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The Scientific World Journal
Volume 2014, Article ID 420561, 10 pages
Research Article

Enhanced Performance by Time-Frequency-Phase Feature for EEG-Based BCI Systems

1State Key Laboratory of Robotics, Shenyang Institute of Automation (SIA), Chinese Academy of Sciences (CAS), Shenyang 110016, China
2University of Chinese Academy of Sciences, Beijing 100049, China
3School of Automation and Information Engineering, Kunming University of Science and Technology, Kunming 650500, China
4Department of Advanced Robotics, Chiba Institute of Technology, Chiba 2750016, Japan
5School of Mechanical Engineering & Automation, Northeastern University, Shenyang 110004, China
6Key Laboratory of Motor and Brain Imaging, Capital Institute of Physical Education, Beijing 100088, China

Received 29 March 2014; Revised 18 May 2014; Accepted 2 June 2014; Published 17 June 2014

Academic Editor: Hak-Keung Lam

Copyright © 2014 Baolei Xu et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


We introduce a new motor parameter imagery paradigm using clench speed and clench force motor imagery. The time-frequency-phase features are extracted from mu rhythm and beta rhythms, and the features are optimized using three process methods: no-scaled feature using “MIFS” feature selection criterion, scaled feature using “MIFS” feature selection criterion, and scaled feature using “mRMR” feature selection criterion. Support vector machines (SVMs) and extreme learning machines (ELMs) are compared for classification between clench speed and clench force motor imagery using the optimized feature. Our results show that no significant difference in the classification rate between SVMs and ELMs is found. The scaled feature combinations can get higher classification accuracy than the no-scaled feature combinations at significant level of 0.01, and the “mRMR” feature selection criterion can get higher classification rate than the “MIFS” feature selection criterion at significant level of 0.01. The time-frequency-phase feature can improve the classification rate by about 20% more than the time-frequency feature, and the best classification rate between clench speed motor imagery and clench force motor imagery is 92%. In conclusion, the motor parameter imagery paradigm has the potential to increase the direct control commands for BCI control and the time-frequency-phase feature has the ability to improve BCI classification accuracy.