Electroencephalogram (EEG) and its sub‐bands represent electrical pattern of human brain. EEG signal contains transient components, spikes, and different types of artifacts due to eye blinking, movement of the person, anxiety, and so forth, during EEG capture. Wavelet transforms are powerful mathematical tool for sampling approximation to get clean EEG. It also helps in filtering, sampling, interpolation, noise reduction, signal approximation and signal enhancement, and feature extraction. In this paper, we have analyzed artifact cleaning via PSD graphs and statistical features extracted from motor imagery EEG‐like standard deviation variance. For this, we considered 19 channels EEG signal and applied orthogonal Daubechies wavelet, bi‐orthogonal rbio wavelet and Coifman wavelets to check the better performance of different wavelets. Coifman wavelet uses both scaling function and vanishing moments for sampling approximation and hence give smooth sampling compared to rbio and Daubechies wavelet transforms. Coif is a compactly supported wavelet system which also helps in smooth sampling approximations than other wavelets in the state of arts. The detailed coefficients and approximate coefficients can be further used for extracting features from EEG and classification purposes. Artifacts cleaning is thus observed better in coif wavelet analysis compared to other wavelets from the power distributions as power spectral density (PSD) graphs, standard deviation and variance obtained. Matlab R2013b is used for filtering and sampling EEG. Python 2.7 is used for statistical features extraction.

中文翻译：

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利用小波族提取统计特征提取运动图像脑电信号的伪像

脑电图（EEG）及其子带代表人脑的电子模式。脑电信号捕获期间，脑电信号包含瞬态分量，尖峰以及由于眨眼，人的移动，焦虑等导致的不同类型的伪影。小波变换是强大的数学工具，可用于采样近似以获得清晰的脑电图。它还有助于滤波，采样，内插，降噪，信号逼近和信号增强以及特征提取。在本文中，我们通过PSD图和从运动图像EEG样的标准偏差方差中提取的统计特征分析了伪像清洁。为此，我们考虑了19个通道的EEG信号，并应用正交Daubechies小波，双正交rbio小波和Coifman小波来检查不同小波的更好性能。Coifman小波同时使用缩放函数和消失矩进行采样逼近，因此与rbio和Daubechies小波变换相比，平滑采样。Coif是一个紧凑支持的小波系统，与现有技术中的其他小波相比，它还有助于平滑采样近似。详细系数和近似系数可以进一步用于从EEG和分类目的中提取特征。因此，在coif小波分析中，与其他小波相比，从功率分布中观察到的伪像清洁效果更好，如功率谱密度（PSD）图，标准偏差和方差。Matlab R2013b用于对脑电图进行过滤和采样。Python 2.7用于统计特征提取。