Title |
Non-linear Parameter Estimates from Non-stationary MEG Data
|
---|---|
Published in |
Frontiers in Neuroscience, August 2016
|
DOI | 10.3389/fnins.2016.00366 |
Pubmed ID | |
Authors |
Martínez-Vargas, Juan D., López, Jose D., Baker, Adam, Castellanos-Dominguez, German, Woolrich, Mark W., Barnes, Gareth, Martínez-Vargas, Juan D, López, Jose D, Woolrich, Mark W |
Abstract |
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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