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Identification of arm movements using statistical features from EEG signals in wavelet packet domain

机译:利用小波包域中脑电信号的统计特征识别手臂运动

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In this paper, a method to classify arm movements using statistical features of electroencephalogram (EEG) signals calculated from wavelet packet and Fourier transforms, has been proposed. The EEG signals are analyzed using bi-orthogonal wavelet packet family. Fourier transform is then applied to the corresponding detail coefficients and higher order statistical moment named kurtosis is calculated from the magnitude of the Fourier components. The features are shown to be distinguishable for the EEG signals of four different arm movements. K-nearest neighbor (KNN)-based classifiers are developed using these features to identify the arm movements, right hand forward and backward; left hand forward and backward. A mean accuracy of 92.84% is achieved which is shown to be better than some existing techniques.
机译:本文提出了一种利用小波包和傅立叶变换计算的脑电图(EEG)信号统计特征对手臂运动进行分类的方法。使用双正交小波包族分析脑电信号。然后将傅立叶变换应用于相应的细节系数,并从傅立叶分量的大小计算出称为峰度的高阶统计矩。这些特征对于四个不同手臂运动的EEG信号是可区分的。使用这些功能开发基于K近邻(KNN)的分类器,以识别手臂运动(右手向前和向后);左手向前和向后。达到了92.84%的平均精度,这表明它比某些现有技术要好。

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