Nonlinear Analysis of the BOLD Signal

Abstract

The linearized filtering approach to the hemodynamic system is limited in capturing the inherent nonlinearities of physiological systems. The nonlinear estimation method therefore should be thought of as a natural way to access the nonlinear data assimilation problem. In this paper, we present a nonlinear filtering algorithm which is computationally expensive compared to the existing linearization filtering algorithms, for hemodynamic data assimilation, to address the deficiencies inherent to linearization. Simultaneous estimation of the physiological states and the system parameters have been demonstrated in a simulated and real data. The method provides more reasonable inference about the parameters of models for hemodynamic data assimilation.

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