Decoding Listeners Identity Person Identification From Eeg Signals Using A Lightweight Spiking Transformer

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

**arXiv ID:** 2510.17879
**Authors:** Zheyuan Lin, Siqi Cai, Haizhou Li
**Published:** 2025-10-17T08:20:01Z
**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.

## Skill Description

This skill is generated from the arXiv paper: Decoding Listeners Identity: Person Identification from EEG Signals Using a Lightweight Spiking Transformer (2510.17879).

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## References

- [arXiv:2510.17879](http://arxiv.org/abs/2510.17879v1)