  Motor imagery EEG classification plays a crucial role in non-invasive
Brain-Computer Interface (BCI) research. However, the classification is
affected by the non-stationarity and individual variations of EEG signals.
Simply pooling EEG data with different statistical distributions to train a
classification model can severely degrade the generalization performance. To
address this issue, the existing methods primarily focus on domain adaptation,
which requires access to the target data during training. This is unrealistic
in many EEG application scenarios. In this paper, we propose a novel
multi-source domain generalization framework called EEG-DG, which leverages
multiple source domains with different statistical distributions to build
generalizable models on unseen target EEG data. We optimize both the marginal
and conditional distributions to ensure the stability of the joint distribution
across source domains and extend it to a multi-source domain generalization
framework to achieve domain-invariant feature representation, thereby
alleviating calibration efforts. Systematic experiments on a simulative dataset
and BCI competition datasets IV-2a and IV-2b demonstrate the superiority of our
proposed EEG-DG over state-of-the-art methods. Specifically, EEG-DG achieves an
average classification accuracy/kappa value of 81.79%/0.7572 and 87.12%/0.7424
on datasets IV-2a and IV-2b, respectively, which even outperforms some domain
adaptation methods. Our code is available at
https://github.com/XC-ZhongHIT/EEG-DG for free download and evaluation.
