基于时-频-空间域的运动想象脑电信号特征提取方法研究  

Feature Extraction of Motor Imagery Electroencephalography Based on Time-frequency-space Domains

作　　者：王月茹[1,2] 李昕[1,2,3] 李红红[1,2] 邵成成[1,2] 应立娟[1,2] 吴水才[3] 

机构地区：[1]燕山大学生物医学工程研究所,秦皇岛066004 [2]河北省测试计量技术及仪器重点实验室,秦皇岛066004 [3]北京工业大学生命科学与生物工程学院,北京100124

出　　处：《生物医学工程学杂志》2014年第5期955-961,共7页Journal of Biomedical Engineering

基　　金：河北省优秀专家出国资助项目;河北省教育厅重点项目资助(ZD2010115);河北省自然科学基金资助项目(F2014203204);中国博士后科学基金资助项目(2014M550582)

摘　　要：脑机接口(BCI)是在人或动物脑与外部设备间建立的直接连接通路,信号分析功能模块是其核心部分,其中特征提取算法的效果如何是脑电图(EEG)信号分析算法的关键。EEG信号本身信噪比低,传统的EEG特征提取方法存在着缺少空间信息,需要的特征量个数较多,分类正确率低等不足。针对以上问题,本文提出了一种基于小波和独立分量分析(ICA)的时间-频率-空间EEG特征的提取方法,分别用离散小波变换(DWT)和ICA提取时频域特征和空域特征。并用支持向量机(SVM)和遗传算法(GA)相结合的方法对提取的特征进行分类。实验对比结果表明,所提出的方法有效地克服了传统的时频特征提取方法空间信息描述不足等问题,对于2003年BCI竞赛数据datasetⅢ分析,最高分类正确率为90.71%。The purpose of using brain-computer interface（BCI）is to build a bridge between brain and computer for the disable persons,in order to help them to communicate with the outside world.Electroencephalography（EEG）has low signal to noise ratio（SNR）,and there exist some problems in the traditional methods for the feature extraction of EEG,such as low classification accuracy,lack of spatial information and huge amounts of features.To solve these problems,we proposed a new method based on time domain,frequency domain and space domain.In this study,independent component analysis（ICA）and wavelet transform were used to extract the temporal,spectral and spatial features from the original EEG signals,and then the extracted features were classified with the method combined support vector machine（SVM）with genetic algorithm（GA）.The proposed method displayed a better classification performance,and made the mean accuracy of the Graz datasets in the BCI Competitions of 2003 reach 96%.The classification results showed that the proposed method with the three domains could effectively overcome the drawbacks of the traditional methods based solely on time-frequency domain when the EEG signals were used to describe the characteristics of the brain electrical signals.

关 键 词：脑电图 特征提取 独立分量分析 小波变换 支持向量机 

分 类 号：TN911.73[电子电信—通信与信息系统]