An Interpretable Contrastive Learning Transformer for EEG-Based Person Identification

作者：Xinghan Shao, C. Chang, John Q. Gan, Haixian Wang · 发表于：IEEE Transactions on Information Forensics and Security · 年份：2025 · DOI：10.1109/tifs.2025.3570183 · 被引用次数：7 · 研究领域：Gait Recognition and Analysis、Emotion and Mood Recognition、Biometric Identification and Security

Research on electroencephalogram (EEG)-based person identification is increasing because EEG signals must be collected from the living body, making them difficult to steal or alter. However, EEG signals are greatly influenced by subjects’ states, and most studies on EEG-based person identification have overlooked this influence. In this study, we proposed an interpretable contrastive learning transformer to tackle the impact of state changes on EEG-based person identification. Contrastive learning transformers construct pairs of EEG signal feature samples to capture state-independent and identity-distinct features. Specifically, the power spectral density (PSD) of EEG signals from the same user in different paradigms is used as positive samples, while the PSD from other users is used as negative samples. Pairs of samples are encoded to obtain corresponding features and then projected into a contrastive space through a multi-layer perceptron. Then, the NT-Xent loss function minimizes the distance between positive samples within the same batch and maximizes the distance between negative samples. Finally, to eliminate bias between positive sample pairs from different paradigms, we introduced the cross-paradigm alignment loss for the first time to capture individual consistency. We evaluated our model on two datasets. Dataset 1 contains EEG signals from 109 individuals, recorded across multiple paradigms designed to elicit different states. Dataset 2 consists of EEG signals from ...