A brain-computer interface (BCI) enables direct communication between the brain and an external device. Electroencephalogram (EEG) is a common input signal for BCIs, due to its convenience and low cost. Most research on EEG-based BCIs focuses on the accurate decoding of EEG signals, while ignoring their security. Recent studies have shown that machine learning models in BCIs are vulnerable to adversarial attacks. This paper proposes adversarial filtering based evasion and backdoor attacks to EEG-based BCIs, which are very easy to implement. Experiments on three datasets from different BCI paradigms demonstrated the effectiveness of our proposed attack approaches. To our knowledge, this is the first study on adversarial filtering for EEG-based BCIs, raising a new security concern and calling for more attention on the security of BCIs.

翻译：脑机接口（BCI）实现了大脑与外部设备之间的直接通信。脑电图（EEG）因其便捷性和低成本，常被用作BCI的输入信号。当前基于EEG的BCI研究多集中于EEG信号的精确解码，而忽视了其安全性。近期研究表明，BCI中的机器学习模型易受对抗性攻击。本文提出了一种基于对抗性滤波的、易于实施的EEG-BCI逃避与后门攻击方法。在三种不同BCI范式数据集上的实验验证了所提攻击方法的有效性。据我们所知，这是首项针对EEG-BCI对抗性滤波的研究，揭示了新的安全隐患，并呼吁对BCI安全性给予更多关注。