1. ESTIMATION OF NEURONAL ACTIVITY AND BRAIN DYNAMICS USING A DUAL KALMAN FILTER WITH PHYSIOLOGYCAL BASED LINEAR MODEL
- Author
-
Eduardo Giraldo and Cesar G. Castellanos
- Subjects
modelo fisológico ,Computer science ,Electroencephalography ,lcsh:Technology ,Control theory ,medicine ,Simulation ,physiological model ,filtro de Kalman ,medicine.diagnostic_test ,estimation ,Quantitative Biology::Neurons and Cognition ,lcsh:T ,Dynamics (mechanics) ,Linear model ,modelo del cerebro ,General Medicine ,Kalman filter ,Inverse problem ,brain model ,cerebro ,estimación ,Dual (category theory) ,Problema inverso ,Noise ,modelo fisiológico ,Filter (video) ,investigaciones ,lcsh:TA1-2040 ,lcsh:Engineering (General). Civil engineering (General) - Abstract
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.
- Published
- 2013