Brain-Controlled 2D Navigation Robot Based on a Spatial Gradient Controller and Predictive Environmental Coordinator

Deyu Zhang, Siyu Liu, Jian Zhang, Guoqi Li, Dingjie Suo, Tiantian Liu, Jiawei Luo, Zhiyuan Ming, Jinglong Wu, Tianyi Yan

Research output: Contribution to journalArticlepeer-review


Objective: Brain-computer interfaces (BCIs) have been used in two-dimensional (2D) navigation robotic devices, such as brain-controlled wheelchairs and brain-controlled vehicles. However, contemporary BCI systems are driven by binary selective control. On the one hand, only directional information can be transferred from humans to machines, such as 'turn left' or 'turn right', which means that the quantified value, such as the radius of gyration, cannot be controlled. In this study, we proposed a spatial gradient BCI controller and corresponding environment coordinator, by which the quantified value of brain commands can be transferred in the form of a 2D vector, improving the flexibility, stability and efficiency of BCIs. Methods: A horizontal array of steady-state visual stimulation was arranged to excite subject (EEG) signals. Covariance arrays between subjects' electroencephalogram (EEG) and stimulation features were mapped into quantified 2-dimensional vectors. The generated vectors were then inputted into the predictive controller and fused with virtual forces generated by the robot's predictive environment coordinator in the form of vector calculation. The resultant vector was then interpreted into the driving force for the robot, and real-time speed feedback was generated. Results: The proposed SGC controller generated a faster (27.4 s vs. 34.9 s) response for the single-obstacle avoidance task than the selective control approach. In practical multiobstacle tasks, the proposed robot executed 39% faster in the target-reaching tasks than the selective controller and had better robustness in multiobstacle avoidance tasks (average failures significantly dropped from 27% to 4%). Significance: This research proposes a new form of brain-machine shared control strategy that quantifies brain commands in the form of a 2-D control vector stream rather than selective constant values. Combined with a predictive environment coordinator, the brain-controlled strategy of the robot is optimized and provided with higher flexibility. The proposed controller can be used in brain-controlled 2D navigation devices, such as brain-controlled wheelchairs and vehicles.

Original languageEnglish
Pages (from-to)6138-6149
Number of pages12
JournalIEEE Journal of Biomedical and Health Informatics
Issue number12
Publication statusPublished - Dec 1 2022
Externally publishedYes


  • Brain-computer interfaces
  • human-machine shared control
  • robot control

ASJC Scopus subject areas

  • Computer Science Applications
  • Health Informatics
  • Electrical and Electronic Engineering
  • Health Information Management


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