Hemodynamic response during motor imagery (MI) is studied extensively by functional magnetic resonance imaging (fMRI) technologies. To further understand the human brain functions under MI, a more precise classification of the brain regions corresponding to each brain function is desired. In this study, a Bayesian trained radial basis function (RBF) neural network, which determines the weights and regularization parameters automatically by Bayesian learning, is applied to make a precise classification of the hemodynamic response to the tasks during the MI experiment. To illustrate the proposed method, data with MI task performance from 1 subject was used. The results demonstrate that this approach splits the hemodynamic response to different tasks successfully. To further understand the human brain functions under MI, a more precise classification of the brain areas corresponding to each brain function is desired. In this study , a Bayesian trained radial basis function (RBF) neural network, which determines the weights and regularization parameters automatically by Bayesian learning, is applied to make a precise classification of the hemodynamic response to the tasks during the MI experiment. To illustrate the proposed method, data with MI task performance from 1 subject was used. The results demonstrates that this approach splits the hemodynamic response to different tasks successfully.