{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T09:00:47Z","timestamp":1785315647558,"version":"3.55.0"},"reference-count":30,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T00:00:00Z","timestamp":1785283200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Key R&D Program of Shandong Province","award":["2023CXPT039"],"award-info":[{"award-number":["2023CXPT039"]}]},{"name":"Key R&D Program of Shandong Province","award":["2025CXGC010610"],"award-info":[{"award-number":["2025CXGC010610"]}]},{"DOI":"10.13039\/501100007129","name":"Shandong Provincial Natural Science Foundation","doi-asserted-by":"publisher","award":["ZR2021QH290"],"award-info":[{"award-number":["ZR2021QH290"]}],"id":[{"id":"10.13039\/501100007129","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Jinan Science and Technology Plan \u201cChallenge-Driven\u201d Project","award":["202428048"],"award-info":[{"award-number":["202428048"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Electroencephalogram (EEG)-based biometric sensing provides a promising pathway for secure and user-specific human authentication, but practical deployment remains limited by cross-session non-stationarity and potentially optimistic evaluation protocols caused by window- or event-level leakage. This study developed a cross-session EEG biometric sensing framework using interpretable spectral features and target-session calibration, with trial-level non-overlapping data partitioning to prevent overlap between training, calibration, and blind-test sets. Multi-channel EEG events were segmented into non-overlapping 2 s windows and aggregated into event-level samples to ensure strict isolation among training, validation, calibration, and blind testing subsets. Power spectral density, differential entropy, and log-variance features were evaluated using support vector machine, linear discriminant analysis, logistic regression, random forest, and a compact convolutional neural network designed for EEG decoding (EEGNet). Both closed-set identification and biometric verification were assessed using Rank-N accuracy, cumulative match characteristic curves, macro-F1, equal error rate, area under the curve, and true acceptance rate (TAR) at fixed false acceptance rate (FAR) levels. In the in-house cross-day dataset, limited target-session calibration improved Rank-1 accuracy from 44.82% to 98.70% for support vector machine (SVM) and from 19.93% to 99.94% for linear discriminant analysis (LDA). SVM with differential entropy achieved an equal error rate (EER) of 1.04 \u00b1 0.60%, area under the receiver operating characteristic curve (AUC) of 0.9985 \u00b1 0.0008, and TAR@FAR = 0.1% of 98.44 \u00b1 1.06%. External validation on the public multi-session motor imagery dataset confirmed the calibration benefit. These results demonstrate strong closed-set identification and score-level verification performance under a cohort-wide, calibration-assisted cross-session experimental protocol.<\/jats:p>","DOI":"10.3390\/s26154811","type":"journal-article","created":{"date-parts":[[2026,7,29]],"date-time":"2026-07-29T08:16:52Z","timestamp":1785313012000},"page":"4811","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Interpretable Spectral Features for Cross-Session EEG Biometric Identification and Verification"],"prefix":"10.3390","volume":"26","author":[{"given":"Cai","family":"Chen","sequence":"first","affiliation":[{"name":"Shandong Institute of Advanced Technology, Chinese Academy of Sciences, Jinan 250100, China"},{"name":"State Key Laboratory of Environment Health (Incubation), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, #13 Hangkong Road, Wuhan 430030, China"},{"name":"Key Laboratory of Environment and Health, Ministry of Education, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, #13 Hangkong Road, Wuhan 430030, China"},{"name":"Key Laboratory of Environment and Health (Wuhan), Ministry of Environmental Protection, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, #13 Hangkong Road, Wuhan 430030, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiazheng","family":"Sun","sequence":"additional","affiliation":[{"name":"Shandong Institute of Advanced Technology, Chinese Academy of Sciences, Jinan 250100, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Danyang","family":"Lv","sequence":"additional","affiliation":[{"name":"Shandong Institute of Advanced Technology, Chinese Academy of Sciences, Jinan 250100, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chongxuan","family":"Tian","sequence":"additional","affiliation":[{"name":"School of Control Science and Engineering, Shandong University, Jinan 250100, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuxian","family":"Li","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Environment Health (Incubation), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, #13 Hangkong Road, Wuhan 430030, China"},{"name":"Key Laboratory of Environment and Health, Ministry of Education, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, #13 Hangkong Road, Wuhan 430030, China"},{"name":"Key Laboratory of Environment and Health (Wuhan), Ministry of Environmental Protection, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, #13 Hangkong Road, Wuhan 430030, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ningling","family":"Zhang","sequence":"additional","affiliation":[{"name":"Shandong Institute of Advanced Technology, Chinese Academy of Sciences, Jinan 250100, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tao","family":"Jing","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Environment Health (Incubation), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, #13 Hangkong Road, Wuhan 430030, China"},{"name":"Key Laboratory of Environment and Health, Ministry of Education, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, #13 Hangkong Road, Wuhan 430030, China"},{"name":"Key Laboratory of Environment and Health (Wuhan), Ministry of Environmental Protection, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, #13 Hangkong Road, Wuhan 430030, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,7,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"e70148","DOI":"10.1111\/crj.70148","article-title":"Rapid Nasal Breathing as a Biometric Trigger: High-Accuracy Electroencephalogram-Based Authentication for Clinical Applications","volume":"20","author":"Chen","year":"2026","journal-title":"Clin. 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