提出隐私保护的脑机接口分类方法，提升多用户协作下的解码准确率。
Federated Motor Imagery Classification for Privacy-Preserving Brain-Computer Interfaces
- 采用本地批次归一化和尖锐度感知优化，缓解用户间数据差异
- 在三个公开数据集上超越六种先进联邦学习方法，甚至超过集中式训练
- 适合关注脑电信号隐私与模型泛化能力的研究者
基于脑电图（EEG）的脑机接口（BCI）需要大量用户数据以训练高精度分类器，但保护用户隐私至关重要。联邦学习（FL）为此提供了有前景的解决方案。本文提出联邦分类方法FedBS，结合本地批次特定批归一化与尖锐度感知最小化优化器，用于保护脑电图中运动想象（MI）分类的隐私。该方法通过局部批次归一化减少不同客户端间的数据差异，并在本地训练中使用尖锐度感知最小化优化器提升模型泛化能力。在三个公开的MI数据集上，使用三种主流深度学习模型进行实验，结果表明FedBS优于六种最先进的联邦学习方法。尤为突出的是，其性能甚至超过不考虑隐私保护的集中式训练。综上所述，FedBS在保护用户脑电数据隐私的同时，支持多用户参与大规模机器学习训练，从而提升脑机接口解码准确率。
原文摘要 · Abstract (English)
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.
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