传感技术学报2016，Vol.29Issue(6)：802-807,6.DOI:10.3969/j.issn.1004-1699.2016.06.003
多任务运动想象脑电特征的融合分类研究
Research of Fusion Classification of EEG Features for Multi-Class Motor Imagery
摘要
Abstract
In view of the problems of pattern simplification,low accuracy of classification and poor practicability in motor imagery BCI,they improve feature extraction method to common spatial pattern(CSP),and the support vector machine(SVM)is used to carry out multi-class classification,combining with the CSP to classify the feature signal of EEG.Firstly,they select EEG signal in the specific channel to do wavelet decomposition and reconstruction,in or⁃der to remove redundant information;Secondly,they improve the method,by doing subtractions between different characteristic parameters,and obtain obvious characteristics of EEG;Finally,the SVM is used to carry out multi-task classification,combining with the CSP to classify the feature signal of EEG. Using BCI competition data,the four kinds of motor imagery tasks of left hand,right hand,tongue and feet are identified based on EEG signals. Ex⁃perimental results show that the correct rate of classifying is 90.9% for maximum,the average accuracy rate is 86.4%,the Kappa coefficient is 0.8867,and the information transmission rate was 0.68bit/trial and the method can extract EEG features effectively and achieve better classification to a multi-task motor imagery of EEG signals.关键词
脑机接口/运动想象/特征提取和分类/小波变换/共空间模式/支持向量机Key words
BCI/motor imagery/feature extraction and classification/wavelet transform/common spatial pattern/support vector machine分类
信息技术与安全科学引用本文复制引用
张焕,乔晓艳..多任务运动想象脑电特征的融合分类研究[J].传感技术学报,2016,29(6):802-807,6.基金项目
国家自然科学基金项目（81403130）；山西省自然科学基金项目 （）
