[1]黄丹丹,张少白.基于高时—频分辨率脑电信号特征提取方法分析[J].计算机技术与发展,2013,(02):135-138.
　HUANG Dan-dan,ZHANG Shao-bai.Electroencephalography Feature Extraction Analysis Based on High Time-frequency Resolution[J].,2013,(02):135-138.
点击复制

基于高时—频分辨率脑电信号特征提取方法分析()
分享到：

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2013年02期
页码:
135-138
栏目:
智能、算法、系统工程
出版日期:
1900-01-01

文章信息/Info

Title:
Electroencephalography Feature Extraction Analysis Based on High Time-frequency Resolution
文章编号:
1673-629X（2013）02-0135-04
作者:
黄丹丹; 张少白
南京邮电大学 计算机学院
Author(s):
HUANG Dan-dan; ZHANG Shao-bai
关键词:
脑电; 特征提取; 中心仿射滤波; Wigner-Ville分布
Keywords:
Electroencephalography; feature extraction; median affined filter; Wigner-Ville distribution
文献标志码:
A
摘要:
分析脑电信号主要采用时频分析法,其中交叉项和分辨率是相互矛盾的两个因素.基于高时—频分辨率分析(High Time-Frequency Resolution Analysis,HTFRA)方法能够将这两者相结合.该方法以维格纳-威尔分布(Wigner-Ville Distribution,WVD)为基础,对Wigner-Ville分布的结果利用中心仿射滤波法进行非线性滤波,有效地消除了Wigner-Ville分布的交叉项干扰,而不影响信号的分辨率.对仿真信号采用传统的短时付氏变换、Wigner-Ville 分布及HTFRA 求时频能量分布,结果显示:HTFRA较传统的方法更清晰地反映信号在时频域内的能量变化.该方法使得脑电信号适用于各种分类算法
Abstract:
The way to analyze EEG signals is mainly the method of time-frequency analysis,of which cross terms and resolution are two contradictory factors. However,the high time-frequency resolution analysis (HTFRA) can combine both of them. The HTFRA is based on the Wigner-Ville distribution and effectively eliminate the cross of Wigner-Ville distribution without affecting the signal resolution by using the median affined filter method for nonlinear filtering. The simulated signals are analyzed with short-time Fourier transform,Wign-er-Ville distribution,and HTFRA respectively. The results indicate that HTFRA gives a better energy distribution in the time-frequency field compared with the traditional methods. This method is better applicable to the classification of EEG

相似文献/References:

[1]田昕辉 李成基.带有短语切分的中文文本分类方法[J].计算机技术与发展,2010,(01):5.
　TIAN Xin-hui,LEE Sung-kee.Phrase Segmentation for Chinese Text Classification[J].,2010,(02):5.
[2]何小娜 逄焕利.基于二维直方图和改进蚁群聚类的图像分割[J].计算机技术与发展,2010,(03):128.
　HE Xiao-na,PANG Huan-li.Image Segmentation Based on Improved Ant Colony Clustering and Two- Dimensional Histogram[J].,2010,(02):128.
[3]罗林波 陈绮.氨基酸序列特征提取方法研究[J].计算机技术与发展,2010,(02):206.
　LUO Lin-bo,CHEN Qi.Research of Feature Extraction Methods of Amino Acid Sequence[J].,2010,(02):206.
[4]姜鹤 陈丽亚.SVM文本分类中一种新的特征提取方法[J].计算机技术与发展,2010,(03):17.
　JIANG He,CHEN Li-ya.A New Feature Selection Method in SVM Text Categorization[J].,2010,(02):17.
[5]毛雁明 兰美辉 王运琼 冯乔生.一种改进的基于Harris的角点检测方法[J].计算机技术与发展,2009,(05):130.
　MAO Yan-ming,LAN Mei-hui,WANG Yun-qiong,et al.An Improved Corner Detection Method Based on Harris[J].,2009,(02):130.
[6]赵辉 张鹏.网络异常的主动检测与特征分析[J].计算机技术与发展,2009,(08):159.
　ZHAO Hui,ZHANG Peng.Active Detection and Feature Analysis About Network Anomaly[J].,2009,(02):159.
[7]汤婷 吴小培 项明.指纹图像增强与特征提取[J].计算机技术与发展,2009,(01):81.
　TANG Ting,WU Xiao-pei,XIANG Ming.Fingerprint Image Enhancement and Minutiae Extraction[J].,2009,(02):81.
[8]张国富 凌捷 彭辉 谷保平.基于支持向量机的手写签名研究[J].计算机技术与发展,2008,(05):57.
　ZHANG Guo-fu,LING Jie,PENG Hui,et al.Research of Handwritten Signature Based on SVM[J].,2008,(02):57.
[9]黄国宏 刘刚.一种新的基于Fisher准则的线性特征提取方法[J].计算机技术与发展,2008,(05):227.
　HUANG Guo-hong,LIU Gang.A New Linear Feature Extraction Method Based on Fisher Criterion[J].,2008,(02):227.
[10]黄国宏 刘刚.一种新的基于DCT变换的线性判别分析[J].计算机技术与发展,2008,(06):97.
　HUANG Guo-hong,LIU Gang.A Novel Linear Discriminant Analysis Based on DCT[J].,2008,(02):97.
[11]李坤 褚蕾蕾 朱世东 吴小培.基于mu节律能量的运动意识分类研究[J].计算机技术与发展,2006,(08):157.
　LI Kun,CHU Lei-lei,ZHU Shi-dong,et al.Study of Classification of Motor Imageries Based on Energy of mu Rhythm of EEG[J].,2006,(02):157.

更新日期/Last Update: 1900-01-01