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)
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| dc.title.spa.fl_str_mv |
Non-linear parameter estimates from non-stationary MEG data |
| title |
Non-linear parameter estimates from non-stationary MEG data |
| spellingShingle |
Non-linear parameter estimates from non-stationary MEG data MEG inverse problem Co-registration Hidden Markov Model Non-stationary brain activity Bayesian comparison |
| title_short |
Non-linear parameter estimates from non-stationary MEG data |
| title_full |
Non-linear parameter estimates from non-stationary MEG data |
| title_fullStr |
Non-linear parameter estimates from non-stationary MEG data |
| title_full_unstemmed |
Non-linear parameter estimates from non-stationary MEG data |
| title_sort |
Non-linear parameter estimates from non-stationary MEG data |
| dc.creator.fl_str_mv |
López Hincapié, José David Castellanos Domínguez, César Germán Barnes, Gareth Robert Baker, Adam Woolrich, Mark W. |
| dc.contributor.author.none.fl_str_mv |
López Hincapié, José David Castellanos Domínguez, César Germán Barnes, Gareth Robert Baker, Adam Woolrich, Mark W. |
| dc.subject.none.fl_str_mv |
MEG inverse problem Co-registration Hidden Markov Model Non-stationary brain activity Bayesian comparison |
| topic |
MEG inverse problem Co-registration Hidden Markov Model Non-stationary brain activity Bayesian comparison |
| description |
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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2016 |
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2016 |
| dc.date.accessioned.none.fl_str_mv |
2017-07-14T20:26:32Z |
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2017-07-14T20:26:32Z |
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info:eu-repo/semantics/article |
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http://purl.org/coar/version/c_970fb48d4fbd8a86 http://purl.org/coar/version/c_970fb48d4fbd8a85 |
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http://purl.org/coar/resource_type/c_2df8fbb1 |
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https://purl.org/redcol/resource_type/ART |
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Artículo de investigación |
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http://purl.org/coar/resource_type/c_2df8fbb1 |
| dc.identifier.citation.spa.fl_str_mv |
Martínez, J. D., López, J. D., Castellanos, C. G., Barnes, G. R., Baker, Adam., & Woolrich, M.W. (2016). Non-linear parameter estimates from non-stationary MEG data. Frontiers in Neuroscience, 10(366), 1-9. DOI: 10.3389/fnins.2016.00366 |
| dc.identifier.issn.none.fl_str_mv |
1662-4548 |
| dc.identifier.uri.none.fl_str_mv |
http://hdl.handle.net/10495/7662 |
| dc.identifier.doi.none.fl_str_mv |
10.3389/fnins.2016.00366 |
| dc.identifier.eissn.none.fl_str_mv |
166-2453 |
| identifier_str_mv |
Martínez, J. D., López, J. D., Castellanos, C. G., Barnes, G. R., Baker, Adam., & Woolrich, M.W. (2016). Non-linear parameter estimates from non-stationary MEG data. Frontiers in Neuroscience, 10(366), 1-9. DOI: 10.3389/fnins.2016.00366 1662-4548 10.3389/fnins.2016.00366 166-2453 |
| url |
http://hdl.handle.net/10495/7662 |
| dc.language.iso.spa.fl_str_mv |
eng |
| language |
eng |
| dc.rights.*.fl_str_mv |
Atribución 2.5 Colombia (CC BY 2.5 CO) |
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info:eu-repo/semantics/openAccess |
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https://creativecommons.org/licenses/by/2.5/co/ |
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http://purl.org/coar/access_right/c_abf2 |
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https://creativecommons.org/licenses/by/4.0/ |
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Atribución 2.5 Colombia (CC BY 2.5 CO) https://creativecommons.org/licenses/by/2.5/co/ http://purl.org/coar/access_right/c_abf2 https://creativecommons.org/licenses/by/4.0/ |
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openAccess |
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application/pdf |
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Frontiers Media |
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Sistemas Embebidos e Inteligencia Computacional (SISTEMIC) |
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Suiza |
| institution |
Universidad de Antioquia |
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andres.perez@udea.edu.co |
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López Hincapié, José DavidCastellanos Domínguez, César GermánBarnes, Gareth RobertBaker, AdamWoolrich, Mark W.2017-07-14T20:26:32Z2017-07-14T20:26:32Z2016Martínez, J. D., López, J. D., Castellanos, C. G., Barnes, G. R., Baker, Adam., & Woolrich, M.W. (2016). Non-linear parameter estimates from non-stationary MEG data. Frontiers in Neuroscience, 10(366), 1-9. DOI: 10.3389/fnins.2016.003661662-4548http://hdl.handle.net/10495/766210.3389/fnins.2016.00366166-2453ABSTRACT: 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.8application/pdfengFrontiers MediaSistemas Embebidos e Inteligencia Computacional (SISTEMIC)Suizainfo:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_2df8fbb1https://purl.org/redcol/resource_type/ARTArtículo de investigaciónhttp://purl.org/coar/version/c_970fb48d4fbd8a86http://purl.org/coar/version/c_970fb48d4fbd8a85Atribución 2.5 Colombia (CC BY 2.5 CO)info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/2.5/co/http://purl.org/coar/access_right/c_abf2https://creativecommons.org/licenses/by/4.0/MEG inverse problemCo-registrationHidden Markov ModelNon-stationary brain activityBayesian comparisonNon-linear parameter estimates from non-stationary MEG dataFrontiers in Neuroscience1910366ORIGINALLopezJose_2016_NonlinearParameterEstimates.pdfLopezJose_2016_NonlinearParameterEstimates.pdfArtículo de investigaciónapplication/pdf3465602http://bibliotecadigital.udea.edu.co/bitstream/10495/7662/1/LopezJose_2016_NonlinearParameterEstimates.pdf6920869c08b551cf596f4c49fde2e18bMD51CC-LICENSElicense_urllicense_urltext/plain; charset=utf-849http://bibliotecadigital.udea.edu.co/bitstream/10495/7662/2/license_url4afdbb8c545fd630ea7db775da747b2fMD52license_textlicense_texttext/html; charset=utf-80http://bibliotecadigital.udea.edu.co/bitstream/10495/7662/3/license_textd41d8cd98f00b204e9800998ecf8427eMD53license_rdflicense_rdfapplication/rdf+xml; charset=utf-80http://bibliotecadigital.udea.edu.co/bitstream/10495/7662/4/license_rdfd41d8cd98f00b204e9800998ecf8427eMD54LICENSElicense.txtlicense.txttext/plain; charset=utf-81748http://bibliotecadigital.udea.edu.co/bitstream/10495/7662/5/license.txt8a4605be74aa9ea9d79846c1fba20a33MD5510495/7662oai:bibliotecadigital.udea.edu.co:10495/76622021-06-18 09:00:38.025Repositorio Institucional Universidad de Antioquiaandres.perez@udea.edu.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 |