title: Automatic artifact removal from EEG - a mixed approach based on double blind source separation and support vector machine.
abstract: Electroencephalography (EEG) recordings are often obscured by physiological artifacts that can render huge amounts of data useless and thus constitute a key challenge in current brain-computer interface research. This paper presents a new algorithm that automatically and reliably removes artifacts from EEG based on blind source separation and support vector machine. Performance on a motor imagery task is compared for artifact-contaminated and preprocessed signals to verify the accuracy of the proposed approach. The results showed improved results over all datasets. Furthermore, the online applicability of the algorithm is investigated.
authors.affiliation: Center for Brain-Like Computing and Machine Intelligence, Department of Computer Science and Engineering, Shanghai Jiao Tong University, 800 Dong Chuan Road, 200240, China.
authors.forename: Georg
authors.initials: G
authors.lastname: Bartels
authors.forename: Li-Chen
authors.initials: LC
authors.lastname: Shi
authors.forename: Bao-Liang
authors.initials: BL
authors.lastname: Lu
mesh_terms: D000328:Adult; D000465:Algorithms; D016477:Artifacts; D001331:Automation / Q000379:methods*; D004569:Electroencephalography / Q000379:methods*; D006801:Humans; D008297:Male; D009068:Movement / Q000502:physiology; D009132:Muscles / Q000502:physiology; D055815:Young Adult