计算机工程与应用2016，Vol.52Issue(18)：148-153,6.DOI:10.3778/j.issn.1002-8331.1411-0329
基于小波包和组合分类器的脑电信号分类
Classification of motor imagery task based on wavelet packet decomposition and combination of multiple classifiers
摘要
Abstract
In order to improve classification accuracy, this paper describes a novel method based on Wavelet Packet Decomposition(WPD)and voting combination of multiple classifiers to classify the Motor Imagery(MI)Electroencepha-logram(EEG)signals. First, the pre-processed MI data is decomposed into wavelet coefficients using WPD, from which features based on Relative Wavelet Packet Energy(RWPE)in all sub-bands are extracted;then, the L-2 norm of wavelet packet coefficients in special sub-bands at channels C3 and C4 is obtained according to the diversity of the hemispheric brainwave in different mental tasks;finally, features are combined to feed into a multiple classifier combination based on majority voting strategy for classification and correct rate of 92.85% is achieved. The experimental results indicate that the RWPE and L-2 norm effectively reflect the event-related desynchronization and synchronization(ERD and ERS)char-acteristics of left and right MI;the combined classifier improves the classification performance, which is superior to the single classifier.关键词
脑-机接口/特征提取/小波包分解/组合分类器/投票组合Key words
Brain-Computer Interface(BCI)/feature extraction/Wavelet Packet Decomposition(WPD)/multiple classifiers/voting combination分类
信息技术与安全科学引用本文复制引用
郭红想,严军,王典洪,余蓓蓓..基于小波包和组合分类器的脑电信号分类[J].计算机工程与应用,2016,52(18):148-153,6.基金项目
湖北省自然科学基金项目（No.2012055077）；中央高校基金项目（No.2012079108）。 （）
