Hybrid algorithm for multi artifact removal from single channel EEG

Abstract

Electroencephalogram (EEG) signals recorded from the ambulatory systems are mostly contaminated by various artifacts like, electrooculogram (EOG), motion artifacts (MA) and electrocardiogram (ECG) artifacts. These artifacts limit the accuracy in further analysis of EEG in practise. So far, several existing methods have been proposed with the combination of decomposition techniques and independent component analysis (ICA) to remove single artifacts and only few methods to remove multiple artifacts from the single channel EEG. As improperly denoised EEG signals can result in wrong diagnosis, in this work, Singular Spectrum Analysis (SSA) and ICA are jointly combined with Generalized Moreau Envelope Total Variation (GMETV) technique to simultaneously remove combinations of different artifacts from single channel EEG. In this work, the SSA is used to decompose the contaminated single channel EEG, while the ICA is employed to separate the various hidden sources as independent components (ICs). Although the ICA is adequate in source separation, there is still, some essential EEG signal data appearing as artifact in the IC. Hence, eliminating this would allow EEG signal information to be lost. The GMETV approach is proposed in this paper, to estimate the actual artifacts in order to address these issues. The estimated actual artifacts are subtracted from the artifact ICs providing the residue of wanted component of EEG. This residue is added back to the remaining ICs, to obtain the denoised EEG. Simulation results demonstrated that the proposed technique performs better compared to the existing techniques. The Relative Root Mean Square Error (RRMSE) is reduced by 12.02% and 7.22% compared to SSA-ICA and SSA-ICA-thresholding respectively. Similarly, the Correlation Coefficient (CC) is increased by 21.48% and 8.25% with respect to SSA-ICA and SSA-ICA-thresholding respectively.