De-noising of EEG signals with shift-based cycle spinning on wave atoms

Abstract

EEG signals offer qualitative insights during brain disorder analysis and help in the brief assessment of brain diseases. However, EEG signal acquisition is susceptible to noise of various kinds and renders the analysis phase difficult and ill-posed. Hence, an appropriate technique is necessary to reduce the impairment effect caused by the noise on analysis. Wavelet 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 artifacts around the sharp edges. The proposed shift-based cycle spinning technique 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. De-noising studies on the OpenNeuro EEG dataset indicate the usefulness of the suggested technique. The results are analysed based on the performance measurements SNR and MSE that establish an advantage of shift-based cycle spinning model in getting better results.