{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,4]],"date-time":"2026-11-04T23:45:56Z","timestamp":1793835956813,"version":"4.6.1"},"reference-count":33,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T00:00:00Z","timestamp":1785369600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Neurosci."],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Electroencephalography (EEG)-based classification of Alzheimer's disease (AD) and frontotemporal dementia (FTD) relative to cognitively normal (CN) controls is commonly interpreted through stable disease-related patterns. However, classification-relevant EEG responses in computational models may also appear fragmented or weakly preserved, making subject-level reliability difficult to assess. This study investigated whether recurrent disorder-like EEG patterns can provide exploratory evidence of subject-wise discriminative organization in dementia classification.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>We analyzed a publicly available resting-state EEG dataset including AD, FTD, and CN subjects. Clustered Pattern Projection (CPP) was applied to Dynamic Mode Decomposition (DMD)-based epoch descriptors to construct prototype-based EEG representations. Classification was performed using a linear support vector machine under a nested leave-one-subject-out cross-validation (LOSO-CV) framework. Subject-level reliability was further examined using margin-based analysis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>CPP showed competitive subject-level performance, particularly in the FTD vs. CN classification task, a setting in which resting-state discriminative patterns are often less consistently preserved than in AD. In addition, task-dependent subject-level margin patterns were observed under strict subject-wise validation, suggesting that margin analysis may provide a useful exploratory tool for evaluating the reliability of learned EEG representations.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>These findings suggest that EEG generalization in dementia classification should not be interpreted only through preserved canonical biomarkers. Instead, recurrent disorder-like patterns may contribute to computationally detectable decision structure, and CPP provides a framework for examining such patterns under strict subject-wise validation.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fnins.2026.1871265","type":"journal-article","created":{"date-parts":[[2026,7,30]],"date-time":"2026-07-30T05:39:26Z","timestamp":1785389966000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["Clustered pattern projection for EEG dementia classification: evaluating the reliability of disorder patterns"],"prefix":"10.3389","volume":"20","author":[{"given":"Hunseok","family":"Kang","sequence":"first","affiliation":[{"name":"College of Engineering and Technology, American University of the Middle East","place":["Egaila, Kuwait"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jacob","family":"Kang","sequence":"additional","affiliation":[{"name":"Fischell Department of Bioengineering, University of Maryland","place":["College Park, MD, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mustafa","family":"Zeki","sequence":"additional","affiliation":[{"name":"College of Engineering and Technology, American University of the Middle East","place":["Egaila, Kuwait"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jong-Hyeon","family":"Seo","sequence":"additional","affiliation":[{"name":"School of Basic Sciences, Hanbat National University","place":["Daejeon, Republic of Korea"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,7,30]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"1464","DOI":"10.3390\/eng5030078","article-title":"Machine and deep learning trends in EEG-based detection and diagnosis of Alzheimer's disease: a systematic review","volume":"5","author":"Aviles","year":"2024","journal-title":"Eng"},{"key":"B2","doi-asserted-by":"publisher","first-page":"1373515","DOI":"10.3389\/fnins.2024.1373515","article-title":"Data leakage in deep learning studies of translational EEG","volume":"18","author":"Brookshire","year":"2024","journal-title":"Front. 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