Non-linear parameter estimates from non-stationary MEG data

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 orde...

Full description

Autores:
López Hincapié, José David
Castellanos Domínguez, César Germán
Barnes, Gareth Robert
Baker, Adam
Woolrich, Mark W.
Tipo de recurso:
Article of investigation
Fecha de publicación:
2016
Institución:
Universidad de Antioquia
Repositorio:
Repositorio UdeA
Idioma:
eng
OAI Identifier:
oai:bibliotecadigital.udea.edu.co:10495/7662
Acceso en línea:
http://hdl.handle.net/10495/7662
Palabra clave:
MEG inverse problem
Co-registration
Hidden Markov Model
Non-stationary brain activity
Bayesian comparison
Rights
openAccess
License
Atribución 2.5 Colombia (CC BY 2.5 CO)