Online EMG artifacts removal from EEG based on blind source separation

  • Authors:

  • Junfeng Gao;Yong Yang;Pan Lin;Pei Wang

  • Affiliations:

  • Research Institute of Biomedical Engineering, Xi'an Jiaotong University, Xi'an, China;School of Information Technology, Jiangxi University of Finance and Economics, Nanchang, China and School of Life Science and Technology, University of Electronic Science and Technology of China, ...;Functional Neurolmaging Laboratory Center for Mind/Brain Sciences, University of Trent, Italy;Research Institute of Biomedical Engineering, Xi'an Jiaotong University, Xi'an, Chin

  • Venue:
  • CAR'10 Proceedings of the 2nd international Asia conference on Informatics in control, automation and robotics - Volume 1
  • Year:
  • 2010

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Abstract

Electromyography (EMG) artifacts are the main and serious contaminated sources to the electroencephalogram (EEG) signals. In this paper, a fully automated EMG removal technique based on canonical correlation analysis (CCA) method is presented. CCA method was proved more suitable to reconstruct the EMG-free EEG data than independent component analysis (lCA) methods in the study. Specially, a number of contaminated and clean EEG data were analyzed in order to decide a reasonable correlation threshold, by which this method can remove successfully not only the light EMG artifacts but also heavy EMG artifacts from the EEG data in real-time application with the little distortion of not only the underlying ictal activity signal but the EOG artifacts.