Performance Comparison of Denoising Methods of Electroencephalogram

-Electroencephalograph (EEG) is the recording of electrical activity of neurons of the brain along the scalp. EEG signal is contaminated by various kinds of artifacts when it travels from different layer of brain. It is necessary to eliminate all such disturbances in EEG signal for proper diagnosis. In this study, mainly two types of artifacts such as 50Hz and Electromygram (EMG) artifact have been removed using three different algorithms. These algorithms (Finite impulse response (FIR) filter, Infinite impulse response (IIR) filter and discrete wavelet transform) have been applied to remove the above mentioned artifacts from the EEG signal. The filtered out signals from the IIR low pass filter, FIR low filter and Wavelet (sym29) are compared with their Signal to Noise Ratio (SNR) values. About 28 epochs of the signal were studied and the performance analysis was done. The result showed that the compatibility of mother wavelet symlets (sym29) for denoising is better than the rest two.