公开(公告)号：US20150269336A1

公开(公告)日：2015-09-24

申请号：US14583127

申请日：2014-12-25

Applicant: Beijing University of Technology

Inventor： Lijuan Duan ,  Hui Ge ,  Zhen Yang ,  Yuanhua Qiao ,  Wei Ma ,  Haiyan Zhou

IPC: G06F19/00 ,  G06N99/00 ,  A61B5/0484 ,  A61B5/04 ,  A61B5/00 ,  G06N5/04 ,  A61B5/0478

CPC classification number: A61B5/0484 ,  A61B5/04012 ,  A61B5/0476 ,  A61B5/0478 ,  A61B5/04842 ,  A61B5/7267 ,  G06N20/00

Abstract: The present invention relates to a method for selecting features of EEG signals based on a decision tree: firstly, acquired multi-channel EEG signals are pre-processed, and then the pre-processed EEG signals are performed with feature extraction by utilizing principal component analysis, to obtain a analysis data set matrix with decreased dimensions; superior column vectors are obtained through analyzing from the analysis data set matrix with decreased dimensions by utilizing a decision tree algorithm, and all the superior column vectors are jointed with the number of the columns increased and the number of the rows unchanged, to be reorganized into a final superior feature data matrix; finally, the reorganized superior feature data matrix is input to a support vector machine (SVM) classifier, to perform a classification on the EEG signals, to obtain a classification accuracy. In the present invention, superior features are selected by utilizing a decision tree, to avoid influence of subjective factors during the selection, so that the selection is more objective and with a higher classification accuracy. The average classification accuracy through the present invention may reach 89.1%, increased by 0.9% compared to the conventional superior electrode reorganization.

Abstract translation: 本发明涉及一种基于决策树选择EEG信号特征的方法：首先，对所获取的多通道EEG信号进行预处理，然后通过利用主成分分析进行特征提取预处理的EEG信号 ，以获得尺寸减小的分析数据集矩阵; 通过利用决策树算法从具有减小的维度的分析数据集矩阵分析中获得优越的列向量，并且所有上级列向量与增加的列的数量和行数不变而被重新组合 最后的优势特征数据矩阵; 最后，将重组的优异特征数据矩阵输入到支持向量机（SVM）分类器，对EEG信号进行分类，以获得分类精度。 在本发明中，通过利用决策树来选择优越特征，以避免在选择期间主观因素的影响，使得选择更客观并且具有更高的分类精度。 本发明的平均分类精度可达89.1％，比常规优良电极重组提高0.9％。