Independent component analysis: an introduction

被引：263

作者：

Stone, JV ^{[1
]}

机构：

[1] Univ Sheffield, Dept Psychol, Sheffield S10 2TP, S Yorkshire, England

来源：

TRENDS IN COGNITIVE SCIENCES | 2002年 / 6卷 / 02期

关键词：

D O I：

10.1016/S1364-6613(00)01813-1

中图分类号：

B84 [心理学]; C [社会科学总论]; Q98 [人类学];

学科分类号：

03 ; 0303 ; 030303 ; 04 ; 0402 ;

摘要：

Independent component analysis (ICA) is a method for automatically identifying the underlying factors in a given data set. This rapidly evolving technique is currently finding applications in analysis of biomedical signals (e.g. ERP, EEG, fMRI, optical imaging), and in models of visual receptive fields and separation of speech signals. This article illustrates these applications, and provides an informal introduction to ICA.

引用

页码：59 / 64

页数：6

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