Removal of EOG artifacts and separation of different cerebral activity components from single channel EEG-An efficient approach combining SSA-ICA with wavelet thresholding for BCI applications

The electroencephalogram (EEG) signals are usually interfered by many sources of noise like electrooculogram (EOG), which degrades the signals of interest. It causes the poor performance of the Brain-Computer Interface (BCI) systems. In this work, the problem of removal of EOG artifacts and separation of different cerebral activities found in a single-channel contaminated EEG is addressed. For this purpose, a novel model, based on the combined use of Singular Spectrum Analysis (SSA) and Independent Component Analysis (ICA) with a Stationary Wavelet Transform (SWT) is presented. ICA technique is a highly efficient method, which deals with the multichannel EEG signals. But it is difficult to apply the ICA on single channel EEG. Hence, using SSA, the single channel contaminated EEG signals are converted into multivariate information. Then, the multivariate information is fed to ICA, which separates the source signals as different independent components (ICs). Despite the fact that the ICA method performs excellent source separation, still, some required EEG signal content is present in the IC representing itself as an artifact, and thus dropping it would cause loss of EEG signal content. To avoid this problem, SWT is applied on the artifact IC, which performs the thresholding, to separate the actual artifact and preserve the EEG signal content. Matlab simulations have been done on both synthetically generated and real-life EEG signals and the proposed model is compared with the existing works. It is demonstrated that the proposed model has the best artifact separation performance than all the existing techniques, which is shown in terms of the metrics, RRMSE (Relative Root Mean Square Error) and MAE (Mean Absolute Error).

Automated summary

In this study, a novel model combining SSA and ICA is proposed to address the problem of EOG artifacts removal and separation of different cerebral activities in single-channel contaminated EEG signals. The model outperforms existing techniques in terms of artifact separation performance, as demonstrated through MATLAB simulations on synthetically generated and real-life EEG signals.