Non-Linear Transform-Based Robust Adaptive Latency Change Estimation of Evoked Potentials^[*]

Summary

Objectives: To improve the latency change estimation of evoked potentials (EP) under the lower order -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 α-stable process e_n (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 α-stable noise conditions.

Conclusions: The new algorithm is robust under the lower order -stable noise conditions, and it also provides a better performance than the DLMS, DLMP and SDA algorithms without the need to estimate thevalue of the EP signals and noises.

Keywords

Evoked potentials - adaptive - latency - signal detection - nonlinear

^* This work is supported by the National Science Foundation of China by Grant 30170259 and Grant 60172072, National 973 project by Grant 2001 CCA 00700, and the Science and Technology Foundation of the Liaoning Province of China by Grant 2001101057.

• References

• 1 Kong X, Qiu T. Latency change estimation for evoked potentials via frequency selective adaptive phase spectrum analyzer. IEEE Trans on Biomedical Engineering 1999; 46: 1004-12.
• 2 Vaz CV, Thakor NV. Adaptive Fourier estimation of time varying evoked potentials. IEEE Trans on Biomedical Engineering 1989; 36: 448-55.
• 3 Kong X, Thakor NV. Adaptive estimation of latency changes in evoked potentials. IEEE Trans on Biomedical Engineering 1996; 43: 189-97.
• 4 Gupta L. et al. Nonlinear alignment and averaging for estimating the evoked potential. IEEE Trans on Biomedical Engineering, 1996; 43: 341-7.
• 5 McEwen JA, Anderson GB. Modeling the stationary and Gaussianity of spontaneous electroencephalogram activity,. IEEE Trans on Biomedical Engineering 1975; 22: 361-9.
• 6 Hazarika N, Tsoi AC, Sergejew AA. Nonlinear consideration in EEG signal classification. IEEE Trans on Signal Processing 1998; 45: 829-36.
• 7 Kong X, Qiu T. Adaptive estimation of latency change in evoked potentials by direct least mean p-norm time delay estimation. IEEE Trans on Biomedical Engineering 1999; 46: 994-1003.
• 8 Matson D, Weiss M. Evoked potential analysis of impact acceleration experiments. in Proc. AGARD Conf., Neuilly Sur Seine, France 1988; 432 (28) 1-13.
• 9 Ma X, Nikias CL. Joint estimation of time delay and frequency delay in impulsive noise using fractional lower order statistics. IEEE Trans on Signal Processing 1996; 44: 2669-87.
• 10 Nikias CL, Shao M. Signal Processing with Alpha-Stable Distributions and Applications. New York: John Wiley & Sons Inc.; 1995
• 11 Etter DM, Stearn SD. Adaptive estimation of time delay in sampled system. IEEE Trans on Acoustics, Speech, Signal Processing 1981; 29: 582-7.
• 12 Qiu T, Kong X. A new adaptive latency change estimation algorithm for evoked potentials under non-Gaussian noise condition. Proceedings of the IEEE-EMBS Asia-Pacific Conference on Biomedical Engineering,. Hangzhou: 2000: 135 6.:
• 13 Kong X, Qiu T. Latency change estimation for evoked potentials: a comparison of algorithms. Medical & Biomedical Engineering & Computing 2001; 39: 208-24.
• 14 Tsihrintzis GA, Nikias CL. Fast estimation of the parameters of alpha-stable impulsive interference,. IEEE Trans on Signal Processing 1996; 44: 1492-503.