Estimation of brainstem auditory evoked potentials using a nonlinear adaptive filtering algorithm

摘要: In this study, we introduce a novel nonlinear system not only for tracking of both the latency and amplitude variations in brainstem auditory evoked potential (BAEP), but also reduction single-trial numbers BAEP pattern extraction process. Trial-to-trial (AEP) are very important quantifying dynamical properties nervous specifying group-specific effects clinical applications. Due to dynamics AEP, adaptive filtering technique is considered as powerful tool such variations. Therefore, have designed wavelet network-based filter (WaNe-NAF) satisfying asymptotic stability sense Lyapunov. The simulation results verified that proposed WaNe-NAF can effectively track trial-to-trial We compared with most widely used ensemble averaging using real measured human data. shows promise requiring less number ensembles than conventional method attain adequate signal quality. As result, suggested AEP acquisition processing systems.