{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,7]],"date-time":"2026-09-07T06:08:11Z","timestamp":1788761291315,"version":"build-2803163510"},"reference-count":35,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,7]],"date-time":"2026-09-07T00:00:00Z","timestamp":1788739200000},"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. Aging Neurosci."],"abstract":"<jats:p>\n                    Alzheimer's disease (AD) is associated with progressive cognitive decline and altered brain functional activity, yet objective and interpretable electrophysiological indicators remain insufficiently established. Resting-state electroencephalography (EEG) offers a low-cost and clinically accessible candidate, provided that the analysis is validated at the subject level and remains interpretable. This study evaluated an interpretable resting-state EEG framework for distinguishing AD patients from healthy control (HC) subjects. A total of 63 participants (36 AD and 27 HC) from a publicly available dataset were included. Subject-level spectral and nonlinear complexity features were extracted from 19 preprocessed scalp channels; missing-value imputation, feature screening, redundancy pruning, scaling, and model fitting were carried out within each fold of leave-one-subject-out cross-validation. Four linear classifiers were compared, and Ridge Logistic regression was retained for out-of-fold SHAP interpretation because of its balanced hard-label performance and direct compatibility with Linear SHAP. Ridge Logistic regression achieved an exploratory AUC of 0.912 (accuracy = 0.857, sensitivity = 0.778, specificity = 0.963) under a non-nested validation design. Across 30 independently balanced epoch resamples, mean AUC was 0.887 \u00b1 0.020; using all accepted epochs yielded AUC = 0.917. SHAP analysis indicated that the classifier drew jointly on posterior \u03b1 activity, frontal and temporal \u03b8 power, the \u03b8\/\u03b1 ratio, and slow\/fast ratio features. A classifier-independent microstate analysis revealed reduced putative Class B occurrence, increased putative Class C and Class D duration, and six FDR-corrected off-diagonal transition differences in AD; the three temporal effects persisted across repeated\n                    <jats:italic>K<\/jats:italic>\n                    = 4 initializations and matched\n                    <jats:italic>K<\/jats:italic>\n                    = 3\u20136 solutions. These findings suggest that resting-state EEG can provide non-invasive and interpretable information about AD-related functional alterations, pending validation in larger, independent cohorts.\n                  <\/jats:p>","DOI":"10.3389\/fnagi.2026.1900857","type":"journal-article","created":{"date-parts":[[2026,9,7]],"date-time":"2026-09-07T05:33:17Z","timestamp":1788759197000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Explainable machine learning for Alzheimer's disease characterization using small-sample EEG data"],"prefix":"10.3389","volume":"18","author":[{"given":"Lang","family":"Shen","sequence":"first","affiliation":[{"name":"Laboratory of Intelligent Brain Neuroimaging, Peking University Advanced Institute of Information Technology (AIIT)","place":["Hangzhou, Zhejiang, China"]},{"name":"School of Statistics and Data Science, Zhejiang Gongshang University","place":["Hangzhou, Zhejiang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wei","family":"Tong","sequence":"additional","affiliation":[{"name":"Laboratory of Intelligent Brain Neuroimaging, Peking University Advanced Institute of Information Technology (AIIT)","place":["Hangzhou, Zhejiang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ye","family":"Zhao","sequence":"additional","affiliation":[{"name":"Laboratory of Intelligent Brain Neuroimaging, Peking University Advanced Institute of Information Technology (AIIT)","place":["Hangzhou, Zhejiang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bicheng","family":"Wu","sequence":"additional","affiliation":[{"name":"Laboratory of Intelligent Brain Neuroimaging, Peking University Advanced Institute of Information Technology (AIIT)","place":["Hangzhou, Zhejiang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peng","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Psychiatry, Affiliated Xiaoshan Hospital, Hangzhou Normal University","place":["Hangzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jing","family":"Kan","sequence":"additional","affiliation":[{"name":"Laboratory of Intelligent Brain Neuroimaging, Peking University Advanced Institute of Information Technology (AIIT)","place":["Hangzhou, Zhejiang, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,9,7]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"580","DOI":"10.3390\/cimb47080580","article-title":"From amyloid to synaptic dysfunction: biomarker-driven insights into Alzheimer's disease","volume":"47","author":"Agnello","year":"2025","journal-title":"Curr. 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