Weighted time series analysis for electroencephalographic source localization
This paper presents a new method to estimate neural activity from electroencephalographic signals using a weighted time series analysis. The method considers a physiologically based linear model that takes both spatial and temporal dynamics into account and a weighting stage to modify the assumption...
- Autores:
-
Giraldo Suárez, Eduardo
Castellanos Domínguez, César Germán
Peluffo Ordoñez, Diego Hernán
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2012
- Institución:
- Universidad Nacional de Colombia
- Repositorio:
- Universidad Nacional de Colombia
- Idioma:
- spa
- OAI Identifier:
- oai:repositorio.unal.edu.co:unal/39495
- Acceso en línea:
- https://repositorio.unal.edu.co/handle/unal/39495
http://bdigital.unal.edu.co/29592/
- Palabra clave:
- inverse problem
brain mapping
weighting matrix
- Rights
- openAccess
- License
- Atribución-NoComercial 4.0 Internacional
| Summary: | This paper presents a new method to estimate neural activity from electroencephalographic signals using a weighted time series analysis. The method considers a physiologically based linear model that takes both spatial and temporal dynamics into account and a weighting stage to modify the assumptions of the model from observations. The calculated weighting matrix is included in the cost function used to solve the dynamic inverse problem, and therefore in the Kalman filter formulation. In this way, a weighted Kalman filtering approach is proposed including a preponderance matrix. The filter’s performance (in terms of localization error) is analyzed for several SNRs. The optimal performance is achieved using the linear model with a weighting matrix computed by an inner product method. |
|---|