{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,4]],"date-time":"2026-09-04T05:15:42Z","timestamp":1788498942640,"version":"build-2803163510"},"reference-count":17,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2026,8,31]],"date-time":"2026-08-31T00:00:00Z","timestamp":1788134400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Funded by the European Union \u2013 NextGenerationEU","award":["SDAFEM \u2013 uniri-mz-25-30"],"award-info":[{"award-number":["SDAFEM \u2013 uniri-mz-25-30"]}]},{"award":["SDAFEM \u2013 uniri-mz-25-30"],"award-info":[{"award-number":["SDAFEM \u2013 uniri-mz-25-30"]}],"id":[{"id":"https:\/\/ror.org\/019w4f821","id-type":"ROR","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Alzheimer\u2019s disease (AD) and frontotemporal dementia (FTD) are neurodegenerative disorders with partially overlapping clinical manifestations, making early and differential diagnosis challenging. This study investigated whether electroencephalography (EEG)-derived spectral features and Granger-causality (GC)-based directed functional connectivity features can characterize and classify AD, FTD, and healthy control (HC) subjects. Resting-state eyes-closed EEG recordings from 88 participants were analyzed, including 36 AD, 23 FTD, and 29 HC subjects. Spectral features included absolute and relative band power and spectral ratios, while directed connectivity features were extracted from broadband and frequency-specific GC matrices. Statistical analyses identified theta\/alpha ratio (TAR) as the dominant spectral marker, with the strongest three-group differences observed in frontal and global TAR features. GC analysis revealed group-related alterations mainly in alpha-band regional directed connectivity, although three-group GC features did not survive false discovery rate (FDR) correction at q &lt; 0.05. In the main nested cross-validation analysis, the spectral-only model achieved the best three-class performance, with balanced accuracy of 0.572 and macro-F1 of 0.557. For dementia group (DEM) vs. HC classification, the combined GC + spectral feature (GC + SPEC) set achieved balanced accuracy of 0.710 and macro-F1 of 0.665. For AD vs. FTD classification, the combined GC + SPEC feature set achieved the highest numerical performance in the main nested cross-validation (CV) comparison, with balanced accuracy of 0.584 and macro-F1 of 0.559. In the separate long permutation-testing analysis, which used a reduced hyperparameter grid for computational feasibility, above-chance performance was confirmed for the three-class spectral model and the DEM vs. HC GC + SPEC model (p &lt; 0.001), but not for AD vs. FTD (p = 0.270). An exploratory photobiomodulation (PBM) single-case analysis showed longitudinal EEG reorganization, including increased alpha power, reduced delta\/alpha ratio (DAR) and beta\/alpha ratio (BAR), mixed TAR changes, and HC-like GC\/GC + SPEC centroid projections. Overall, the results support the value of spectral and directed connectivity EEG markers for dementia-related EEG characterization, while highlighting the persistent difficulty of AD vs. FTD differentiation.<\/jats:p>","DOI":"10.3390\/s26175530","type":"journal-article","created":{"date-parts":[[2026,9,1]],"date-time":"2026-09-01T03:19:26Z","timestamp":1788232766000},"page":"5530","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Spectral and Directed Connectivity EEG Markers for Classifying Alzheimer\u2019s Disease, Frontotemporal Dementia, and Healthy Controls with Exploratory Photobiomodulation Case-Study Projection"],"prefix":"10.3390","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3461-963X","authenticated-orcid":false,"given":"Zoran","family":"\u0160verko","sequence":"first","affiliation":[{"name":"Department of Electric Power Systems, Faculty of Engineering, University of Rijeka, 51000 Rijeka, Croatia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0266-9253","authenticated-orcid":false,"given":"Sa\u0161a","family":"Vlahini\u0107","sequence":"additional","affiliation":[{"name":"Department of Automation and Electronics, Faculty of Engineering, University of Rijeka, 51000 Rijeka, Croatia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1036-9261","authenticated-orcid":false,"given":"Miroslav","family":"Vranki\u0107","sequence":"additional","affiliation":[{"name":"Department of Automation and Electronics, Faculty of Engineering, University of Rijeka, 51000 Rijeka, Croatia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9343-1739","authenticated-orcid":false,"given":"Nino","family":"Stojkovi\u0107","sequence":"additional","affiliation":[{"name":"Department of Electric Power Systems, Faculty of Engineering, University of Rijeka, 51000 Rijeka, Croatia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,8,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Ntetska, A., Miltiadous, A., Tsipouras, M.G., Tzimourta, K.D., Afrantou, T., Ioannidis, P., Tsalikakis, D.G., Sakkas, K., Oikonomou, E.D., and Grigoriadis, N. 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