Non-linear Parameter Estimates from Non-stationary MEG Data.
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.
Signal Processing and Recognition Group, Department of Electric and Electronic Engineering and Computation, Universidad Nacional de Colombia Manizales, Colombia.
Juan D
JD
Martínez-Vargas
SISTEMIC, Facultad de Ingeniería, Universidad de Antioquia UDEA Medellín, Colombia.
Jose D
JD
López
Oxford Centre for Human Brain Activity, Warneford Hospital, University of Oxford Oxford, UK.
Adam
A
Baker
Signal Processing and Recognition Group, Department of Electric and Electronic Engineering and Computation, Universidad Nacional de Colombia Manizales, Colombia.
German
G
Castellanos-Dominguez
Oxford Centre for Human Brain Activity, Warneford Hospital, University of OxfordOxford, UK; Centre for Functional MRI of the Brain, John Radcliffe Hospital, University of OxfordOxford, UK.
Mark W
MW
Woolrich
Wellcome Trust Centre for Neuroimaging, Institute of Neurology, University College London London, UK.
Gareth
G
Barnes
Bayesian comparison; Hidden Markov Model; MEG inverse problem; co-registration; non-stationary brain activity