  We propose a generative model of a group EEG analysis, based on appropriate
kernel assumptions on EEG data. We derive the variational inference update rule
using various approximation techniques. The proposed model outperforms the
current state-of-the-art algorithms in terms of common pattern extraction. The
validity of the proposed model is tested on the BCI competition dataset.
