The physiological artifacts such as electromyogram (EMG) and electrooculogram (EOG) remain a major problem in electroencephalogram (EEG) research. A number of techniques are currently in use to remove these artifacts with the hope that the process does not unduly degrade the quality of the obscured EEG. In this paper, a new method has been proposed by combining two techniques: a canonical correlation analysis (CCA) followed by a stationary wavelet transform (SWT) to remove EMG artifacts and a second-order blind identification (SOBI) technique followed by SWT to remove EOG artifacts. The simulation results show that these combinations are more effective than either using the individual techniques alone or using other combinations of techniques. The quality of the artifact removal is evaluated by calculating correlations between processed and unprocessed data, and the practicability of the technique is judged by comparing execution times of the algorithms. 
Artifacts-matched blind source separation and wavelet transform for multichannel EEG denoising