Journal of Northeastern University Natural Science ›› 2016, Vol. 37 ›› Issue (8): 1100-1103.DOI: 10.12068/j.issn.1005-3026.2016.08.008

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Blind Separation of EEG Based on Blind Deconvolution

HUANG Lu1,2, WANG Hong3   

  1. 1. Sino-Dutch Biomedical and Information Engineering School, Northeastern University, Shenyang 110167, China; 2. College of Information Engineering, Dalian Ocean University, Dalian 116023, China; 3. School of Mechanical Engineering & Automation, Northeastern University, Shenyang 110819, China.
  • Received:2015-05-18 Revised:2015-05-18 Online:2016-08-15 Published:2016-08-12
  • Contact: WANG Hong
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Abstract: The convolution mixture model was adopted to describe the as-observed EEG signals, and then a method for the blind separation of EEG based on blind deconvolution was put forward. The cost function was established based on the independence of EEG sources, and iteration was carried out using conjugate gradient method. The verification was implemented with simulation experiment, adopting the correlation coefficients between separated signals and source signals as the verification indexes. Experimental results show that the method proposed can achieve blind separation of EEG successfully, providing a theoretical and practical reference for the processing of EEG and other physiological signals.

Key words: physiological signal, electroencephalogram, signal processing, blind separation , blind deconvolution

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