基于共变的非高斯噪声中诱发电位的盲分离方法

Blind Estimation of Evoked Potentials Based on Covariations in Non-Gaussian Noise

诱发电位(EP)信号检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。传统的EP信号提取与分离方法中,通常认为EP信号背景中的EEG等噪声是高斯分布的。近年来一些研究表明EEG信号具有一定的非高斯性。α-稳定分布模型可以用来描述在诸如加速度撞击或缺氧窒息等特殊试验条件下具有显著脉冲特性的自发EEG信号,由于不存在二阶及二阶以上的统计量,本文对基于共变的盲分离方法进行了分析和讨论。仿真结果表明,该方法是一种在分数低阶稳定分布噪声条件下具有良好韧性的EP信号盲分离方法。

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 some researches indicate that electroencephalogram（EEG） is non-guassian in some especial conditions.Alpha stable distribution can model impulsive EEG in especial experimentation such as acceleration bump and devoid oxygen.In this paper,blind signals separation based on covariations is analyzed and discussed by the nonexistence of the finite second or higher order statistic.The simulation experimental results show that the method has good performance to separate Evoked potentials（EPs） from fractional lower order alpha stable distribution noise.