title: Estimation of dynamic neural activity using a Kalman filter approach based on physiological models.
abstract: This paper presents a new method to estimate dynamic neural activity from EEG signals. The method is based on a Kalman filter approach, using physiological models that take both spatial and temporal dynamics into account. The filter's performance (in terms of estimation error) is analyzed for the cases of linear and nonlinear models having either time invariant or time varying parameters. The best performance is achieved with a nonlinear model with time-varying parameters.
authors.affiliation: Faculty of Electrical and Electronic Engineering, Physics and Computer Science, Technological University of Pereira, Colombia. egiraldos@utp.edu.co
authors.forename: E
authors.initials: E
authors.lastname: Giraldo
authors.forename: A J
authors.initials: AJ
authors.lastname: den Dekker
authors.forename: G
authors.initials: G
authors.lastname: Castellanos-Dominguez
mesh_terms: D000465:Algorithms; D001931:Brain Mapping / Q000379:methods; D003198:Computer Simulation; D004569:Electroencephalography / Q000379:methods*; D006439:Hemodynamics; D006801:Humans; D016014:Linear Models; D008279:Magnetic Resonance Imaging / Q000379:methods; D015233:Models, Statistical; D009010:Monte Carlo Method; D016011:Normal Distribution; D015203:Reproducibility of Results; D013997:Time Factors