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Adaptive asynchronous control system of robotic arm based on augmented reality-assisted brain-computer interface.

Lingling Chen1,2, Pengfei Chen1,2,3, Shaokai Zhao4,3

  • 1School of Artificial Intelligence and Data Science, Hebei University of Technology, Tianjin 300130, People's Republic of China.

Journal of Neural Engineering
|October 15, 2021
PubMed
Summary

This study introduces an augmented reality brain-computer interface for asynchronous robotic arm control using steady-state visual evoked potentials (SSVEP). The novel system achieved high accuracy, improving human-robot interaction and reducing user fatigue.

Keywords:
asynchronous controlaugmented realitybrain–computer interfacedynamic windowrobotic arm

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Area of Science:

  • Robotics and Neuroscience
  • Human-Computer Interaction
  • Augmented Reality

Background:

  • Brain-controlled robotic arms offer significant potential but face limitations in flexibility and user attention.
  • Existing systems often require frequent attention switching, leading to user fatigue and reduced efficiency.

Purpose of the Study:

  • To develop an asynchronous robotic arm control system using steady-state visual evoked potentials (SSVEP) within an augmented reality (AR) environment.
  • To enhance user experience by eliminating the need for attention switching between visual stimuli and the robotic arm.
  • To improve the accuracy and efficiency of brain-computer interfaces (BCIs) for robotic control.

Main Methods:

  • Implemented an AR environment allowing simultaneous viewing of the robotic arm and visual stimulation.
  • Developed a multi-template algorithm combining canonical correlation analysis and task-related component analysis for target identification.
  • Utilized a dynamic window optimization strategy for adaptive adjustment of visual stimulation duration.

Main Results:

  • Achieved an average offline accuracy of 94.97% for controlling the robotic arm asynchronously.
  • Reported an average information transfer rate of 67.37 ± 14.27 bits·min⁻¹.
  • Online experiments with ten subjects demonstrated an average single command selection time of 2.04 seconds, with subjects efficiently completing tasks and experiencing reduced visual fatigue.

Conclusions:

  • The proposed AR-based SSVEP-BCI system effectively enables asynchronous robotic arm control, demonstrating feasibility and potential.
  • This human-computer interaction strategy offers a promising new direction for BCI-controlled robots.
  • The system's ability to reduce user fatigue and improve task completion highlights its practical applicability.