An electroencephalogram (EEG) is an electrical signal in microvolts captured noninvasively from the brain, which provides important and unique information about the brain. The frequency of an EEG signal lies between 0 and 100 Hz. Decomposition of an EEG signal into various bands such as alpha, beta, delta, theta, and gamma is essential in seizure-related studies. EEGs play a key role in the diagnosis of epileptic seizures and neurological disorders. In this paper, multiple wavelet families for decomposition and reconstruction are explored and are compared based on risk functions and reconstruction measures. While dealing with the wavelets it is a difficult task to choose the correct/accurate wavelet for the given biosignal analysis. Various statistical properties were studied by the authors to check the suitability of various wavelets for normal and diseased EEG signal decomposition and reconstruction. The methodology was applied to 3 groups (63 subjects) consisting of both sexes and aged between 1 and 80 years: 1) normal healthy subjects, 2) patients with focal seizures, and 3) patients with generalized seizures. Our result shows that the Haar and Bior3.7 wavelets are more suitable for normal as well as diseased EEG signals, as the mean square error, mean approximate error, and percent root mean square difference of these wavelets are much smaller than in other wavelets. The signal-to-error ratio for Haar and Bior3.7 was much higher than in any other wavelet studied.

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.[1] 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.[6] 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. [5]

Activity refers two activities and in this work to classification between two activities is achieved with the help of statistical feature extraction technique.[5] The term silent activity refers to the two processes in which we are proposing a method to predict Cognitive and Non cognitive tasks performed by the human brain. Electroencephalogram (EEG) is the electrical signal of brain which contains valuable information. In this work EEG and its frequency sub-bands have been analyzed to detect silent activity signal. The electroencephogram (EEG) contains information about brain hence the sub band decomposition of EEG is used for analyzing many brain diseases.[1] The subband 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, and delta.[2] A wavelet transform has been applied to decompose the EEG into its sub bands. Statistical features Standard deviation, Covariance is calculated for each sub-band. The effective classification of EEG used for brain computer interface and can be used for silent communication or for recognizing different mental tasks. [5]