Dynamic Inverse Problem Solution Using a Kalman Filter Smoother for Neuronal Activity Estimation

This article presents an estimation method of neuronal activity into the brain using a Kalman smoother approach that takes into account in the solution of the inverse problem the dynamic variability of the time series. This method is applied over a realistic head model calculated with the boundary e...

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Bibliographic Details
Published inTecno - Lógicas (Instituto Tecnológico Metropolitano) no. 27; pp. 33 - 51
Main Authors Giraldo-Suárez, Eduardo, Padilla-Buriticá, Jorge I., Castellanos-Domínguez, César G.
Format Journal Article
LanguagePortuguese
English
Published Instituto Tecnológico Metropolitano - ITM 01.12.2011
Instituto Tecnológico Metropolitano
Subjects
actividad neuronal
ENGINEERING, MULTIDISCIPLINARY
filtro de Kalman
Inverse problem
Kalman filter
modelo fisiológico
neuronal activity
physiological model
Problema inverso
filtro de Kalman
neuronal activity
Kalman filter
Problema inverso
actividad neuronal
physiological model
modelo fisiológico
Inverse problem
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Summary:This article presents an estimation method of neuronal activity into the brain using a Kalman smoother approach that takes into account in the solution of the inverse problem the dynamic variability of the time series. This method is applied over a realistic head model calculated with the boundary element method. A comparative analysis for the dynamic estimation methods is made up from simulated EEG signals for several noise conditions. The solution of the inverse problem is achieved by using high performance computing techniques and an evaluation of the computational cost is performed for each method. As a result, the Kalman smoother approach presents better performance in the estimation task than the regularized static solution, and the direct Kalman filter.
ISSN:2256-5337
0123-7799
2256-5337