T Qiu1, H Wang, Y Zhang, H Bao. 1. Department of Electronic Engineering, Dalian University of Technology, Dalian, China. qiutsh@sohu.com
Abstract
OBJECTIVES: To improve the latency change estimation of evoked potentials (EP) under the lower order alpha-stable noise conditions by proposing and analyzing a new adaptive EP latency change detection algorithm (referred to as the NLST). METHODS: The NLST algorithm is based on the fractional lower order moment and the nonlinear transform for the error function. The computer simulation and data analysis verify the robustness of the new algorithm. RESULTS: The theoretical analysis shows that the iteration equation of the NLST transforms the lower order alpha-stable process en (k) into a second order moment process by a nonlinear transform. The simulations and the data analysis showed the robustness of the NLST under the lower order alpha-stable noise conditions. CONCLUSIONS: The new algorithm is robust under the lower order alpha-stable noise conditions, and it also provides a better performance than the DLMS, DLMP and SDA algorithms without the need to estimate the alpha value of the EP signals and noises.
OBJECTIVES: To improve the latency change estimation of evoked potentials (EP) under the lower order alpha-stable noise conditions by proposing and analyzing a new adaptive EP latency change detection algorithm (referred to as the NLST). METHODS: The NLST algorithm is based on the fractional lower order moment and the nonlinear transform for the error function. The computer simulation and data analysis verify the robustness of the new algorithm. RESULTS: The theoretical analysis shows that the iteration equation of the NLST transforms the lower order alpha-stable process en (k) into a second order moment process by a nonlinear transform. The simulations and the data analysis showed the robustness of the NLST under the lower order alpha-stable noise conditions. CONCLUSIONS: The new algorithm is robust under the lower order alpha-stable noise conditions, and it also provides a better performance than the DLMS, DLMP and SDA algorithms without the need to estimate the alpha value of the EP signals and noises.