一种新的基于小波包分解的EEG特征抽取与识别方法研究

为了提高脑思维任务分类精度,提出一种新的脑电特征抽取与识别方法.首先进行小波包分解,然后结合能反映脑电信号在时域与频域上的能量分布特征的小波包熵概念,从小波包库中选择最优小波包基,对各个最优基所对应的小波系数求取统计特性,然后根据不同脑思维任务下左右半脑各导联间的差异性对各个导联对求取不对称率构成分类特征向量,最后利用SVM分类器对其进行分类.实验结果表明:相对于一般的小波包分解,最优小波包基和自回归特征抽取方法,该方法对5类不同脑思维任务的所有10种不同组合任务对的平均分类预测精度可以达到95.41％～99.65％.

A New Method of EEG Classification with Feature Extraction Based on Wavelet Packet Decomposition

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 WPD,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.