Decoding Listeners Identity: Person Identification from EEG Signals Using a Lightweight Spiking Transformer
This work addresses the problem of high computational cost in EEG person identification for applications like security and brain-computer interfaces, offering an incremental improvement in efficiency.
The paper tackles EEG-based person identification by proposing a lightweight spiking transformer model, achieving 100% classification accuracy with less than 10% energy consumption compared to traditional deep neural networks.
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.