Estimation of neuronal activity and brain dynamics using a dual Kalman filter with physiologycal based linear model

In this research article a dynamic estimation of neuronal activity and brain dynamics from electroencephalographic (EEG) signals is presented using a dual Kalman filter. The dynamic model for brain behavior is evaluated using physiological-based linear models. Filter performance is analyzed for simu...

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Bibliographic Details
Published inRevista ingenierías (Medellín, Colombia) Vol. 12; no. 22; pp. 169 - 180
Main Authors Giraldo, Eduardo, Castellanos, César G.
Format Journal Article
LanguageEnglish
Portuguese
Published Universidad de Medellín 01.06.2013
Subjects
brain model
ENGINEERING, MULTIDISCIPLINARY
estimación
estimation
filtro de Kalman
Inverse problem
Kalman filter
modelo del cerebro
modelo fisiológico
modelo fisológico
physiological model
Problema inverso
estimation
filtro de Kalman
modelo fisológico
Kalman filter
Problema inverso
brain model
modelo del cerebro
estimación
physiological model
Inverse problem
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Summary:In this research article a dynamic estimation of neuronal activity and brain dynamics from electroencephalographic (EEG) signals is presented using a dual Kalman filter. The dynamic model for brain behavior is evaluated using physiological-based linear models. Filter performance is analyzed for simulated and clinical EEG data, over several noise conditions. As a result a better performance on the solution of the dynamic inverse problem is achieved, in case of time varying parameters compared with the system with fixed parameters and the static case. An evaluation of computational load is performed when predicted dynamic cases, estimated using the Kalman filter, are up to ten times faster than the static case.
ISSN:1692-3324
2248-4094
DOI:10.22395/rium.v12n22a15