{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T10:01:47Z","timestamp":1790071307368,"version":"4.0.1"},"reference-count":40,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2026,9,17]],"date-time":"2026-09-17T00:00:00Z","timestamp":1789603200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Electroencephalography (EEG) is an emerging biometric trait because it is difficult to steal or forge and requires acquisition from living subjects. However, many EEG-based person identification methods rely on large enrollment datasets, increasing acquisition burden and limiting practical deployment. To address this issue, we propose a data-efficient person identification framework based on time-interval-based multi-positive contrastive learning (TIB-MPCL). For each one-second EEG segment, overlapping, adjacent, and non-adjacent positive samples are constructed to exploit temporal diversity. These samples are jointly optimized through contrastive learning, together with an alignment module that promotes intra-subject compactness and inter-subject separability. Experiments on the 109-subject PhysioNet dataset demonstrate strong performance under both sufficient- and limited-sample conditions. TIB-MPCL achieves an average accuracy of 99.05% using all available training samples and 83.86% using only 10 one-second samples per subject, with accuracies above 91% for motor execution and imagery. Cross-session experiments on SEED-IV further evaluate robustness to session-dependent variability, achieving an average accuracy of 83.79% when the test session is completely excluded from training.<\/jats:p>","DOI":"10.3390\/s26185891","type":"journal-article","created":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T09:04:17Z","timestamp":1790067857000},"page":"5891","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Data-Efficient EEG-Based Person Identification via Time-Interval-Based Multi-Positive Contrastive Learning"],"prefix":"10.3390","volume":"26","author":[{"given":"Xinghan","family":"Shao","sequence":"first","affiliation":[{"name":"Key Laboratory of Child Development and Learning Science of Ministry of Education, School of Biological Science & Medical Engineering, Southeast University, Nanjing 211189, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"software","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"},{"role":"resources","vocabulary":"credit"},{"role":"data-curation","vocabulary":"credit"},{"role":"writing-original-draft","vocabulary":"credit"},{"role":"visualization","vocabulary":"credit"}]},{"given":"Haixian","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Child Development and Learning Science of Ministry of Education, School of Biological Science & Medical Engineering, Southeast University, Nanjing 211189, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"},{"role":"resources","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"supervision","vocabulary":"credit"},{"role":"project-administration","vocabulary":"credit"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7696-8024","authenticated-orcid":false,"given":"Zhengping","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Innovations, Sanjiang University, Nanjing 210012, China"},{"name":"School of Electronic Science and Engineering, Nanjing University, Nanjing 210023, China"}],"role":[{"vocabulary":"crossref","role":"author"},{"role":"conceptualization","vocabulary":"credit"},{"role":"methodology","vocabulary":"credit"},{"role":"validation","vocabulary":"credit"},{"role":"formal-analysis","vocabulary":"credit"},{"role":"investigation","vocabulary":"credit"},{"role":"resources","vocabulary":"credit"},{"role":"writing-review-editing","vocabulary":"credit"},{"role":"supervision","vocabulary":"credit"},{"role":"project-administration","vocabulary":"credit"}]}],"member":"1968","published-online":{"date-parts":[[2026,9,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"7166","DOI":"10.1109\/TIFS.2025.3587181","article-title":"DARN: A Dual Attention Refinement Network for Enhancing Feature Robustness in VEP-Based EEG Biometrics","volume":"20","author":"Liu","year":"2025","journal-title":"IEEE Trans. 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