Novel Approaches for the Removal of Motion Artifact From EEG Recordings

Abstract

The electroencephalogram (EEG) signal is contaminated with various noises or artifacts during recording. For the automated detection of neurological disorders, it is a vital task to filter out these artifacts from the EEG signal. In this paper, we propose two novel approaches for the removal of motion artifact from the single channel EEG signal. These methods are based on the multiresolution total variation (MTV) and multiresolution weighted total variation (MWTV) filtering schemes. The multiresolution analysis using the discrete wavelet transform (DWT) helps to segregate the EEG signal into various subband signals. The total variation (TV) and weighted TV (WTV) are applied to the approximation subband signal. The filtered approximation subband signal is evaluated based on the difference between the noisy approximation subband signal and the output of the TV or WTV filter. The processed EEG signal is obtained using the multiresolution wavelet-based reconstruction. The difference in the signal to noise ratio (<inline-formula> <tex-math notation= LaTeX >$\\Delta $ </tex-math></inline-formula>SNR) and the percentage of reduction in correlation coefficients (<inline-formula> <tex-math notation= LaTeX >$\\eta $ </tex-math></inline-formula>) is used for evaluating the diagnostic quality of the processed EEG signal. The experimental results demonstrate that the proposed MTV and MWTV approaches have better denoising performance with (average <inline-formula> <tex-math notation= LaTeX >$\\Delta $ </tex-math></inline-formula>SNR, and average <inline-formula> <tex-math notation= LaTeX >$\\eta $ </tex-math></inline-formula>) values of (29.12 dB and 68.56%) and (29.29 dB and 67.51%), respectively, as compared to the existing techniques.