Chinese Journal of Tissue Engineering Research ›› 2011, Vol. 15 ›› Issue (39): 7360-7363.doi: 10.3969/j.issn.1673-8225.2011.39.035

Previous Articles     Next Articles

A system of motor imaging brain-computer interface based on LabVIEW

Zhou Ya1, He Qing-hua2, Jiao Xiao-bo3   

  1. 1College of Electricity and Information Engineering, Xuchang University, Xuchang  461000, Henan Province, China
    2State Key Laboratory of Trauma, Burns and Combined Injury, Daping Hospital, Surgery Institute of the Third Military Medical University, Chongqing  400042, China
    3Power Telecommunication Center, Xuchang Electric Power Company, Xuchang  461000, Henan Province, China
  • Received:2011-05-30 Revised:2011-07-26 Online:2011-09-24 Published:2011-09-24
  • Contact: He Qing-hua, Doctor, Associate researcher, State Key Laboratory of Trauma, Burns and Combined Injury, Daping Hospital, Surgery Institute of the Third Military Medical University, Chongqing 400042, China
  • About author:Zhou Ya★, Master, Assistant teacher, College of Electricity and Information Engineering, Xuchang University, Xuchang 461000, Henan Province, China zhouyatop@126.com
  • Supported by:

    the National Natural Science Foundation of China, No. 30300418*; the Science and Technology Tackle Key Program of Chongqing City, No. 2009AC5023*; the Science and Technology Foundation of Xuchang University, No. 2011B031*

PDF

569

Abstract:

BACKGROUND: The brain-computer interface (BCI) based on event-related potential can be widely used in the rehabilitation of disabled patients, showing its importance and the feasibility of future implementation.
OBJECTIVE: To present the realization of BCI based on motor imaging under LabVIEW.
METHODS: The critical parts of the solution are the realization of the visual stimulation and the feature extraction of electroencephalogram (EEG). The testee fixates on playback images of the left and right hand on the screen, which can induce EEG. The band pass filtering is used to improve the signal-to-noise ratio (SNR). The EEG data intercepted with the sliding window have been analyzed from the perspective of time-domain energy and obtained the feature extraction of motor imaging EEG. On-line feature extraction lays the foundation for the realization of real-time system.
RESULTS AND CONCLUSION: The program can effectively extract the features of motor imagery, and effectively conduct a classification by off-line pattern recognition, with classification results achieving 82%.

CLC Number: