Ocular Artifact Detection and Removing from EEG by wavelet families: A Comparative Study

The Electroencephalogram (EEG) is a biological signal that represents the electrical activity of the brain. Biological artifacts like ocular artifact (OA) are one of the main interferences in EEG recordings. Eye blinks and movements of the eyeballs produce a signal known as electrooculogram (EOG) that these are 10 to 100 times stronger than the EEG signal. Due to the frequency range of EEG signal and OA which has overlapping with each other, identify and removing of the EOG artifacts are one of the main challenges for researchers, because an incorrect denoising may lose some of the important information of EEG signals. In this context, our aim is to propose a technique based on wavelet transform for accurate identification of the blink artifact zone and removal of EEG signals. We propose using absolute value of signal reconstructed details for blink zone detection and the efficiency of wavelet families to remove the blink artifact which is evaluated by calculating the mean squared error (MSE) between denoised and clean EEG signals and comparing with the results before and after artifact removing show that db7, sym7, coif5, rbio1.5 and dmey at 4th level are preferable and effective in blink artifact zone detection and db7, coif5, dmey, db5 and db9 respectively provide the best result for blink artifact removing with minimum loss important information. Keywords: EEG, EOG, OA, Wavelet transform, MS

Ocular Artifact Detection and Removing from EEG using wavelet families: A Comparative Study

The Electroencephalogram (EEG) is a biological signal that represents the electrical activity of the brain. Biological artifacts like ocular artifact (OA) are one of the main interferences in EEG recordings. Eye blinks and movements of the eyeballs produce a signal known as electrooculogram (EOG) that these are 10 to 100 times stronger than the EEG signal. Due to the frequency range of EEG signal and OA which has overlapping with each other, identify and removing of the EOG artifacts are one of the main challenges for researchers, because an incorrect denoising may lose some of the important information of EEG signals. In this context, our aim is to propose a technique based on wavelet transform for accurate identification of the blink artifact zone and removal of EEG signals. We propose using absolute value of signal reconstructed details for blink zone detection and the efficiency of wavelet families to remove the blink artifact which is evaluated by calculating the mean squared error (MSE) between denoised and clean EEG signals and comparing with the results before and after artifact removing show that db7, sym7, coif5, rbio1.5 and dmey at 4th level are preferable and effective in blink artifact zone detection and db7, coif5, dmey, db5 and db9 respectively provide the best result for blink artifact removing with minimum loss of important information