A wavelet based de-noising technique for ocular artifact correction of the electroencephalogram

This paper investigates a wavelet based denoising of the electroencephalogram (EEG) signal to correct for the presence of the ocular artifact (OA). The. proposed technique is based on an over-complete wavelet expansion of the EEG as follows: i) a stationary wavelet transform (SWT) is applied to the corrupted EEG; ii) the thresholding of the coefficients in the lower frequency bands is performed; iii) the de-noised signal is reconstructed. This paper demonstrates the potential of the proposed technique for successful OA correction. The advantage over conventional methods is that there is no need for the recording of the electrooculogram (EOG) signal itself. The approach works both for eye blinks and eye movements. Hence, there is no need to discriminate between different artifacts. To allow for a proper comparison, the contaminated EEG signals as well as the corrected signals are presented together with their corresponding power spectra.