Estimation of the Hemodynamic Response during Motor Imagery Using Bayesian RBF Neural Network

• Published in: 电子科技学刊 Vol. 8; no. 2; pp. 168 - 172
• Main Authors: Zheng-Yong Pan, Wei-Shuai Lü, Jing-Na Zhang, Wei Liao, Hua-Fu Chen
• Format: Journal Article
• Language: English
• Published: School of Applied Mathematics,University of Electronic Science and Technology of China,Chengdu 610054,China%Key Laboratory for Neuroinformation of Ministry of Education,School of Life Science and Technology,University of Electronic Science end Technology of China,Chengdu 610054,China 2010
• Subjects: functional magnetic resonance imaging(fMRI); motor imagery; radial basis function; Bayesian study
• ISSN: 1674-862X
• DOI: 10.3969/j.issn.1674-862X.2010.02.015

TN1; Hemodynamic response during motor imagery(MI)b 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 b 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.