AUTHOR=Martínez-Vargas Juan D. , López Jose D. , Baker Adam , Castellanos-Dominguez German , Woolrich Mark W. , Barnes Gareth 

TITLE=Non-linear Parameter Estimates from Non-stationary MEG Data

JOURNAL=Frontiers in Neuroscience

VOLUME=10

YEAR=2016

URL=https://www.frontiersin.org/journals/neuroscience/articles/10.3389/fnins.2016.00366

DOI=10.3389/fnins.2016.00366

ISSN=1662-453X

ABSTRACT=<p>We demonstrate a method to estimate key electrophysiological parameters from resting state data. In this paper, we focus on the estimation of head-position parameters. The recovery of these parameters is especially challenging as they are non-linearly related to the measured field. In order to do this we use an empirical Bayesian scheme to estimate the cortical current distribution due to a range of laterally shifted head-models. We compare different methods of approaching this problem from the division of M/EEG data into stationary sections and performing separate source inversions, to explaining all of the M/EEG data with a single inversion. We demonstrate this through estimation of head position in both simulated and empirical resting state MEG data collected using a head-cast.</p>