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Non-linear Parameter Estimates from Non-stationary MEG Data

Martinez-Vargas, JD; Lopez, JD; Baker, A; Castellanos-Dominguez, G; Woolrich, MW; Barnes, G; (2016) Non-linear Parameter Estimates from Non-stationary MEG Data. Frontiers in Neuroscience , 10 , Article 366. 10.3389/fnins.2016.00366. Green open access

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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.

Type: Article
Title: Non-linear Parameter Estimates from Non-stationary MEG Data
Open access status: An open access version is available from UCL Discovery
DOI: 10.3389/fnins.2016.00366
Publisher version: http://dx.doi.org/10.3389/fnins.2016.00366
Language: English
Additional information: Copyright © 2016 Martínez-Vargas, López, Baker, Castellanos-Dominguez, Woolrich and Barnes. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Keywords: Science & Technology, Life Sciences & Biomedicine, Neurosciences, Neurosciences & Neurology, MEG inverse problem, co-registration, Hidden Markov Model, non-stationary brain activity, Bayesian comparison, SOURCE RECONSTRUCTION, CONNECTIVITY, LOCALIZATION, EEG
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology > Imaging Neuroscience
URI: https://discovery.ucl.ac.uk/id/eprint/1516995
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