EEG/MEG Kalman-like source estimation
- Autores
- Bria, Oscar N.
- Año de publicación
- 2003
- Idioma
- inglés
- Tipo de recurso
- documento de conferencia
- Estado
- versión publicada
- Descripción
- The estimation of the instant location and strength of sources takes a considerable importance for many areas of sensor space-array processing, e.g., brain activity in non-invasive electro-medicine. State-space models are a well suited framework for solving that dynamic estimation problem and they are in the core of our studies. Related to brain electrical activity, the state estimation problem can be solved by analyzing spatio-temporal data provided by EEG/MEG measures. Nonlinear Kalman-like filter is proposed for estimating locustemporal data related to electrical activity in the brain. The experimental framework is described.
Eje: Procesamiento de Señales
Red de Universidades con Carreras en Informática (RedUNCI) - Materia
-
Ciencias Informáticas
EEG/MEG
source estimation
Signal processing - Nivel de accesibilidad
- acceso abierto
- Condiciones de uso
- http://creativecommons.org/licenses/by-nc-sa/2.5/ar/
- Repositorio
.jpg)
- Institución
- Universidad Nacional de La Plata
- OAI Identificador
- oai:sedici.unlp.edu.ar:10915/21512
Ver los metadatos del registro completo
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EEG/MEG Kalman-like source estimationBria, Oscar N.Ciencias InformáticasEEG/MEGsource estimationSignal processingThe estimation of the instant location and strength of sources takes a considerable importance for many areas of sensor space-array processing, e.g., brain activity in non-invasive electro-medicine. State-space models are a well suited framework for solving that dynamic estimation problem and they are in the core of our studies. Related to brain electrical activity, the state estimation problem can be solved by analyzing spatio-temporal data provided by EEG/MEG measures. Nonlinear Kalman-like filter is proposed for estimating locustemporal data related to electrical activity in the brain. The experimental framework is described.Eje: Procesamiento de SeñalesRed de Universidades con Carreras en Informática (RedUNCI)2003-05info:eu-repo/semantics/conferenceObjectinfo:eu-repo/semantics/publishedVersionObjeto de conferenciahttp://purl.org/coar/resource_type/c_5794info:ar-repo/semantics/documentoDeConferenciaapplication/pdf71-75http://sedici.unlp.edu.ar/handle/10915/21512enginfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/2.5/ar/Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5)reponame:SEDICI (UNLP)instname:Universidad Nacional de La Platainstacron:UNLP2025-09-03T10:27:31Zoai:sedici.unlp.edu.ar:10915/21512Institucionalhttp://sedici.unlp.edu.ar/Universidad públicaNo correspondehttp://sedici.unlp.edu.ar/oai/snrdalira@sedici.unlp.edu.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:13292025-09-03 10:27:31.327SEDICI (UNLP) - Universidad Nacional de La Platafalse |
| dc.title.none.fl_str_mv |
EEG/MEG Kalman-like source estimation |
| title |
EEG/MEG Kalman-like source estimation |
| spellingShingle |
EEG/MEG Kalman-like source estimation Bria, Oscar N. Ciencias Informáticas EEG/MEG source estimation Signal processing |
| title_short |
EEG/MEG Kalman-like source estimation |
| title_full |
EEG/MEG Kalman-like source estimation |
| title_fullStr |
EEG/MEG Kalman-like source estimation |
| title_full_unstemmed |
EEG/MEG Kalman-like source estimation |
| title_sort |
EEG/MEG Kalman-like source estimation |
| dc.creator.none.fl_str_mv |
Bria, Oscar N. |
| author |
Bria, Oscar N. |
| author_facet |
Bria, Oscar N. |
| author_role |
author |
| dc.subject.none.fl_str_mv |
Ciencias Informáticas EEG/MEG source estimation Signal processing |
| topic |
Ciencias Informáticas EEG/MEG source estimation Signal processing |
| dc.description.none.fl_txt_mv |
The estimation of the instant location and strength of sources takes a considerable importance for many areas of sensor space-array processing, e.g., brain activity in non-invasive electro-medicine. State-space models are a well suited framework for solving that dynamic estimation problem and they are in the core of our studies. Related to brain electrical activity, the state estimation problem can be solved by analyzing spatio-temporal data provided by EEG/MEG measures. Nonlinear Kalman-like filter is proposed for estimating locustemporal data related to electrical activity in the brain. The experimental framework is described. Eje: Procesamiento de Señales Red de Universidades con Carreras en Informática (RedUNCI) |
| description |
The estimation of the instant location and strength of sources takes a considerable importance for many areas of sensor space-array processing, e.g., brain activity in non-invasive electro-medicine. State-space models are a well suited framework for solving that dynamic estimation problem and they are in the core of our studies. Related to brain electrical activity, the state estimation problem can be solved by analyzing spatio-temporal data provided by EEG/MEG measures. Nonlinear Kalman-like filter is proposed for estimating locustemporal data related to electrical activity in the brain. The experimental framework is described. |
| publishDate |
2003 |
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2003-05 |
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eng |
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eng |
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info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by-nc-sa/2.5/ar/ Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5) |
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openAccess |
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http://creativecommons.org/licenses/by-nc-sa/2.5/ar/ Creative Commons Attribution-NonCommercial-ShareAlike 2.5 Argentina (CC BY-NC-SA 2.5) |
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