The distribution shift of electroencephalography (EEG) data causes poor generalization of braincomputer interfaces (BCIs) in unseen domains. Some methods try to tackle this challenge by collecting a portion of user data for calibration. However, it is time-consuming, mentally fatiguing, and user-unfriendly. To achieve zerocalibration BCIs, most studies employ domain generalization (DG) techniques to learn invariant features across different domains in the training set. However, they fail to fully explore invariant features within the same domain, leading to limited performance. In this paper, we present an novel method to learn domain-invariant features from both interdomain and intra-domain perspectives. For intra-domain invariant features, we propose a knowledge distillation framework to extract EEG phase-invariant features within one domain. As for inter-domain invariant features, correlation alignment is used to bridge distribution gaps across multiple domains. Experimental results on three public datasets validate the effectiveness of our method, showcasing stateof-the-art performance. To the best of our knowledge, this is the first domain generalization study that exploit Fourier phase information as an intra-domain invariant feature to facilitate EEG generalization. More importantly, the zerocalibration BCI based on inter- and intra-domain invariant features has significant potential to advance the practical applications of BCIs in real world.

翻译：脑电图（EEG）数据的分布偏移导致脑机接口（BCI）在未见域中泛化能力较差。部分方法通过收集用户数据进行校准以应对这一挑战，但这耗时费力、易致精神疲劳且用户友好性差。为实现零校准BCI，多数研究采用域泛化（DG）技术学习训练集中不同域间的不变特征，但未能充分挖掘同域内的不变特征，导致性能受限。本文提出一种新颖方法，从跨域与域内双重视角学习域不变特征。针对域内不变特征，我们设计了知识蒸馏框架以提取单域内EEG相位不变特征；针对跨域不变特征，采用相关性对齐弥合多域间的分布差异。在三个公开数据集上的实验结果验证了本方法的有效性，并展现出最先进的性能。据我们所知，这是首个利用傅里叶相位信息作为域内不变特征促进EEG泛化的域泛化研究。更重要的是，基于跨域与域内不变特征的零校准BCI具有推动BCI在现实世界中实际应用的显著潜力。