Electroencephalogram Signals Denoising using Various Mother Wavelet Functions: A Comparative Analysis

In this paper, various mother wavelet functions are proposed for ElectroEncephaloGram (EEG) signal denoising problem. EEG is a graphical measuring of the brain electrical activity which is recording from the scalp. It represents the voltage fluctuations resulting from ionic current flows within the neurons of the brain. During recording time, there are several artifacts noises can corrupt the original EEG signal such as eye blink, eye movements, muscles activity, and interference of power line. Therefore, the EEG signals should be processed to remove these noises obtaining the efficient EEG features. Several techniques have been proposed for EEG noises reduction in which one of these techniques is an EEG signal denoising using wavelet transform (WT). Selection efficient mother wavelet function (Φ) is considered as a critical parameter in wavelet denoising for non-stationary signal because it will affect the denoised signal. In this paper, four mother wavelet functions (i.e, db4, sym7, bior3.9, and coif3) are tested using standard EEG dataset which is established by Kiern and Aunon. The selected mother wavelet functions evaluation using five criteria which are: Signal-to-Noise-Ration (SNR), SNR improvement, Mean Square Error (MSE), Root Mean Square Error (RMSE), and percentage root mean square difference (PRD. Finally, the coif3 achieves the efficient EEG signal denoising for Power Line Noise (PLN) and Electromyogram (EMG) noise. In addition, sym7 obtained the best result with White Gaussian Noise (WGN).

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