山东生物医学工程2002，Vol.21Issue(2)：4-6,3.
基于独立分量分析的生理信号盲源分离
Blind Separation of Biosignals Using Independent Component Analysis
摘要
Abstract
The independent component analysis(ICA)for blind source separation and the Extended ICA algorithm are introduced in this paper. The algorithm does not need to calculate the higher order statistics of signals,and has the property of fast convergence. The experimental results for separation of speech and EEG signals using the proposed algorithm is presented.关键词
盲源分离/独立分量分析/人工神经网络/极大似然估计/脑电/语音Key words
Blind source separation(BSS) Independent component analysis(ICA) Neural networks Maximum likelihood estimation Electroencephalogram(EEG) Speech分类
医药卫生引用本文复制引用
周卫东..基于独立分量分析的生理信号盲源分离[J].山东生物医学工程,2002,21(2):4-6,3.基金项目
山东省自然科学基金资助项目(NO.Y2000C25)) （NO.Y2000C25）
