Electroencephalogram (EEG)-based brain-computer interfaces (BCIs) are widely adopted due to their efficiency and portability; however, their decoding algorithms still face multiple challenges, including inadequate generalization, adversarial vulnerability, and privacy leakage. This paper proposes Secure and Accurate FEderated learning (SAFE), a federated learning-based approach that protects user privacy by keeping data local during model training. SAFE employs local batch-specific normalization to mitigate cross-subject feature distribution shifts and hence improves model generalization. It further enhances adversarial robustness by introducing perturbations in both the input space and the parameter space through federated adversarial training and adversarial weight perturbation. Experiments on five EEG datasets from motor imagery (MI) and event-related potential (ERP) BCI paradigms demonstrated that SAFE consistently outperformed 14 state-of-the-art approaches in both decoding accuracy and adversarial robustness, while ensuring privacy protection. Notably, it even outperformed centralized training approaches that do not consider privacy protection at all. To our knowledge, SAFE is the first algorithm to simultaneously achieve high decoding accuracy, strong adversarial robustness, and reliable privacy protection without using any calibration data from the target subject, making it highly desirable for real-world BCIs.

翻译：基于脑电图（EEG）的脑机接口（BCI）因其高效性与便携性而被广泛采用；然而，其解码算法仍面临多重挑战，包括泛化能力不足、对抗脆弱性以及隐私泄露。本文提出安全准确联邦学习（SAFE），这是一种基于联邦学习的方法，通过在模型训练过程中保持数据本地化来保护用户隐私。SAFE采用局部批次特定归一化来缓解跨被试特征分布偏移，从而提升模型泛化能力。该方法进一步通过联邦对抗训练与对抗权重扰动，在输入空间和参数空间中引入扰动，以增强对抗鲁棒性。在运动想象（MI）和事件相关电位（ERP）两种BCI范式下的五个EEG数据集上的实验表明，SAFE在解码准确率与对抗鲁棒性上均持续优于14种先进方法，同时确保了隐私保护。值得注意的是，其性能甚至超过了完全不考虑隐私保护的集中式训练方法。据我们所知，SAFE是首个在不使用任何目标被试校准数据的情况下，同时实现高解码准确率、强对抗鲁棒性与可靠隐私保护的算法，这使其对于现实世界的BCI应用极具吸引力。