Abstract
Electroencephalography (EEG) data are used to design useful indicators that act as proxies for detecting humans’ mental activities. However, these electrical signals are susceptible to different forms of interferences—known as artifacts—from voluntarily and involuntarily muscle movements that greatly obscure the information in the signal. It is pertinent to design effective artifact removal techniques (ARTs) capable of removing or reducing the impact of these artifacts. However, most ARTs have been focusing on handling a few specific types, or a single type, of EEG artifacts. EEG processing that generalizes to multiple types of artifacts remains a major challenge. In this paper, we investigate a variety of eight different and typical artifacts that occur in practice. We characterize the spatiotemporal-frequency influence of these EEG artifacts and offer two heuristics. The proposed heuristics extend influential independent component analysis to clean the contaminated EEG signal. These proposed heuristics are compared against four state-of-the-art EEG ARTs using both real and synthesized EEG, collected in the presence of multiple artifacts. The results show that both heuristics offer superior spatiotemporal-frequency performance in automatic artifacts removal and are able to reconstruct clean EEG signals.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: IEEE Transactions on Emerging Topics in Computational Intelligence
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
