Identification of nonlinear fMRI models using Auxiliary Particle Filter and kernel smoothing method

Hettiarachchi, Imali T., Mohamed, Shady and Nahavandi, Saeid 2012, Identification of nonlinear fMRI models using Auxiliary Particle Filter and kernel smoothing method, in EMBC 2012 : Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, IEEE, Piscataway, N.J., pp. 4212-4216, doi: 10.1109/EMBC.2012.6346896.

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Title Identification of nonlinear fMRI models using Auxiliary Particle Filter and kernel smoothing method
Author(s) Hettiarachchi, Imali T.ORCID iD for Hettiarachchi, Imali T. orcid.org/0000-0002-4220-0970
Mohamed, ShadyORCID iD for Mohamed, Shady orcid.org/0000-0002-8851-1635
Nahavandi, SaeidORCID iD for Nahavandi, Saeid orcid.org/0000-0002-0360-5270
Conference name IEEE Engineering in Medicine and Biology Society. Conference (34th : 2012 : San Diego, California)
Conference location San Diego, California
Conference dates 28 Aug.-1 Sep. 2012
Title of proceedings EMBC 2012 : Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Editor(s) [Unknown]
Publication date 2012
Conference series IEEE Engineering in Medicine and Biology Society Conference
Start page 4212
End page 4216
Total pages 5
Publisher IEEE
Place of publication Piscataway, N.J.
Keyword(s) analytical models
data models
estimation
Summary Hemodynamic 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.
ISBN 1424441196
9781424441198
Language eng
DOI 10.1109/EMBC.2012.6346896
Field of Research 090399 Biomedical Engineering not elsewhere classified
Socio Economic Objective 970109 Expanding Knowledge in Engineering
HERDC Research category E1 Full written paper - refereed
Persistent URL http://hdl.handle.net/10536/DRO/DU:30050978

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