ArticleFrontiers in neuroscience2022
Implementation of a Convolutional Neural Network for Eye Blink Artifacts Removal From the Electroencephalography Signal.
Article in Frontiers in neuroscience, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Clinical utility and prospective of TMS-EEG: Updated review from an international expert group.Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology · 2026Review
- Automatic EEG artifact detection using a local-global feature fusion network in time and time-frequency domains.Acta epileptologica · 2026Article
- AQMFB-DWT: A Preprocessing Technique for Removing Blink Artifacts Before Extracting Pain-evoked Potential EEG.Neuroscience bulletin · 2025Article
- Robin's Viewer: Using deep-learning predictions to assist EEG annotation.Frontiers in neuroinformatics · 2022Article
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3 authors.
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Abstract
Electroencephalography (EEG) signals are disrupted by technical and physiological artifacts. One of the most common artifacts is the natural activity that results from the movement of the eyes and the blinking of the subject. Eye blink artifacts (EB) spread across the entire head surface and make EEG signal analysis difficult. Methods for the elimination of electrooculography (EOG) artifacts, such as independent component analysis (ICA) and regression, are known. The aim of this article was to implement the convolutional neural network (CNN) to eliminate eye blink artifacts. To train the CNN, a method for augmenting EEG signals was proposed. The results obtained from the CNN were compared with the results of the ICA and regression methods for the generated and real EEG signals. The results obtained indicate a much better performance of the CNN in the task of removing eye-blink artifacts, in particular for the electrodes located in the central part of the head.
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