title: A preliminary study of muscular artifact cancellation in single-channel EEG.
abstract: Electroencephalogram (EEG) recordings are often contaminated with muscular artifacts that strongly obscure the EEG signals and complicates their analysis. For the conventional case, where the EEG recordings are obtained simultaneously over many EEG channels, there exists a considerable range of methods for removing muscular artifacts. In recent years, there has been an increasing trend to use EEG information in ambulatory healthcare and related physiological signal monitoring systems. For practical reasons, a single EEG channel system must be used in these situations. Unfortunately, there exist few studies for muscular artifact cancellation in single-channel EEG recordings. To address this issue, in this preliminary study, we propose a simple, yet effective, method to achieve the muscular artifact cancellation for the single-channel EEG case. This method is a combination of the ensemble empirical mode decomposition (EEMD) and the joint blind source separation (JBSS) techniques. We also conduct a study that compares and investigates all possible single-channel solutions and demonstrate the performance of these methods using numerical simulations and real-life applications. The proposed method is shown to significantly outperform all other methods. It can successfully remove muscular artifacts without altering the underlying EEG activity. It is thus a promising tool for use in ambulatory healthcare systems.
authors.affiliation: Department of Biomedical Engineering, School of Medical Engineering, Hefei University of Technology, Hefei 230009, China. xunchen@ece.ubc.ca.
authors.forename: Xun
authors.initials: X
authors.lastname: Chen
authors.affiliation: Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC V6T 1Z4, Canada. aipingl@ece.ubc.ca.
authors.forename: Aiping
authors.initials: A
authors.lastname: Liu
authors.affiliation: Department of Biomedical Engineering, School of Medical Engineering, Hefei University of Technology, Hefei 230009, China. hpeng@hfut.edu.cn.
authors.forename: Hu
authors.initials: H
authors.lastname: Peng
authors.affiliation: Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC V6T 1Z4, Canada. rababw@ece.ubc.ca.
authors.forename: Rabab K
authors.initials: RK
authors.lastname: Ward
mesh_terms: D016477:Artifacts; D004569:Electroencephalography / Q000379:methods*; D006801:Humans; D009132:Muscles / Q000502:physiology; D012815:Signal Processing, Computer-Assisted