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Research on shared control of robots based on hybrid brain-computer interface.

Ziqi Zhang1, Mengfan Li1, Ran Wei1

  • 1the State Key Laboratory of Reliability and Intelligence of Electrical Equipment, the School of Health Sciences and Biomedical Engineering, Hebei University of Technology, Tianjin 300132, China.

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Summary

This study introduces a hybrid brain-computer interface (BCI) using electroencephalogram (EEG) and electromyography (EMG) for robot navigation. The novel method enhances safety and accuracy by adapting to users' mental states, significantly reducing collisions and improving obstacle traversal success rates.

Keywords:
Human-robot interactionHybrid brain-computer interfaceMental stateShared control

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Area of Science:

  • Human-Robot Interaction
  • Neuroscience
  • Robotics

Background:

  • Emerging artificial intelligence drives advancements in human-robot interaction technologies.
  • Brain-computer interfaces (BCI) enable human-robot communication and control.
  • Inconsistent mental states degrade BCI accuracy and robot control precision.

Purpose of the Study:

  • To propose a hybrid BCI-based shared control (HB-SC) method for robust brain-controlled robot navigation.
  • To enhance BCI command accuracy and robot control safety by accounting for dynamic user mental states.

Main Methods:

  • A hybrid BCI approach fusing electroencephalogram (EEG) and electromyography (EMG) for mental state monitoring and control.
  • Shared control integrating human-perceived obstacle data with robot-perceived environmental data.
  • Mental state assessment to screen valid BCI commands before outputting to a layered costmap.

Main Results:

  • The HB-SC method demonstrated a 37.50% reduction in collisions during navigation experiments.
  • Success rate for traversing obstacles improved by 25.00% with the HB-SC system.
  • Navigation trajectories generated by the HB-SC system were more consistent with expected paths.

Conclusions:

  • The HB-SC method dynamically adjusts BCI command output based on real-time brain states.
  • This adaptive control strategy significantly reduces errors associated with unstable user mental states.
  • The proposed system enhances overall safety and reliability in brain-controlled robotic navigation.