[1]陈启超,张学军,黄婉露.EMD融合PSD、CSP的脑电特征提取方法[J].计算机技术与发展,2019,29(05):126-130.[doi:10. 3969 / j. issn. 1673-629X. 2019. 05. 027]
　CHEN Qi-chao,ZHANG Xue-jun,HUANG Wan-lu.An EEG Feature Extraction Method of EMD Fusing PSD and CSP[J].,2019,29(05):126-130.[doi:10. 3969 / j. issn. 1673-629X. 2019. 05. 027]
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EMD融合PSD、CSP的脑电特征提取方法()
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《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
29
期数:
2019年05期
页码:
126-130
栏目:
应用开发研究
出版日期:
2019-05-10

文章信息/Info

Title:
An EEG Feature Extraction Method of EMD Fusing PSD and CSP
文章编号:
1673-629X(2019)05-0126-05
作者:
陈启超1; 2; 张学军1; 黄婉露1
1. 南京邮电大学 电子与光学工程学院 微电子学院,江苏 南京 210023;2. 南京邮电大学 射频集成与微组装技术国家地方联合工程实验室,江苏 南京 210023
Author(s):
CHEN Qi-chao1; 2; ZHANG Xue-jun1; HUANG Wan-lu1
1. School of Electronics and Optical Engineering & School of Microelectronics,Nanjing University of Posts and Telecommunications,Nanjing 210023,China;2. Nation-Local Joint Project Engineering Lab of RF Integration & Micropackage,Nanjing University of Posts and Telecommunications,Nanjing 210023,China
关键词:
脑电信号; 经验模式分解; 相关系数; 功率谱密度; 公共空间模式
Keywords:
brain electrical signals; empirical mode decomposition; correlation coefficient; power spectral density; common spatial pattern
分类号:
R318;TN911. 7
DOI:
10. 3969 / j. issn. 1673-629X. 2019. 05. 027
摘要:
为了提高运动想象分类精确度,提出一种基于经验模式分解(EMD),并结合功率谱密度(PSD)和公共空间模式(CSP)的特征提取算法。 首先将采集的脑电信号进行预处理,再对信号使用 EMD 算法得到多个固有模态函数( IMFs)。通过计算每次实验原始脑电信号与各阶 IMF 分量之间的相关系数,并计算所有实验得出的相关系数的绝对值的平均数,选择具有较大相关系数绝对值平均数的固有模态函数,计算其功率谱密度作为特征,经共空间模式投影映射再提取相应的特征向量,并用支持向量机(SVM)进行分类。 对 9 名受试者的运动想象进行分类结果分析,得到的平均分类正确率在96% 以上。 最后将该方法与其他方法做比较,证明了该算法的可行性。
Abstract:
In order to improve the classification accuracy of motor imagery,we propose a feature extraction algorithm based on empirical mode decomposition (EMD) combined with power spectral density (PSD) and common space pattern (CSP). First,the collected EEG signal is preprocessed,and then EMD algorithm is used to obtain multiple natural modal functions ( IMFs). The correlation coefficient between the original EEG signal and the IMF components are calculated, and the average of the absolute values of the correlation coefficients derived from all experiments is computed. The intrinsic modal function with an average absolute number of large correlation coefficients is selected,and its power spectral density is calculated as a feature. The corresponding feature vector is extracted by the common space pattern projection mapping and classified by a support vector machine (SVM). The classification of the motor imagery of 9 subjects is analyzed,and the average classification accuracy is above 96% . Finally,this method is compared with other methods to prove its the feasibility.

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更新日期/Last Update: 2019-05-10