| 基于独立分量分析的诱发电位信号提取方法 |
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| 引用本文： | 解宁,张旭秀,等.基于独立分量分析的诱发电位信号提取方法[J].大连铁道学院学报,2002,23(2):62-65. |
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| 作者姓名： | 解宁 张旭秀 |
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| 作者单位： | [1]大连理工学院电子与信息工程学院，辽宁大连116024 [2]大连铁道学院电气信息分院，辽宁大连116028 |
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| 基金项目： | 国家自然科学基金资助项目(30170259)，辽宁省科学技术基金资助项目(2001101057) |
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| 摘 要： | 诱发电位（EP）信号在检测神经系统状态时有重要意义，但EP信号总是没在自发脑电波（EEG）信号中，因此，为利用EP信号诊断神经系统的损伤和病变，需要从二的混合信号中提出EP信号。独立分量分析（ICA）是一种新近发展起来的信号分离方法。本反ICA方法应用于EP信号的提取，并与传统的滤波方法进行了比较。计算模拟表明，采用ICA方法进行噪声分离的结果明显优于信号滤波方法。
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| 关 键 词： | 独立分量分析 ICA 诱发电位 EP 信号分离 计算机模拟 信号检测 中枢神经系统 医学诊断 |
An Independent Component Analysis Based on Signal Separation Method for Evoked Potentials |
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| XIE Ning,ZHANG Run-xuan,QIU Tian-shuang,ZHANG Xu-xiu.An Independent Component Analysis Based on Signal Separation Method for Evoked Potentials[J].Journal of Dalian Railway Institute,2002,23(2):62-65. |
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| Authors: | XIE Ning ZHANG Run-xuan QIU Tian-shuang ZHANG Xu-xiu |
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| Abstract: | The waveform of Evoked Potential (EP) signals is remarkably important for evaluating the status of neural system. Removing or suppressing the noises in EPs is necessary for clinical applications, since they are always contaminated with ongoing electroencephalogram (EEC) and other noises. Independent Component Analysis (ICA) is a new way for signal separation. It is used to recover EP signals from EEG noises in this study. A comparison with a traditional filtering technology is also conducted. Computer simulation results show that the ICA based method is much better than the filtering technology in signal and noise separation. |
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| Keywords: | Independent Component Analysis (ICA) Evoked Potential(EP) signal separation |
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