电子学报2011，Vol.39Issue(5)：1025-1030,6.
在线脑机接口中脑电信号的特征提取与分类方法
Feature Extraction and Classification of EEG in Online Brain-Computer Interface
摘要
Abstract
In the study of brain-computer interface(BCI),a novel method of extracting electroencephalorgaphy (EEG) features based on discrete wavelet transform (DWT) and autoregressive (AR) model was proposed. First,the EEG signal was decomposed to three levels by Daubechies wavelet function and statistics of wavelet coefficients were computed. Also,the sixth-order AR coefficients of the EEG signal were estimated using Burg' s algorithm. Then, the combination features were used as an input vector for neural network (NN) classifier, support vector machine (SVM) classifier, and linear discriminant analysis (LDA) classifier.Performance of this feature extraction method was tested using the data set from BCI 2003 competition. The recognition rate was compared with the best result of the competition and the classification results showed the effectiveness of this algorithm. Moreover,applying this pattern recognition algorithm to online robot control system based on EFG, the average accuracy of 89.5% was obtained. This method provides a new idea for the study of online BCI system.关键词
在线脑机接口/运动想象/小波变换Key words
online brain-computer interface/ motor imagery/ wavelet transform分类
通用工业技术引用本文复制引用
徐宝国,宋爱国,费树岷..在线脑机接口中脑电信号的特征提取与分类方法[J].电子学报,2011,39(5):1025-1030,6.基金项目
国家863高技术研究发展计划(No.2009AA01Z311,2008AA04Z0202) （No.2009AA01Z311,2008AA04Z0202）
国家自然科学基金(No.60775057) （No.60775057）
中国博士后基金面上项目(No.20100481090) （No.20100481090）
