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.
@article{arxiv.1212.4347,
title = {Bayesian Group Nonnegative Matrix Factorization for EEG Analysis},
author = {Bonggun Shin and Alice Oh},
journal= {arXiv preprint arXiv:1212.4347},
year = {2012}
}