Decoding Listeners Identity: Person Identification from EEG Signals Using a Lightweight Spiking Transformer

• URL: http://arxiv.org/abs/2510.17879v1
• Date: Fri, 17 Oct 2025 08:20:01 GMT
• Title: Decoding Listeners Identity: Person Identification from EEG Signals Using a Lightweight Spiking Transformer
• Authors: Zheyuan Lin, Siqi Cai, Haizhou Li,
• Abstract summary: We propose a novel EEG person identification approach using spiking neural networks (SNNs) with a lightweight spiking transformer for efficiency and effectiveness.<n>The proposed SNN model is capable of handling the temporal complexities inherent in EEG signals.<n>On the EEG-Music Emotion Recognition Challenge dataset, the proposed model achieves 100% classification accuracy with less than 10% energy consumption.
• Score: 31.66487449656124
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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