Compatibility of mother wavelet functions with the electroencephalographic signal

Electroencephalographic EEG gives an electrical representation biosignals to determine the variation in the activity of the human brain related to distinct emotions. EEG signal acquires many kind of noise when it´s travel though different layer of brain. The wavelet transform WT are used to remove a various kind of artifacts such as inherent noise, motion artefact, and ocular artifact. With the suitable choice of wavelet level and smoothing method, it is possible to remove the artifacts noise with a view to verify and analyze the EEG signal. Mother wavelet is particularly effective for describing a various sides of nonstationary signals such as the discontinuities and repeated patterns of the recorded EEG signal. In this research, one-hundred and thirteen potential mother wavelet functions (Daubechies, Coiflets, Biorthogonal, Reverse Biorthogonal, Discrete Meyer and Symlets) are selected and investigate to find the most similar function with EEG signals. In this paper, the mother wavelet that most compatible with EEG signal has been founded by determines the minimum mean square error (MSE) and the larger signal-to-noise ratio (SNR). Both values showed that the compatibility of the mother wavelet Symlets (sym24) for denoising is the best by examining 57 different signals.