• 1. School of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, P.R.China;
  • 2. Brain Cognition and Brain-Computer Intelligence Integration Group, Kunming University of Science and Technology, Kunming 650500, P.R.China;
  • 3. College of Information Engineering, Engineering University of PAP, Xi’an 710000, P.R.China;
  • 4. Faculty of Science, Kunming University of Science and Technology, Kunming 650500, P.R.China;
  • 5. School of Health and Management, Guangzhou Medical University, Guangzhou 511436 P.R.China;
  • 6. School of Medicine, Kunming University of Science and Technology, Kunming 650500, P.R.China;
Export PDF Favorites Scan Get Citation

Motor imagery (MI) is an important paradigm of driving brain computer interface (BCI). However, MI is not easy to control or acquire, and the performance of MI-BCI depends heavily on the performance of the subjects’ MI. Therefore, the correct execution of MI mental activities, ability evaluation and improvement methods play important and even critical roles in the improvement and application of MI-BCI system’s performance. However, in the research and development of MI-BCI, the existing researches mainly focus on the decoding algorithm of MI, but do not pay enough attention to the above three aspects of MI mental activities. In this paper, these problems of MI-BCI are discussed in detail, and it is pointed out that the subjects tend to use visual motor imagery as kinesthetic motor imagery. In the future, we need to develop some objective, quantitatively visualized MI ability evaluation methods, and develop some effective and less time-consumption training methods to improve MI ability. It is also necessary to solve the differences and commonness of MI problems between and within individuals and MI-BCI illiteracy to a certain extent.

Citation: TIAN Guixin, CHEN Junjie, DING Peng, GONG Anmin, WANG Fan, LUO Jiangong, DONG Yiyang, ZHAO Lei, DANG Caiping, FU Yunfa. Execution, assessment and improvement methods of motor imagery for brain-computer interface. Journal of Biomedical Engineering, 2021, 38(3): 434-446. doi: 10.7507/1001-5515.202101037 Copy

  • Previous Article

    Research on feature classification of lower limb motion imagination based on electrical stimulation to enhance rehabilitation
  • Next Article

    Using electroencephalogram for emotion recognition based on filter-bank long short-term memory networks