A brain-computer interface (BCI) establishes a direct communication pathway between the brain and an external device. Electroencephalogram (EEG) is the most popular input signal in BCIs, due to its convenience and low cost. Most research on EEG-based BCIs focuses on the accurate decoding of EEG signals; however, EEG signals also contain rich private information, e.g., user identity, emotion, and so on, which should be protected. This paper first exposes a serious privacy problem in EEG-based BCIs, i.e., the user identity in EEG data can be easily learned so that different sessions of EEG data from the same user can be associated together to more reliably mine private information. To address this issue, we further propose two approaches to convert the original EEG data into identity-unlearnable EEG data, i.e., removing the user identity information while maintaining the good performance on the primary BCI task. Experiments on seven EEG datasets from five different BCI paradigms showed that on average the generated identity-unlearnable EEG data can reduce the user identification accuracy from 70.01\% to at most 21.36\%, greatly facilitating user privacy protection in EEG-based BCIs.

翻译：脑机接口（BCI）在大脑与外部设备之间建立了一条直接的通信通路。脑电图（EEG）因其便捷性和低成本，成为BCI中最常用的输入信号。大多数基于EEG的BCI研究侧重于EEG信号的精确解码；然而，EEG信号也包含丰富的私人信息，例如用户身份、情绪等，这些信息应当受到保护。本文首先揭示了基于EEG的BCI中一个严重的隐私问题，即EEG数据中的用户身份可以被轻易学习，从而可以将同一用户的不同会话EEG数据关联起来，以更可靠地挖掘私人信息。为解决此问题，我们进一步提出了两种方法，将原始EEG数据转换为身份不可学习的EEG数据，即在保持主要BCI任务良好性能的同时，移除用户身份信息。在来自五种不同BCI范式的七个EEG数据集上的实验表明，平均而言，生成的身份不可学习EEG数据可将用户识别准确率从70.01%降低至最多21.36%，极大地促进了基于EEG的BCI中的用户隐私保护。