| 独立分量提取的在线算法及其应用 |
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| 引用本文： | 张道信,吴小培,沈谦,郭晓静.独立分量提取的在线算法及其应用[J].系统仿真学报,2004,16(1):17-19,34. |
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| 作者姓名： | 张道信 吴小培 沈谦 郭晓静 |
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| 作者单位： | 安徽大学计算机科学与信息工程学院,合肥,230039 |
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| 基金项目： | 国家自然科学基金项目(60271024)，安徽省自然科学基金项目(0043214) |
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| 摘 要： | 独立分量分析(ICA)根据信源统计独立特性对观测信号进行盲分离运算，由于其独特的分离性能而受到广泛关注，但目前其实现的算法普遍采用批处理的形式。文章探讨了在线ICA算法，并将其应用于脑电信号伪迹消除中，实验结果表明，丈中所提出的在线ICA算法具有良好的稳健性和盲源分离性能。
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| 关 键 词： | 独立分量分析 盲源分离 脑电信号 在线算法 |
| 文章编号： | 1004-731X(2004)01-0017-03 |
Online Algorithm of Independent Component Analysis and Its Application |
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| ZHANG Dao-xin,WU Xiao-pei,SHEN Qian,GUO Xiao-jing.Online Algorithm of Independent Component Analysis and Its Application[J].Journal of System Simulation,2004,16(1):17-19,34. |
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| Authors: | ZHANG Dao-xin WU Xiao-pei SHEN Qian GUO Xiao-jing |
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| Abstract: | Independent component analysis (ICA) processes blind separation according to the statistical characteristic of sources. Its particular property has been well concerned, but the presented algorithms are almost by means of batch process. In this paper, we introduce an approach of online algorithm of Infomax, and then it is used for the EEG artifacts removing. The experiment results show that the online algorithm of Infomax is robust and performs blind source separation (BSS) well. |
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| Keywords: | independent component analysis blind source separation EEG online algorithm |
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