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 classifi...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.展开更多
基金supported by the National Natural Science Foundation of China under Grant No. 9082006 and 30770590Key Research Project of Science and Technology of MOE under Grant No. 107097863 Program under Grant No. 2008AA02Z4080
文摘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.
