The hemodynamic model is a fundamental approach for successfully monitoring and possibly forecasting brain activities in the biomedical engineering field. The hemodynamic model describes the inner scenario of a blood flowing voxel in a human brain and it is most popular hypothesis on the brain related research activities. The hemodynamic model has nonlinearities in nature. The solution of such type hemodynamic model is researchable work.

There are many model solving algorithms by using fMRI images; recently, Haifeng Wu presented Confounds Square-root Cubature Kalman Filtering and Confounds Square-root Cubature Smoothing (CSCKF-CSCKS) is the latest approach for solving hemodynamic models. The relative accuracy of this model was shown 84%. In this article, in order to achieve better accuracy, the data analysis and model algorithms are presented differently and find new result that was not mentioned earlier.

The data analysis of this experiment shows that if the maximum number of iterations increases three times, the overall accuracy for solving the hemodynamic model raises by 5.76% under the exact type of fMRI measurements used in both cases. We also represent a formula for calculating a relative error to evaluate the performance of these estimations.

A recommendation is made for solving the hemodynamic model algorithm by using fMRI images to get better performance for estimating the model's biophysical parameters and hidden states. As a result, we will find out more accurate scenario of a specific region of human brain by using fMRI images of that region.