基于独立分量分析的诱发电位信号噪声分离方法

诱发电位(EP)信号的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之一。但是，从人体体表所得到的EP信号含有大量的噪声，最典型的噪声是人体自发产生的脑电图信号(EEG)。因此，为利用EP信号诊断神经系统的损伤和病变，需要从混合信号中去除EEG等噪声。独立分量分析(ICA)是一种新近发展起来的统计信号处理方法。本文把ICA方法应用于EP信号的噪声消除，并与传统的自适应滤波方法进行了比较。计算机模拟表明，采用ICA方法进行信号噪声分离的结果明显优于自适应滤波方法。

Independent Component Analysis Based Signal and Noise Separation for Evoked Potentials

The detection and analysis technology of evoked potentials(EPs) is an important means in clinical diagnosis for injury or disease of the central nervous system. Generally, the EP signals obtained from the body surface are always contaminated by heavy noises. The most typical one is the spontaneous electroencephalogram(EEG). Therefore, it is necessary to remove such noises like EEG from mixed signals in order to detect the injury or disease reliably. The independent component analysis(ICA) is a newly developed statistical signal processing method. In this paper we introduces how to remove noises in EP signals using an ICA based method, and the results are compared with the results obtained using traditional adaptive filtering. It is clear from computer simulations that the ICA based method is much more effective for signal and noise separation than the method of adaptive filtering.