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Publication Details

Reference TypeReport
Author(s)Theodorou, E.
TitleLinear and Nonlinear Estimation models applied to Hemodynamic Model
Journal/Conference/Book TitleTechnical Report-2005-1
KeywordsStochastic Estimation, Kalman Filters, Shaping Filters, Hemodynamic model
Abstract 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.
Place PublishedComputational Action and Vision Lab University of Minnesota
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