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。
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.
引文
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