Comparison of Sub-band Decomposition and Reconstruction of EEG Signal by Daubechies9 and Symlet9 Wavelet

Abstract

The effective classification of EEG used for brain computer interface and can be used for silent communication or for recognizing different mental tasks. The electroencephogram (EEG) contains information about brain hence the sub-band decomposition of EEG is used for analyzing many brain diseases. The sub-band decomposition means to extract various brain waves with different frequency bands such as alpha, beta, delta, theta and gamma from EEG signal to get more information from it. The work was carried out to extract various brain waves using discrete wavelet transform. The EEG signal is decompose into five sub-bands alpha, beta, gamma, theta, delta using daubechies and symlet wavelet. Based on application, these decomposed brain waves can be given to any network as input for further analysis. The decomposed signal was further reconstructed to obtain the original signal. Original signal was compared with the reconstructed signal and mean square error (MSE) was calculated. The work carried out shows that the MSE for symlet wavelet is less as compared to that of the daubechies wavelet. Symlet wavelet is the best suited wavelet for sub-band decomposition.