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The control of a virtual automatic car based on multiple patterns of motor imagery BCI

  • Hongtao Wang
  • Ting Li
  • Anastasios Bezerianos
  • Hui Huang
  • Yuebang He
  • Peng Chen
Original article
  • 53 Downloads

Abstract

Multiple degrees of freedom (DOF) commands are required for a brain-actuated virtual automatic car, which makes the brain-computer interface (BCI) control strategy a big challenge. In order to solve the challenging issue, a mixed model of BCI combining P300 potentials and motor imagery had been realized in our previous study. However, compared with single model BCI, more training procedures are needed for the mixed model and more mental workload for users to bear. In the present study, we propose a multiple patterns of motor imagery (MPMI) BCI method, which is based on the traditional two patterns of motor imagery. Our motor imagery BCI approach had been extended to multiple patterns: right-hand motor imagery, left-hand motor imagery, foot motor imagery, and both hands motor imagery resulting in turning right, turning left, acceleration, and deceleration for a virtual automatic car control. Ten healthy subjects participated in online experiments, the experimental results not only show the efficiency of our proposed MPMI-BCI strategy but also indicate that those users can control the virtual automatic car spontaneously and efficiently without any other visual attention. Furthermore, the metric of path length optimality ratio (1.23) is very encouraging and the time optimality ratio (1.28) is especially remarkable.

Graphical Abstract

The paradigm of multiple patterns of motor imagery detection and the relevant topographies of CSP weights for different MI patterns.

Keywords

Brain-computer interface Multiple degrees of freedom control Multiple patterns of motor imagery Virtual automatic car 

Notes

Funding information

This study was supported by the Technology Development Project of Guangdong Province (No. 2017A010101034), Innovation Projects for Science supported by Department of Education of Guangdong Province (No. 2016KTSCX141), Science Foundation for Young Teachers of Wuyi University (No. 2018td01).

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Copyright information

© International Federation for Medical and Biological Engineering 2018

Authors and Affiliations

  1. 1.School of Information EngineeringWuYi UniversityJiangmenChina
  2. 2.Singapore Institute for Neurotechnology (SINAPSE), Center for Life ScienceNational University of SingaporeSingaporeSingapore

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