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Artifact removal and brain rhythm decomposition for eeg signal using wavelet approach

机译:小波方法去除脑电信号的伪影和脑节律分解

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摘要

This recent study introduces and discusses briefly the use of wavelet approach in removing the artifacts and extraction of features for electroencephalography (EEG) signal. Many of new approaches have been discovered by the researcher for processing the EEG signal. Generally, the EEG signal processing can be divided into pre-processing and postprocessing. The aim of processing is to remove the unwanted signal and to extract important features from the signal. However, the selections of non-suitable approach affect the actual result and wasting the time and energy. Wavelet is among the effective approach that can be used for processing the biomedical signal. The wavelet approach can be performed in MATLAB toolbox or by coding, that require a simple and basic command. In this paper, the application of wavelet approach for EEG signal processing is introduced. Moreover, this paper also discusses the effect of using db3 mother wavelet with 5th decomposition level of stationary wavelet transform and db4 mother wavelet with 7th decomposition level of discrete wavelet transform in removing the noise and decomposing of the brain rhythm. Besides, the simulation result are also provided for better configuration.
机译:这项最新的研究介绍并简要讨论了小波方法在消除伪影和提取脑电图(EEG)信号特征中的用途。研究人员发现了许多用于处理EEG信号的新方法。通常,EEG信号处理可分为预处理和后处理。处理的目的是去除不想要的信号并从信号中提取重要特征。但是,选择不合适的方法会影响实际结果,浪费时间和精力。小波是可用于处理生物医学信号的有效方法之一。小波方法可以在MATLAB工具箱中执行,也可以通过编码来执行,这需要简单且基本的命令。本文介绍了小波方法在脑电信号处理中的应用。此外,本文还讨论了使用具有固定小波变换的第五分解级的db3母小波和具有离散小波变换的第七分解级的db4母小波在去除噪声和分解脑节律方面的效果。此外,还提供了仿真结果以实现更好的配置。

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