestamp":1786625739000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2076-3425\/16\/8\/856"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,13]]},"references-count":50,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2026,8]]}},"alternative-id":["brainsci16080856"],"URL":"https:\/\/doi.org\/10.3390\/brainsci16080856","relation":{},"ISSN":["2076-3425"],"issn-type":[{"value":"2076-3425","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,13]]}}}