| 基于多维统计分析方法的脑电消噪 |
| |
| 引用本文： | 吴小培,张道信. 基于多维统计分析方法的脑电消噪[J]. 安徽大学学报(自然科学版), 2002, 26(4): 69-75. DOI: 10.3969/j.issn.1000-2162.2002.04.015 |
| |
| 作者姓名： | 吴小培 张道信 |
| |
| 作者单位： | 1. 安徽大学,计算智能与信息处理教育部重点实验室,安徽,合肥,230039;中国科技大学,信息科学技术学院,安徽,合肥,230026;2. 安徽大学,计算智能与信息处理教育部重点实验室,安徽,合肥,230039 |
| |
| 基金项目： | 国家自然科学基金;60271024; |
| |
| 摘 要： | 基于二阶统计特性的主分量分析(PCA)和基于高阶统计特性的独立分量分析(ICA)是两种非常典型的多维统计分析方法.本文对PCA和ICA基本原理进行了简单介绍,并结合脑电消噪问题,对两种方法的性能和特点进行了比较.实验结果表明,在脑电消噪和特征增强等方面,独立分量分析方法具有明显的优势.
|
| 关 键 词： | 多维统计分析 主分量分析 独立分量分析 统计信号处理 脑电消噪 |
| 文章编号： | 1000-2162(2002)04-0069-07 |
Removal of artifacts from EEG based on multivariate analysis |
| |
| WU Xiao-pei ,,ZHANG Dao-xin. Removal of artifacts from EEG based on multivariate analysis[J]. Journal of Anhui University(Natural Sciences), 2002, 26(4): 69-75. DOI: 10.3969/j.issn.1000-2162.2002.04.015 |
| |
| Authors: | WU Xiao-pei ZHANG Dao-xin |
| |
| Affiliation: | WU Xiao-pei 1,2,ZHANG Dao-xin 1 |
| |
| Abstract: | Principle Component Analysis(PCA) and Independent Component Analysis(ICA) are two representative of multivariate data analysis methods , The first is a decorrelation-based method and only uses 1st and 2nd order statistics of random variables; the second is a independence-based method and use higher-order statistics. The basic theories and algorithms are briefly introduced in this paper. The detailed comparison of PCA and ICA is studied with the application of artifacts removal of EEG. The experiment results show that ICA has obvious advantages in artifacts cancellation and feature enhancement. |
| |
| Keywords: | multivariate data analysis principal component analysis independent component analysis statistic signal processing artifacts cancellation. |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ |
|
