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Journal of Applied Mathematics
Volume 2014 (2014), Article ID 261347, 10 pages
Research Article

Removal of Muscle Artifacts from Single-Channel EEG Based on Ensemble Empirical Mode Decomposition and Multiset Canonical Correlation Analysis

Xun Chen,1,2 Chen He,2 and Hu Peng1

1Department of Biomedical Engineering, School of Medical Engineering, Hefei University of Technology, Hefei, Anhui 230009, China
2Department of Electrical and Computer Engineering, University of British Columbia, Vancouver, BC, Canada V6T 1Z4

Received 27 March 2014; Revised 25 May 2014; Accepted 25 May 2014; Published 12 June 2014

Academic Editor: Chang-Hwan Im

Copyright © 2014 Xun Chen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Electroencephalogram (EEG) recordings are often contaminated with muscle artifacts. This disturbing muscular activity strongly affects the visual analysis of EEG and impairs the results of EEG signal processing such as brain connectivity analysis. If multichannel EEG recordings are available, then there exist a considerable range of methods which can remove or to some extent suppress the distorting effect of such artifacts. Yet to our knowledge, there is no existing means to remove muscle artifacts from single-channel EEG recordings. Moreover, considering the recently increasing need for biomedical signal processing in ambulatory situations, it is crucially important to develop single-channel techniques. In this work, we propose a simple, yet effective method to achieve the muscle artifact removal from single-channel EEG, by combining ensemble empirical mode decomposition (EEMD) with multiset canonical correlation analysis (MCCA). We demonstrate the performance of the proposed method through numerical simulations and application to real EEG recordings contaminated with muscle artifacts. The proposed method can successfully remove muscle artifacts without altering the recorded underlying EEG activity. It is a promising tool for real-world biomedical signal processing applications.