Providing a promising pathway to link the human brain with external devices, Brain-Computer Interfaces (BCIs) have seen notable advancements in decoding capabilities, primarily driven by increasingly sophisticated techniques, especially deep learning. However, achieving high accuracy in real-world scenarios remains a challenge due to the distribution shift between sessions and subjects. In this paper we will explore the concept of online test-time adaptation (OTTA) to continuously adapt the model in an unsupervised fashion during inference time. Our approach guarantees the preservation of privacy by eliminating the requirement to access the source data during the adaptation process. Additionally, OTTA achieves calibration-free operation by not requiring any session- or subject-specific data. We will investigate the task of electroencephalography (EEG) motor imagery decoding using a lightweight architecture together with different OTTA techniques like alignment, adaptive batch normalization, and entropy minimization. We examine two datasets and three distinct data settings for a comprehensive analysis. Our adaptation methods produce state-of-the-art results, potentially instigating a shift in transfer learning for BCI decoding towards online adaptation.

翻译：脑机接口（BCI）为连接人脑与外部设备提供了有前景的途径，其在解码能力方面已取得显著进展，这主要得益于日益精进的技术，尤其是深度学习。然而，由于不同实验轮次和受试者之间存在数据分布偏移，在真实场景中实现高精度仍具挑战。本文将探索在线测试时适应（OTTA）的概念，以在推理阶段以无监督方式持续调整模型。我们的方法无需访问源数据即可完成适应过程，从而保障隐私。此外，OTTA无需任何特定于轮次或受试者的数据，实现了免校准操作。我们将研究基于轻量级架构的脑电图（EEG）运动想象解码任务，并结合对齐、自适应批归一化和熵最小化等不同OTTA技术。为进行全面分析，我们考察了两个数据集和三种不同的数据设置。我们的适应方法取得了最先进的结果，可能引发BCI解码迁移学习向在线适应方向的范式转变。