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Vol. 170, Issue 5, May 2014, pp. 234-240




Phase Information for Classification Between Clench Speed and Clench Force Motor Imagery

1, 2 Baolei XU, 3 Yunfa FU, 1 Gang SHI, 1, 2 Xuxian YIN, 1, 4 Zhidong WANG and 1, 5 Hongyi LI

1 State Key Laboratory of Robotics, Shenyang Institute of Automation (SIA), Chinese Academy of Sciences (CAS), Shenyang 110016, P. R. China
2 University of Chinese Academy of Sciences, Beijing 100049, P. R. China
3 School of Automation and Information Engineering, Kunming University of Science and Technology, Kunming 650500, P. R. China
4 Dept. of Advanced Robotics, Chiba Institute of Technology, Chiba 2750016, Japan
5 School of Mechanical Engineering & Automation, Northeastern University, Shenyang, China
1 Tel.: +86-24-23970721, fax: +86-24-23970021

E-mail: blxu@sia.cn


Received: 28 February 2014 /Accepted: 30 April 2014 /Published: 31 May 2014

Digital Sensors and Sensor Sysstems


Abstract: In this paper, we investigate the phase information for classification between clench speed and clench force motor imagery for BCI applications. The multivariate extensions of empirical mode decomposition (MEMD) are used to decompose EEG data into intrinsic mode functions (IMFs). Then, the phase information is got by transforming IMFs into analytic signal using Hiblert transforms. Six feature types are compared in the paper for channel C3, Cz and C4: the amplitude of IMFs, the power of IMFs, the amplitude of the corresponding analytic signal, the instantaneous phase of the analytic signal, the instantaneous frequency of the analytic signal and the phase-locking value (PLV) between two channels. The support vector machine with 5- fold cross-validation is used to classify clench speed motor imagery from clench force motor imagery. The results show that for some subjects the instantaneous phase can get the best results, while PLV never performs best compared with other features. The minimum classification error rate of 0.25 is reached in our research.


Keywords: Phase, Brain-computer interface (BCI), MEMD, Hilbert transform, motor parameters imagery.


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