A brain-computer interface (BCI) enables direct communication between the brain and an external device. Electroencephalogram (EEG) is the preferred input signal in non-invasive BCIs, due to its convenience and low cost. EEG-based BCIs have been successfully used in many applications, such as neurological rehabilitation, text input, games, and so on. However, EEG signals inherently carry rich personal information, necessitating privacy protection. This paper demonstrates that multiple types of private information (user identity, gender, and BCI-experience) can be easily inferred from EEG data, imposing a serious privacy threat to BCIs. To address this issue, we design perturbations to convert the original EEG data into privacy-protected EEG data, which conceal the private information while maintaining the primary BCI task performance. Experimental results demonstrated that the privacy-protected EEG data can significantly reduce the classification accuracy of user identity, gender and BCI-experience, but almost do not affect at all the classification accuracy of the primary BCI task, enabling user privacy protection in EEG-based 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，成为非侵入式BCI的首选输入信号。基于EEG的BCI已成功应用于神经康复、文本输入、游戏等诸多领域。然而，EEG信号本身携带丰富的个人信息，亟需隐私保护。本文证明，多种类型的隐私信息（用户身份、性别和BCI使用经验）可轻易从EEG数据中推断出来，这对BCI构成了严重的隐私威胁。为解决这一问题，我们设计了扰动方法，将原始EEG数据转换为隐私保护的EEG数据，在保持主要BCI任务性能的同时隐藏隐私信息。实验结果表明，隐私保护的EEG数据能显著降低用户身份、性别和BCI使用经验的分类准确率，但几乎完全不影响主要BCI任务的分类准确率，从而实现了基于EEG的BCI中的用户隐私保护。