Training an accurate classifier for EEG-based brain-computer interface (BCI) requires EEG data from a large number of users, whereas protecting their data privacy is a critical consideration. Federated learning (FL) is a promising solution to this challenge. This paper proposes Federated classification with local Batch-specific batch normalization and Sharpness-aware minimization (FedBS) for privacy protection in EEG-based motor imagery (MI) classification. FedBS utilizes local batch-specific batch normalization to reduce data discrepancies among different clients, and sharpness-aware minimization optimizer in local training to improve model generalization. Experiments on three public MI datasets using three popular deep learning models demonstrated that FedBS outperformed six state-of-the-art FL approaches. Remarkably, it also outperformed centralized training, which does not consider privacy protection at all. In summary, FedBS protects user EEG data privacy, enabling multiple BCI users to participate in large-scale machine learning model training, which in turn improves the BCI decoding accuracy.

翻译：基于脑电图（EEG）的脑机接口（BCI）训练一个准确的分类器需要大量用户的EEG数据，而保护其数据隐私是一个关键考量。联邦学习（FL）是应对这一挑战的一个有前景的解决方案。本文提出了用于基于EEG的运动想象（MI）分类隐私保护的联邦分类方法，该方法结合了本地批次特定批量归一化与锐度感知最小化（FedBS）。FedBS利用本地批次特定批量归一化来减少不同客户端之间的数据差异，并在本地训练中使用锐度感知最小化优化器以提高模型的泛化能力。在三个公开MI数据集上使用三种流行深度学习模型的实验表明，FedBS优于六种先进的FL方法。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。总之，FedBS保护了用户EEG数据的隐私，使得多个BCI用户能够参与大规模机器学习模型训练，从而提高了BCI的解码精度。