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Updated: May 14, 2025

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Real-Time Mobile Robot Obstacles Detection and Avoidance Through EEG Signals.

Karameldeen Omer1,2, Francesco Ferracuti1, Alessandro Freddi1

  • 1Department of Information Engineering, Università Politecnica delle Marche, 60131 Ancona, Italy.

Brain Sciences
|May 1, 2025
PubMed
Summary
This summary is machine-generated.

Integrating human feedback via brain-computer interfaces (BCI) into mobile robots enhances navigation. Passive BCI offers lower mental load for obstacle avoidance, while active BCI provides higher accuracy.

Keywords:
EEG-based human–robot interactionactive and passive BCIassistive robotic technologiesbrain–computer interface (BCI)cognitive load in BCI systemserror-related potentials (ErrPs)human feedback in roboticsreal-time obstacle detectionsteady-state visually evoked potentials (SSVEPs)

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Area of Science:

  • Robotics
  • Neuroscience
  • Human-Computer Interaction

Background:

  • Mobile robots require advanced navigation systems for safe human interaction.
  • Integrating human feedback into robot control loops is crucial for enhancing safety and usability.

Purpose of the Study:

  • To explore the integration of human feedback into mobile robot control using EEG brain-computer interface (BCI) methods.
  • To assess BCI paradigms for real-time obstacle detection and avoidance in current navigation systems.
  • To enhance human-robot interaction and safety in assistive robotics.

Main Methods:

  • Investigated passive and active BCI technologies for wheelchair-mobile robot navigation.
  • Passive BCI utilized error-related potentials (ErrPs) for automatic error correction.
  • Active BCI used steady-state visually evoked potentials (SSVEPs) for direct user control.
  • Experimental setup involved simulated environments, participant control, and brain signal analysis.

Main Results:

  • Passive BCI: 72.9% classification accuracy, lower mental effort, lower engagement, higher task achievement (e.g., 71% vs. 43%).
  • Active BCI: 84.9% classification accuracy, higher cognitive effort, higher engagement.
  • Passive BCI's single ErrP classification enables autonomous obstacle avoidance, surpassing active BCI's need for multiple commands.

Conclusions:

  • BCI-based robot control involves trade-offs between accuracy, mental load, and engagement.
  • Findings support the development of intuitive assistive robotics for elderly and disabled users.
  • Passive BCI demonstrates potential for effective autonomous navigation with reduced user cognitive burden.