[1]文念,黄丽亚,于涵,等. 基于ICA和聚类的EEG脑源定位研究[J].计算机技术与发展,2015,25(05):228-232.
　WEN Nian,HUANG Li-ya,YU Han,et al. EEG Sources Localization Based on Independent Component Analysis and Clustering[J].,2015,25(05):228-232.
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 基于ICA和聚类的EEG脑源定位研究()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
25
期数:
2015年05期
页码:
228-232
栏目:
应用开发研究
出版日期:
2015-05-10

文章信息/Info

Title:
 EEG Sources Localization Based on Independent Component Analysis and Clustering
文章编号:
1673-629X（2015)05-0228-05
作者:
 文念; 黄丽亚; 于涵; 杨晨; 杨俊宇
 南京邮电大学 电子科学与工程学院
Author(s):
 WEN Nian;  HUANG Li-ya ; YU Han;  YANG Chen;  YANG Jun-yu
关键词:
 ; 脑电; 独立成分分析; 多偶极子源定位; 聚类分析
Keywords:
 ; EEG ; ICA ; multiple-dipole localization;  clustering analysis
分类号:
TP301
文献标志码:
A
摘要:
 将独立成分分析(ICA)与聚类Cluster相结合应用到脑电的多偶极子源定位，先采用快速独立成分分析(fastICA)得到多个独立成分( ICs)，然后通过聚类方法对得到的ICs进行特征提取和有效归类。该方法在去除脑电伪迹和噪声干扰的同时解决了ICA分解后独立成分的选取依赖于经验的局限性。以上消除了伪迹干扰和ICs的不确定性选择对源定位性能的影响，为源定位创造了有利条件。对ICs进行定位也使得整个定位过程像单偶极子定位一样稳定简单。仿真实验中源定位效果得到改善，表明了该方法的有效性。
Abstract:
 A multiple-dipole localization procedure combined Independent Component Analysis ( ICA) and cluster analysis for EEG ( e-lectroencephalograph) is suggested in this paper. Firstly,the fastICA is applied to obtain Independent Components ( ICs) which are then utilized by cluster analysis for feature extraction and effective classification. Removing the brain artifacts and noise by cluster,this ap-proach overcomes the constraint that the selection of the independent components depends on experiences. The work above eliminates the artifact and the uncertainty of ICs effects on the source localization performance,and creates favorable conditions for source localization. Locating the ICs also makes the multiple-dipole localization simple and stable as the single dipole localization. The simulation shows that the effect of the source localization has been significantly enhanced,indicating the effectiveness of this method.

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更新日期/Last Update: 2015-07-27