Authors
Xinghan Shao, Haixian Wang, Zhengping Wu
Published in
Sensors (Basel, Switzerland). Volume 26. Issue 18. Sep 17, 2026. Epub Sep 17, 2026.
Abstract
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.
PMID:
42817498
Bibliographic data and abstract were imported from PubMed on 01 Oct 2026.
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