电子学报2013，Vol.41Issue(1)：193-198,6.DOI:10.3969/j.issn.0372-2112.2013.01.33
一种新的基于小波包分解的EEG特征抽取与识别方法研究
A New Method of EEG Classification with Feature Extraction Based on Wavelet Packet Decomposition
摘要
Abstract
In order to improve accuracy of mental task classification, we propose a new method of EEG classification with feature extraction.First, the raw signals are decomposed by wavelet packet decomposition (WPD).Then, using wavelet packet entropy reflecting the distribution of signal energy in time and frequency domains, the best basis of wavelet packets is selected from a wavelet packet library according to the wavelet packet entropy.Afterwards the statistical features are used to represent the best basis wavelet coefficients.Moreover,the eigenvector is obtained by calculating the asymmetry ratio of the hemispheric brainwave at each electrode in different mental tasks.Finally, the performance of the eigenvector is evaluated via a support vector machines classifier.A publicly available EEG database was used to validate this study.Compared to the conventional WTO, wavelet packet best basis decomposition and existing autoregressive feature extraction methods, the average accuracy for the proposed method ranged from 95.41% to 99.65% for ten different combinations of five mental tasks.关键词
非平稳脑电信号/特征抽取/小波包分解/脑机接口Key words
nonstationary EEG signal/ feature extraction/ wavelet packet decomposition/brain-computer interface分类
医药卫生引用本文复制引用
王登,苗夺谦,王睿智..一种新的基于小波包分解的EEG特征抽取与识别方法研究[J].电子学报,2013,41(1):193-198,6.基金项目
国家自然科学基金(No.60970061,No.61075056,No.61103067) （No.60970061,No.61075056,No.61103067）
