Bayesian radial basis function network for modeling fMRI data

Noisy and nonlinear nature make fMRI signal processing a challenging problem. We proposed and analyzed the Bayesian trained radial basis function (RBF) neural network in fMRI data processing. The method, which determines the regularization parameter in RBF network automatically by Bayesian learning, is especially suitable for fMRI data processing. Both simulated and real fMRI data were tested. Results show that this approach could model fMRI signals and remove the slowly varying drift in the data sets as well.