ABSTRACT Evoked potentials (EPs) have been widely used to quantify neurological system properties. Tra-ditional EP analysis methods are developed under the condition that the background noises in EP are Gaussian distributed. Alpha stable distribution, a generalization of Gaussian, is better for modeling impulsive noises than Gaussian distribution in biomedical signal proc-essing. Conventional blind separation and es-timation method of evoked potentials is based on second order statistics or high order Statis-tics. Conventional blind separation and estima-tion method of evoked potentials is based on second order statistics (SOS). In this paper, we propose a new algorithm based on minimum dispersion criterion and fractional lower order statistics. The simulation experiments show that the proposed new algorithm is more robust than the conventional algorithm.
Cite this paper
nullZha, D. (2008) New blind estimation method of evoked potentials based on minimum dispersion criterion and fractional lower order statistics. Journal of Biomedical Science and Engineering, 1, 91-97. doi: 10.4236/jbise.2008.12015.
 R. R. Gharieb, A. Cichocki. (2001) Noise reduction in brain evoked potentials based on third-order correlations. IEEE Transactions on Biomedical Engineering, 48(5): 501-512.
 C. E. Davila, R. Srebro, and I. A. Ghaleb. (1998) Optimal detection of visual evoked potential. IEEE Transaction on Biomedical Engi-neering, 45(6): 800–803.
 Yanwu Zhang,Yuanliang Ma (1997) CGHA for principal component extraction in the complex domain. IEEE. Trans. on Neural Net-work,vol 8,No.5.
 Mutihac, R. Van Hulle, M.M. (2003) PCA and ICA neural imple-mentations for source separation - a comparative study. Proceedings of the International Joint Conference on Neural Networks, Volume: 1 , 20-24.
 C. L. Nikias and M. shao. (1995) Signal Processing with Al-pha-Stable Distributions and Applications. New York: John Wiley & Sons Inc.
 M. shao and C. L. Nikias. (1993) Signal Processing with fractional lower order moments: stable processes and their applications. Pro-ceedings of IEEE, Vol.81, No.7, 986-1010.
 G. Samorodnitsky, M. S. Taqqu. (1994) Stable Non-Gaussian Ran-dom Process: Stochastic Models with Infinite Variance. New York: Chapman and Hall.
 Xuan Kong,Tianshuang Qiu. (1999) Adaptive Estimation of Latency Change in Evoked Potentials by Direct Least Mean p-Norm Time-Delay Estimation. IEEE Transactions on Biomedical Engi-neering, vol. 46, No. 8, August.
 N. Hazarika, A. C. Tsoi, and A. A. Sergejew. (1997) Nonlinear considerations in EEG signal classification. IEEE Trans. Signal Processing, vol. 45, pp. 829–936.
 X. Ma and C. L. Nikias. (1996) Joint estimation of time delay and frequency delay in impulsive noise using fractional lower-order sta-tistics. IEEE Trans. Signal Processing, vol. 44, pp. 2669–2687, Nov.
 X. Kong and N. V. Thakor. (1996) Adaptive estimation of latency changes in evoked potentials. IEEE Trans. Biomed. Eng., vol. 43, pp. 189–197, Feb.
 C. A. Vaz and N. V. Thakor. (1989) Adaptive Fourier estimation of time varying evoked potentials. IEEE Trans. Biomed. Eng., vol. 36, pp. 448–455, Apr.
 Winter, S.; Sawada, H.; Makino, S. (2003) Geometrical under-standing of the PCA subspace method for overdetermined blind source separation.Acoustics, Speech, and Signal Processing.
 Preben Kidmose. (2001) Blind separation of heavy tail signals. Technical university of denmark ,IMM-Phd, LYNGBY
 Juha Karhumen;Erkki Oja;Liuyue Wang;Ricardo Vigario;Jyrki Joutsensalo. (1997) A class of neural networks for independent component analysis. IEEE. Trans. on Neural Network,vol 8,No.3.