An electroencephalogram (EEG) based brain-computer interface (BCI) enables direct communication between the brain and external devices. However, EEG-based BCIs face at least three major challenges in real-world applications: data scarcity and individual differences, adversarial vulnerability, and data privacy. While previous studies have addressed one or two of these issues, simultaneous accommodation of all three challenges remains challenging and unexplored. This paper fills this gap, by proposing an Augmented Robustness Ensemble (ARE) algorithm and integrating it into three privacy protection scenarios (centralized source-free transfer, federated source-free transfer, and source data perturbation), achieving simultaneously accurate decoding, adversarial robustness, and privacy protection of EEG-based BCIs. Experiments on three public EEG datasets demonstrated that our proposed approach outperformed over 10 classic and state-of-the-art approaches in both accuracy and robustness in all three privacy-preserving scenarios, even outperforming state-of-the-art transfer learning approaches that do not consider privacy protection at all. This is the first time that three major challenges in EEG-based BCIs can be addressed simultaneously, significantly improving the practicalness of EEG decoding in real-world BCIs.

翻译：基于脑电图（EEG）的脑机接口（BCI）实现了大脑与外部设备之间的直接通信。然而，基于EEG的BCI在现实应用中至少面临三大挑战：数据稀缺性与个体差异、对抗性脆弱性以及数据隐私。尽管先前的研究已针对其中一或两个问题提出了解决方案，但同时应对所有三个挑战仍然困难且尚未得到探索。本文填补了这一空白，提出了一种增强鲁棒性集成（ARE）算法，并将其集成到三种隐私保护场景中（集中式无源迁移、联邦式无源迁移以及源数据扰动），从而同时实现基于EEG的BCI的精准解码、对抗鲁棒性与隐私保护。在三个公开EEG数据集上的实验表明，我们提出的方法在所有三种隐私保护场景中，其准确性和鲁棒性均优于10余种经典及前沿方法，甚至超越了完全不考虑隐私保护的前沿迁移学习方法。这是首次能够同时应对基于EEG的BCI中的三大挑战，显著提升了EEG解码在现实世界BCI中的实用性。