A Graphical Model for Estimating Stimulus-Evoked Brain Responses in Noisy MEG data with Large Background Brain Activity

This paper formulates a novel probabilistic graphical model for stimulus-evoked MEG and EEG sensor data obtained in the presence of large background brain activity. The model describes the observed data in terms of unobserved evoked and background sources. We present an expectation maximization algorithm that estimates the model parameters from data. Using the model, the algorithm cleans the stimulus-evoked data by removing interference from background sources and noise artifacts, and separates those data into contributions from independent factors. We demonstrate on real and simulated data that the algorithm outperforms benchmark methods for denoising and separation. We also show that the algorithm improves the performance of existing localization techniques. Keywords— Bayesian methods, graphical models, ICA