提出轻量CNN提升脑机接口抗干扰能力，保障安全可靠
Making Brain-Computer Interfaces More Secure
- 设计轻量级CNN模型专攻脑电信号对抗鲁棒性
- 在两个数据集上对抗攻击下分类准确率优于基线模型
- 适合关注脑机接口安全性的研究人员与开发者
基于脑电图（EEG）的脑机接口（BCI）虽在机器学习推动下发展迅速，但多数研究聚焦分类精度，忽视安全与鲁棒性。近期研究表明，EEG-BMI易受对抗攻击影响，微小扰动即可引发误诊。本文提出一种轻量级自定义卷积神经网络（CNN），在两个EEG数据集上评估其对梯度类对抗攻击的鲁棒性，并与EEGNet、DeepConvNet和SleepEEGNet三种专用于EEG的新型CNN模型对比。实验结果表明，所提模型在对抗扰动下持续保持更高分类性能，验证了其在对抗条件下的可靠性优势，表明轻量架构有助于提升EEG-BMI系统的安全性。
原文摘要 · Abstract (English)
The development of brain-computer interfaces (BCIs) based on electroencephalograms (EEGs) has advanced significantly mainly to machine learning. Although the majority of earlier research has been on increasing classification accuracy, relatively little focus has been placed on security and robustness. According to recent research, EEG-based BCIs are susceptible to adversarial attacks, which can cause misdiagnosis due to minute, well-crafted disturbances. Evaluating model robustness against such perturbations is therefore critical for ensuring reliable deployment. In this study, we propose a lightweight custom Convolutional Neural Network (CNN) architecture to investigate adversarial robustness in EEG-based BCIs. The suggested method is assessed using two EEG datasets and contrasted with three novel CNN models tailored to EEG, namely EEGNet, DeepConvNet, and SleepEEGNet, under gradient-based adversarial attack scenarios. According to experimental findings, the suggested model continuously performs better in classification under adversarial perturbations compared to baseline models, indicating improved robustness. These findings highlight the potential of lightweight architectures for enhancing the reliability of EEG-based BCI systems under adversarial conditions.
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