Removal of muscle artifacts from single-channel EEG based on ensemble empirical mode decomposition and multiset canonical correlation analysis

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. © 2014 xun chen et al.