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

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.