Application of normalized RBF neural network to real-time VEP signal detection in noise

Abstract

The problem of real time signal detection in the noise and its applications to the denoising single-trial evoked potentials (EP) was investigated. The main objective is to estimate the amplitude and the latency of the single trial EP response without losing the individual properties of each epoch, which is important for practical clinical applications. Based on the radial basis function neural network (RBFNN), a method in terms of normalised RBFNN was proposed to obtain preferable results against other nonlinear methods such as ANC with RBFNN prefilter and RBFNN. The performance of the proposed methods was also evaluated with MSE and the ability of tracking peaks. The experimental results provide convergent evidence that the NRBFNN can significantly attenuate the noise and successfully identify the variance between trials. Both simulations and real signal analysis show the applicability and the effectiveness of the proposed algorithm.