  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.
