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Linear and Nonlinear Estimation models applied to Hemodynamic Model


Technical Report


The relation between BOLD signal and neural activity is still poorly understood. The Gaussian Linear Model known as GLM is broadly used in many fMRI data analysis for recovering the underlying neural activity. Although GLM has been proved to be a really useful tool for analyzing fMRI data it can not be used for describing the complex biophysical process of neural metabolism. In this technical report we make use of a system of Stochastic Differential Equations that is based on Buxton model [1] for describing the underlying computational principles of hemodynamic process. Based on this SDE we built a Kalman Filter estimator so as to estimate the induced neural signal as well as the blood inflow under physiologic and sensor noise. The performance of Kalman Filter estimator is investigated under different physiologic noise characteristics and measurement frequencies.

Author(s): Theodorou, E.
Book Title: Technical Report-2005-1
Year: 2005

Department(s): Autonomous Motion
Bibtex Type: Technical Report (techreport)

Address: Computational Action and Vision Lab University of Minnesota
Cross Ref: p10160
Note: clmc

Links: PDF


  title = {Linear and Nonlinear Estimation models applied to Hemodynamic Model},
  author = {Theodorou, E.},
  booktitle = {Technical Report-2005-1},
  address = {Computational Action and Vision Lab University of Minnesota},
  year = {2005},
  note = {clmc},
  crossref = {p10160}