Analysis of Time-Varying VEP Signal Using Rational RBF Network

This contribution studies the problem of denoising single-trial visual evoked potentials (VEP) signal. The main objective for VEP detection is to extract the change of the response and the corresponding latency without losing the individual properties of each trial of the signals, which is meaningful to clinic and practical application. Based on the radial basis function neural network (RBFNN), we proposed normalized RBFNN to obtain preferable results against other nonlinear methods: adaptive noise canceling (ANC) with RBFNN prefilter and RBFNN alone. These three approaches are compared with MSE and the ability of tracking peaks. The experimental results provide convergent evidence for the view that NRBFNN significantly attenuates noise and can successfully identify variance between trials