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.

中文翻译：

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针对基于脑电图的脑机接口的基于对抗性过滤的规避和后门攻击

脑机接口（BCI）可以实现大脑和外部设备之间的直接通信。脑电图 (EEG) 因其便利性和低成本而成为 BCI 的常见输入信号。大多数基于脑电图的脑机接口研究都集中在脑电图信号的准确解码上，而忽视了其安全性。最近的研究表明，脑机接口中的机器学习模型很容易受到对抗性攻击。本文提出了针对基于脑电图的 BCI 的基于对抗性过滤的规避和后门攻击，这非常容易实现。对来自不同 BCI 范式的三个数据集的实验证明了我们提出的攻击方法的有效性。据我们所知，这是第一个针对基于脑电图的脑机接口的对抗性过滤的研究，提出了新的安全问题，并呼吁人们更多地关注脑机接口的安全性。