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A brain-actuated robotic arm system using non-invasive hybrid brain-computer interface and shared control strategy.

Linfeng Cao1, Guangye Li1, Yang Xu1

  • 1State Key Laboratory of Mechanical Systems and Vibrations, Institute of Robotics, Shanghai Jiao Tong University, Shanghai, People's Republic of China.

Journal of Neural Engineering
|April 16, 2021
PubMed
Summary

This study introduces a novel shared control model and hybrid brain-computer interface (BCI) for robotic arm control. The system significantly improves robotic arm task success rates for users with non-invasive BCIs.

Keywords:
Bayesian fusioncomputer visionhybrid brain–computer interfaceintention inferencerobotic armshared control

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Area of Science:

  • Robotics
  • Neuroscience
  • Human-Computer Interaction

Background:

  • Non-invasive electroencephalography (EEG)-based brain-computer interfaces (BCIs) show potential for controlling robotic arms.
  • Current BCI systems often struggle with signal quality and require manual arbitration rules, limiting performance.
  • Shared control strategies offer an alternative but typically rely on task-specific, manually defined rules.

Purpose of the Study:

  • To propose a novel shared control model for dynamic, real-time optimization of robotic arm control commands.
  • To introduce a hybrid BCI scheme for efficient allocation of control resources and multi-dimensional robotic arm manipulation.
  • To enable non-invasive BCI users to effectively control a robotic arm for complex pick-and-place tasks in a 3D space.

Main Methods:

  • Developed a machine agent using computer vision for intention inference and automatic command generation.
  • Implemented a dynamic shared control model that fuses machine autonomy and user intention based on confidence and user characteristics.
  • Utilized a hybrid BCI combining steady-state visual evoked potentials and motor imagery for divided BCI interfaces.

Main Results:

  • Eleven subjects successfully controlled a robotic arm for pick-and-place tasks in a 3D workspace.
  • The shared control strategy achieved an average success rate of approximately 85% for picking tasks.
  • Pure BCI control resulted in an average success rate of approximately 50% for placing tasks.

Conclusions:

  • The proposed novel shared controller optimizes motion automatically.
  • The hybrid BCI scheme effectively allocates paradigms based on command importance for multi-dimensional control.
  • Shared control combined with a hybrid BCI significantly enhances the performance of brain-actuated robotic arm systems.