Joint Singular Spectrum Analysis and Generalized Moreau Envelope Total Variation for motion artifact removal from single channel EEG signals

Abstract

Recorded electroencephalogram (EEG) signals are commonly interfered by several artifacts. It is very crucial to remove the artifacts for automatic detection of brain disorders. In this work, the joint use of Singular Spectrum Analysis (SSA) and Generalized Moreau Envelope Total Variation (GMETV), namely SSA-GMETV technique is proposed to remove the motion artifacts from the single channel EEG signals. In this work, initially, the interfered EEG is decomposed into several bands by SSA. The last subband of the interfered signal is applied to GMETV filter to extract the artifact. This is then subtracted from the last subband signal and added to the remaining SSA decomposed bands, to obtain the denoised EEG signal. Relative Root Mean Square Error (RRMSE) and difference in Signal to Noise Ratio (ΔSNR) are considered to quantify the denoised EEG signals. Experimental results demonstrate that the proposed SSA-GMETV technique performs much better than the existing methods in terms of the performance metrics, average RRMSE and average ΔSNR by 47.41% and 31.81 dB respectively.