IEEE Trans Neural Syst Rehabil Eng. 2024;32:3442-3451. doi: 10.1109/TNSRE.2024.3457504. Epub 2024 Sep 18.
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.
训练基于脑电图的脑机接口 (BCI) 的精确分类器需要大量用户的脑电图数据，而保护他们的数据隐私是一个关键考虑因素。联邦学习 (FL) 是解决这一挑战的一种有前途的方法。本文提出了基于脑电图的运动想象 (MI) 分类的联邦分类与本地批量特定批量归一化和锐度感知最小化 (FedBS)，以保护隐私。FedBS 利用本地批量特定批量归一化来减少不同客户端之间的数据差异，以及在本地训练中使用锐度感知最小化优化器来提高模型泛化能力。在三个公共 MI 数据集上使用三个流行的深度学习模型进行的实验表明，FedBS 优于六种最先进的 FL 方法。值得注意的是，它也优于完全不考虑隐私保护的集中式训练。总之，FedBS 保护用户的脑电图数据隐私，使多个 BCI 用户能够参与大规模机器学习模型训练，从而提高 BCI 解码精度。
