基于分数低阶矩的非高斯噪声中诱发电位提取新方法

诱发电位（EP）信号榆测与分析技术是临床医学诊断神经系统损伤及病变的重要手段之一。传统的EP信号提取与分离方法中，通常认为EP信号中混入的EEG等噪声是高斯分布的。近年来一些研究表明TEEG信号具有一定的非高斯特性。α-稳定分布町以更好地描述实际应用中所遇到的具有显著脉冲特性的EEG噪声。文中简要介绍了稳定分们统计特性，推导了一种适用于EP信号分离提取的新算法。计算机模拟和分析表明，这种算法是一种在分数低阶α稳定分布背景噪声条件下具有良好韧性的EP信号分离提取方法。

Blind Estimation of Evoked Potentials Based on Fractional Lower Order Moments

Evoked potentials(EPs) have been widely used to quantify neurological system properties.Traditional EP analysis has been developed under the condition that the background noises in EP are Gaussian distributed.Recently it is accepted that Alpha stable distribution,a generalization of Gaussian,is better for modeling impulsive noises than Gaussian distribution in biomedical signal processing.Conventional blind separation and estimation method of evoked potentials is based on the second order statistics.In this paper,we modify conventional algorithms and analyze the stability and convergence performances of the new algorithm.The simulation experimental results show that the proposed new algorithm is more robust than the conventional algorithm.