ICA在视觉诱发电位的少次提取与波形分析中的应用

被引：50

作者：

洪波

唐庆玉

杨福生

潘映辐

陈葵

铁艳梅

机构：

[1] 清华大学电机系!北京

[2] 北京友谊医院!北京

来源：

中国生物医学工程学报 | 2000年 / 03期

关键词：

独立分量分析; 少次提取; 人工神经网络;

D O I：

暂无

中图分类号：

R318.19 [其他];

学科分类号：

080502 ;

摘要：

本文提出一种基于扩展的独立分量分析 (ICA)算法的视觉诱发响应少次提取方法。经与目前临床通用的相干平均法比较 ,只经三次平均 ,在波形整体和P10 0潜伏期的提取上 ,效果显著 ,获得医师欢迎 ,很有进一步开发潜力。

引用

页码：334 / 341

页数：8

共 10 条

[1]

An information maximization approach to blind separation and blind deconvolution. Bell AJ,et al. Neural Computation . 1995

[2]

Blind signal separation: statistical principles. Cardoso JF. Proceedings of the IEEE . 1998

[3]

Robust learning algorithm for blind separation of signals. Cichocki A,et al. Electronics Letters . 1994

[4]

Adaptive blind signal processing-Neural network approaches. Amari S,et al. Proceedings of the IEEE . 1998

[5]

Spatially Independent Activity Patterns in Functional Magnetic Resonance Imaging Data During the Stroop Color-naming Task. McKeown MJ,et al. Proceedings of the National Academy of Sciences of the United States of America . 1998

[6]

[7]

Blind separation of auditory event-related brain responses into independent components. Makeig S,et al. Proceedings of the National Academy of Sciences of the United States of America . 1997

[8]

Independent component analysis using an extended infomax algorithm for mixed Subgaussian and Supergaussian sources. Lee TW,et al. Neural Computation . 1999

[9]

Independent component analysis, A new concept. Comon P. Signal Processing . 1994

[10]

Blind source separation of more sources than mixtures using overcomplete representations. Lee TW,et al. IEEE Signal Processing Letters . 1999

← 1 →