A tracing evoked potential estimator

The paper presents an adaptive Gaussian radial basis function neural networ k (RBFNN) for rapid estimation of evoked potential (EP). Usually, a recorde d EP is severely contaminated by background ongoing activities of the brain . Many approaches have been reported to enhance the signal-to-noise ratio ( SNR) of the recorded signal. However, non-linear methods are seldom explore d due to their complexity and the fact that the non-linear characteristics of the signal are generally hard to determine. An RBFNN possesses built-in non-linear activation functions that enable the neural network to learn any function mapping. An RBFNN was carefully designed to model the EP signal. It has the advantage of being linear-in-parameter, thus a conventional adap tive method can efficiently estimate its parameters. The proposed algorithm is simple so that its convergence behaviour and performance in signal-to-n oise ratio (SNR) improvement can be mathematically derived. A series of exp eriments carried out on simulated and human test responses confirmed the su perior performance of the method. In a simulation experiment, an RBFNN havi ng 15 hidden nodes was trained to approximate human visual EP (VEP). For de tecting human brain stem auditory EP (BAEP), the approach (40 hidden nodes and convergence rate=0.005) speeded up the estimation remarkably by using o nly 80 ensembles to achieve a result comparable to that obtained by averagi ng 1000 ensembles.