Frontiers in Neuroscience (Aug 2016)

Non-linear parameter estimates from non-stationary MEG data

  • Juan David Martinez-Vargas,
  • Jose David Lopez,
  • Adam Baker,
  • Mark William Woolrich,
  • Mark William Woolrich,
  • German Castellanos-Dominguez,
  • Gareth Barnes

DOI
https://doi.org/10.3389/fnins.2016.00366
Journal volume & issue
Vol. 10

Abstract

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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.

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