CLASSIFICATIONS OF EEC SIGNALS FOR MENTAL TASKS USING ADAPTIVE RBF NETWORK

摘要

Objective This paper presents classifications of mental tasks based on EEC signals using an adaptive Radial Basis Function (RBF) network with optimal centers and widths for the Brain-Computer Interface (BCD schemes. Methods Initial centers and widths of the network are selected by a cluster estimation method based on the distribution of the training set. Using a conjugate gradient descent method, they are optimized during training phase according to a regularized error function considering the influence of their changes to output values. Results The optimizing process improves the performance of RBF network, and its best cognition rate of three task pairs over four subjects achieves 87.0 percent. Moreover, this network runs fast due to the fewer hidden layer neurons. Conclusion The adaptive RBF network with optimal centers and widths has high recognition rate and runs fast. It may be a promising classifier for on-line BCI scheme.