An electroencephalogram (EEG)-based brain-computer interface (BCI) enables direct communication between the brain and external devices. However, such systems face at least three major challenges in real-world applications: limited decoding accuracy, poor robustness, and privacy risks. Although prior studies have addressed one or two of these issues, methods that simultaneously improve accuracy, robustness, and privacy remain largely unexplored. In this paper, we propose Privacy-preserving Adversarial Transfer (PAT), a unified training framework that combines data alignment, adversarial training, and privacy-preserving transfer. PAT provides a single pipeline that can be instantiated under three privacy-preserving scenarios, i.e., centralized source-free transfer, federated source-free transfer, and transfer with privacy-preserved source data, while jointly improving accuracy and robustness. Experiments on five public EEG datasets under three privacy-preserving scenarios (centralized source-free transfer, federated source-free transfer, and transfer with privacy-preserved source data) show that PAT outperforms over ten classic and state-of-the-art methods in both accuracy and robustness. PAT also outperformed leading transfer learning approaches that do not incorporate any privacy mechanisms by 9.76% in terms of average accuracy and robustness. To our knowledge, this is the first approach that simultaneously addresses all three major challenges in EEG-based BCIs. We believe this work can help motivate further research on more accurate, robust, and privacy-preserving EEG decoding.

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，这类系统在实际应用中至少面临三大挑战：解码精度有限、鲁棒性差以及隐私风险。尽管先前已有研究解决其中一两个问题，但能够同时提升精度、鲁棒性和隐私保护的方法仍鲜有探索。本文提出隐私保护对抗迁移（PAT），一种融合数据对齐、对抗训练和隐私保护迁移的统一训练框架。PAT提供单一的流水线，可在三种隐私保护场景下实例化，即中心化源数据免共享迁移、联邦源数据免共享迁移以及带隐私保护源数据的迁移，同时联合提升精度与鲁棒性。在五个公开EEG数据集上，针对三种隐私保护场景（中心化源数据免共享迁移、联邦源数据免共享迁移以及带隐私保护源数据的迁移）的实验表明，PAT在精度和鲁棒性方面均优于十多种经典及最新方法。此外，与未融入任何隐私机制的领先迁移学习方法相比，PAT在平均精度和鲁棒性上提升了9.76%。据我们所知，这是首个同时应对基于EEG的BCI中三大挑战的方法。我们相信，这项工作有助于启发对更精确、更鲁棒且更隐私保护的脑电解码的进一步研究。