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The Scientific World Journal
Volume 2014, Article ID 420561, 10 pages
http://dx.doi.org/10.1155/2014/420561
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.

Citations to this Article [2 citations]

The following is the list of published articles that have cited the current article.

  • Alimed Celecia Ramos, Rene Gonzalez Hernandez, and Marley Vellasco, “Feature Selection methods applied to Motor Imagery task classification,” 2016 IEEE Latin American Conference on Computational Intelligence (LA-CCI), pp. 1–6, . View at Publisher · View at Google Scholar
  • 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 Journal of Automatica Sinica, vol. 4, no. 3, pp. 551–557, 2017. View at Publisher · View at Google Scholar