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Volume 4 Issue 3
Jul.  2017

IEEE/CAA Journal of Automatica Sinica

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Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2017  >  4(3): 551-557
Chuanwei Liu, Yunfa Fu, Jun Yang, Xin Xiong, Huiwen Sun and Zhengtao Yu, "Discrimination of Motor Imagery Patterns by Electroencephalogram Phase Synchronization Combined With Frequency Band Energy," IEEE/CAA J. Autom. Sinica, vol. 4, no. 3, pp. 551-557, July 2017. doi: 10.1109/JAS.2016.7510121
Citation: Chuanwei Liu, Yunfa Fu, Jun Yang, Xin Xiong, Huiwen Sun and Zhengtao Yu, "Discrimination of Motor Imagery Patterns by Electroencephalogram Phase Synchronization Combined With Frequency Band Energy," IEEE/CAA J. Autom. Sinica, vol. 4, no. 3, pp. 551-557, July 2017. doi: 10.1109/JAS.2016.7510121
shu

Discrimination of Motor Imagery Patterns by Electroencephalogram Phase Synchronization Combined With Frequency Band Energy

doi: 10.1109/JAS.2016.7510121
  • 1.

    Faculty of Information Engineering and Automation, Kunming University Science and Technology, Kunming 650500, China

  • 2.

    Shanghai Micro Electronics Equipment Co., Ltd. (SMEE), Shanghai 200000, China

Funds:

the National Natural Science Foundation of China 81470084

the National Natural Science Foundation of China 61463024

the Research Project for Application Foundation of Yunnan Province 2013FB026

the Cultivation Program of Talents of Yunnan Province KKSY201303048

the Focal Program for Education Department of Yunnan Province 2013Z130

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    Abstract
  • Central nerve signal evoked by thoughts can be directly used to control a robot or prosthetic devices without the involvement of the peripheral nerve and muscles. This is a new strategy of human-computer interaction. A method of electroencephalogram (EEG) phase synchronization combined with band energy was proposed to construct a feature vector for pattern recognition of brain-computer interaction based on EEG induced by motor imagery in this paper. rhythm and beta rhythm were first extracted from EEG by band pass filter and then the frequency band energy was calculated by the sliding time window; the instantaneous phase values were obtained using Hilbert transform and then the phase synchronization feature was calculated by the phase locking value (PLV) and the best time interval for extracting the phase synchronization feature was searched by the distribution of the PLV value in the time domain. Finally, discrimination of motor imagery patterns was performed by the support vector machine (SVM). The results showed that the phase synchronization feature more effective in 4 s-7 s and the correct classification rate was 91.4 %. Compared with the results achieved by a single EEG feature related to motor imagery, the correct classification rate was improved by 3.5 and 4.3 percentage points by combining phase synchronization with band energy. These indicate that the proposed method is effective and it is expected that the study provides a way to improve the performance of the online real-time brain-computer interaction control system based on EEG related to motor imagery.

     

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