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Identification of nonlinear fMRI models using Auxiliary Particle Filter and kernel smoothing method
conference contribution
posted on 2012-01-01, 00:00 authored by Imali HettiarachchiImali Hettiarachchi, Shady MohamedShady Mohamed, Saeid NahavandiHemodynamic models have a high potential in application to understanding the functional differences of the brain. However, full system identification with respect to model fitting to actual functional magnetic resonance imaging (fMRI) data is practically difficult and is still an active area of research. We present a simulation based Bayesian approach for nonlinear model based analysis of the fMRI data. The idea is to do a joint state and parameter estimation within a general filtering framework. One advantage of using Bayesian methods is that they provide a complete description of the posterior distribution, not just a single point estimate. We use an Auxiliary Particle Filter adjoined with a kernel smoothing approach to address this joint estimation problem.
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Event
IEEE Engineering in Medicine and Biology Society. Conference (34th : 2012 : San Diego, California)Pagination
4212 - 4216Publisher
IEEELocation
San Diego, CaliforniaPlace of publication
Piscataway, N.J.Publisher DOI
Start date
2012-08-28End date
2012-09-01ISBN-13
9781424441198ISBN-10
1424441196Language
engPublication classification
E1 Full written paper - refereedTitle of proceedings
EMBC 2012 : Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology SocietyUsage metrics
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