Bayesian Group Nonnegative Matrix Factorization for EEG Analysis

Abstract

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.

Cited by in corpus (1)

• Supervised Dictionary Learning by a Variational Bayesian Group Sparse Nonnegative Matrix Factorization