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

TL;DR

This paper introduces a lightweight spiking transformer model for EEG-based person identification, achieving high accuracy with low energy consumption, advancing secure and efficient brain-computer interface applications.

Contribution

The study presents a novel spiking neural network approach with a lightweight transformer for EEG person identification, significantly reducing energy use while maintaining high accuracy.

Findings

Achieves 100% classification accuracy on EEG dataset.

Uses less than 10% energy of traditional deep neural networks.

Demonstrates energy-efficient, high-performance BCI potential.

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.