EEG-based person identification enables applications in security, personalized brain-computer interfaces (BCIs), and cognitive monitoring. However, existing techniques often rely on deep learning architectures at high computational cost, limiting their scope of applications. In this study, we propose a novel EEG person identification approach using spiking neural networks (SNNs) with a lightweight spiking transformer for efficiency and effectiveness. The proposed SNN model is capable of handling the temporal complexities inherent in EEG signals. On the EEG-Music Emotion Recognition Challenge dataset, the proposed model achieves 100% classification accuracy with less than 10% energy consumption of traditional deep neural networks. This study offers a promising direction for energy-efficient and high-performance BCIs. The source code is available at https://github.com/PatrickZLin/Decode-ListenerIdentity.
@article{arxiv.2510.17879,
title = {Decoding Listeners Identity: Person Identification from EEG Signals Using a Lightweight Spiking Transformer},
author = {Zheyuan Lin and Siqi Cai and Haizhou Li},
journal= {arXiv preprint arXiv:2510.17879},
year = {2025}
}