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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.

Original publication

DOI

10.3389/fnins.2016.00366

Type

Journal article

Journal

Front Neurosci

Publication Date

2016

Volume

10

Keywords

Bayesian comparison, Hidden Markov Model, MEG inverse problem, co-registration, non-stationary brain activity