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There are many sources of interference encountered in theelectroencephalogram (EEG) recordings, specifically ocular, muscular, andcardiac artifacts. Rejection of EEG artifacts is an essential process in EEGanalysis since such artifacts cause many problems in EEG signals analysis. Oneof the most challenging issues in EEG denoising processes is removing theocular artifacts where Electrooculographic (EOG), and EEG signals have anoverlap in both frequency and time domains. In this paper, we build and train adeep learning model to deal with this challenge and remove the ocular artifactseffectively. In the proposed scheme, we convert each EEG signal to an image tobe fed to a U-NET model, which is a deep learning model usually used in imagesegmentation tasks. We proposed three different schemes and made our U-NETbased models learn to purify contaminated EEG signals similar to the processused in the image segmentation process. The results confirm that one of ourschemes can achieve a reliable and promising accuracy to reduce the Mean squareerror between the target signal (Pure EEGs) and the predicted signal (PurifiedEEGs).

Deep learning denoising for EOG artifacts removal from EEG signals