Evoked Potentials Estimation by using Higher Order Adaptive Neural Network filter

Evoked potentials are usually embedded in the ongoing electroencephalogram with a very low signal-to-noise ratio. The neural network filtering technique which has the advantage of complex mapping is one of the applicable methods for evoked potentials estimation. The backpropagation algorithm based on second order statistics is commonly used to adapt neural network filters. However it is easily influenced by additive Gaussian noise. In this study, a neural network filter with a modified back-propagation algorithm for higher order statistics was proposed. With higher-order statistics technique, additive Gaussian noise is suppressed to improve the performance of evoked potentials estimation