Machine learning models are known to be vulnerable to adversarial perturbations in the input domain, causing incorrect predictions. Inspired by this phenomenon, we explore the feasibility of manipulating EEG-based Motor Imagery (MI) Brain Computer Interfaces (BCIs) via perturbations in sensory stimuli. Similar to adversarial examples, these \emph{adversarial stimuli} aim to exploit the limitations of the integrated brain-sensor-processing components of the BCI system in handling shifts in participants' response to changes in sensory stimuli. This paper proposes adversarial stimuli as an attack vector against BCIs, and reports the findings of preliminary experiments on the impact of visual adversarial stimuli on the integrity of EEG-based MI BCIs. Our findings suggest that minor adversarial stimuli can significantly deteriorate the performance of MI BCIs across all participants (p=0.0003). Additionally, our results indicate that such attacks are more effective in conditions with induced stress.

翻译：据了解,机器学习模型在输入领域容易受到对抗性干扰,造成不正确的预测。受这一现象的启发,我们探索通过感官刺激干扰来操纵基于EEG的机动影像(MI)脑计算机界面(BCIs)的可行性。与对抗性例子相似,这些对抗性模拟旨在利用BCI系统综合脑传感器处理组件的局限性,处理参与者因感官刺激变化而变化的反应的转变。本文建议将对抗性刺激作为针对BCIs的攻击矢量,并报告关于视觉对抗性模拟对基于EEG的MI BCIs完整性的影响的初步实验结果。我们的调查结果表明,小型对抗性模拟可大大削弱所有参与者(p=0.0003)MIBCIs的性能。此外,我们的结果表明,这种攻击在诱发压力的条件下更为有效。