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基于EMD和Hilbert变换的自发脑电信号特征提取
引用本文:吴婷,颜国正,钱炳锋. 基于EMD和Hilbert变换的自发脑电信号特征提取[J]. 北京生物医学工程, 2011, 30(4): 381-386. DOI: 10.3969/j.issn.1002-3208.2011.04.12
作者姓名:吴婷  颜国正  钱炳锋
作者单位:吴婷,WU Ting(上海电机大学机械学院,上海,200240;上海交通大学仪器科学工程系,上海,200240);颜国正,YAN Guozheng(上海交通大学仪器科学工程系,上海,200240);钱炳锋,QIAN Bingfeng(上海电机大学机械学院,上海,200240)
基金项目:上海市教育委员会重点学科建设项目,上海市教委晨光计划项目
摘    要:在脑机接口研究中,针对脑电信号的特征提取,提出一种基于EMD的Hilbert变换的方法.在变换过程中根据信号的局部特征自动选择基函数,求得信号在每个时间段的希尔波特谱;以时频窗口内的统计特性作为特征,利用Fisher距离选择最佳特征集输入分类器.最后利用BCI 2003竞赛数据,通过对特征矢量的可分性和识别精度两个指标的评估,表明了所提出方法的有效性.

关 键 词:脑机接口  脑电信号  经典模态分解  希尔伯特变换  特征提取

EEG Feature Extraction Based on Empirical Mode Decomposition and Hilbert Transform in Brain Computer Interface
WU Ting,YAN Guozheng,QIAN Bingfeng. EEG Feature Extraction Based on Empirical Mode Decomposition and Hilbert Transform in Brain Computer Interface[J]. Beijing Biomedical Engineering, 2011, 30(4): 381-386. DOI: 10.3969/j.issn.1002-3208.2011.04.12
Authors:WU Ting  YAN Guozheng  QIAN Bingfeng
Affiliation:1 School of Mechanical Engineering, Shanghai Dianji University, Shanghai 200240 2 School of Electronic, Information and Engineering, Shanghai Jiao Tong University, Shanghai 200240)
Abstract:In the study of brain computer interfaces,a method based on empirical mode decomposition (EMD) and Hilbert transformation was proposed. The method was used for the feature extraction of electroencephalogram. In this method, the basis function was selected automatically according to the local features of signal during the transforming process, the Hilbert spectrum was obtained in each period, and the statistical characteristics in time-frequency window were considered as features. Then the optimal feature sets were formed by the Fisher distance rule and put into the classification. The performance of the eigenvector was evaluated by separability and recognition accuracy with the data set of BCI 2003 competition,and classification results proved the effectiveness of the proposed method.
Keywords:brain computer interface  electroencephalogram  empirical mode decomposition  Hilbert transformation  feature extraction
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