De Noising Of EEG Signals Using Shift Based and Recursive Cycle Spinning and Wave Atoms Techniques

Abstract

A study of Electroencephalogram (EEG) signals are required to predict any brain disorders in the human beings. EEG signals offer qualitative insights during Brain disorder analysis and help in succinct assessment of brain diseases. Usually EEG signals are contaminated by different types noise during their acquisition. However, EEG signal acquisition is susceptible to noise of various kinds and renders the analysis phase difficult and ill-posed. Therefore removing the noise from EEG signals is essential for better analysis of brain disorders. There are different techniques available to remove the noise from EEG signals, but they unable to remove the noise completely. Actually it is not possible to remove the noise completely from the EEG signals. Hence, an appropriate technique is necessary to reduce the impairment effect caused by the noise on analysis. newlineWavelet Thresholding is one of the most widely used techniques to recover the EEG signals from the noise. However, all the variants of wavelet thresholding algorithms suffer from pseudo-gibbs phenomenon leading to ringing effect. Wave atom is a novel multiscale-multidirectional transformation technique. However this wavelet thresholding produces pseudo-gibbs phenomena, which are visual distortions and oscillations in the area of signal processing. The widely appreciated Wave atom transformation too fails from artefacts around the sharp edges. newlineThe proposed cycle spinning techniques of wave atom transformation model estimates the thresholding parameter, in an unbiased manner, from the data and de-noises the signal in a translation invariant manner. The efficacy of the proposed method is demonstrated by conducting the de-noising on various EEG signals that are available. The results analysed based on the performance measures SNR and MSE that establish that the advantage of Cycle Spinning model in getting better results. Hence in this work, we minimized the noise present in the signal to the maximum extent by using Cycle Spinning Techniques.