Input-Output Modeling Between DBS Frequency Parameter and Beta Band Power Using an Autoregressive RBF Neural Network

As a good method to treat Parkinson's disease (PD), deep brain stimulation (DBS) is more and more widely used. Among them, the basal ganglia (BG) is selected as the target of stimulation. The adjustment of DBS parameters is helpful to improve side effects of DBS, especially the stimulation frequency and amplitude. Also, symptoms of PD are proved to be correlated with beta band power (13-35Hz). In the paper, we proposed the autoregressive radial basis function (RBF) neural network to identify the relationship between DBS frequency parameter and beta band power in a computational network model. The root mean square error (RMSE) and correlation coefficient between the actual beta power signal generated by the computational network model and the predicted value by the autoregressive RBF network were used as identification accuracy indexes. The highest accuracy of correlation coefficient can achieve 94.94%. This will help us to choose the appropriate stimulus parameters according to the beta power change in the future.