Neural adaptive filters for estimating brainstem auditory evoked potential

摘要

This paper presents Neural Adaptive Filters (NAF's) for estimating brainstem auditory evoked potential (BAEP). The method is evaluated by using simulation and human data. It was observed that a significant improvement in waveform estimation,compared with the ensemble averaging and time varying adaptive filter (TVAF), can be achieved by the neural adaptive filters. In this work, a multilayer perceptron network (MLP) trained with the backpropagation learning rule and Radial Basis FunctionNetwork (RBFN) trained with stochastic gradient-based algorithm were employed for neural filter implementation. It was found that the RBFN give rises to improvements in BAEP estimation over the MLP.