用轻量脉冲变压器实现高效高精度脑电身份识别
Decoding Listeners Identity: Person Identification from EEG Signals Using a Lightweight Spiking Transformer
- 采用脉冲神经网络+轻量脉冲变压器处理脑电信号时序特征
- 在脑电音乐情绪数据集上达100%准确率，能耗低于传统模型10%
- 适合低功耗脑机接口场景，尤其嵌入式设备应用
基于脑电（EEG）的身份识别可用于安全、个性化脑机接口（BCIs）和认知监测。然而，现有方法多依赖计算成本高的深度学习架构，限制了实际应用。本文提出一种新型的脑电身份识别方法，采用脉冲神经网络（SNN）结合轻量脉冲变压器，在保证性能的同时提升效率。所提SNN模型能有效处理脑电信号固有的时序复杂性。在EEG-Music Emotion Recognition Challenge数据集上，该模型实现100%分类准确率，能耗不足传统深度神经网络的10%。本研究为低功耗、高性能脑机接口提供了新方向。源代码已公开：https://github.com/PatrickZLin/Decode-ListenerIdentity。
原文摘要 · Abstract (English)
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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