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...
- 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)