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

Karameldeen Omer; Francesco Ferracuti; Alessandro Freddi; Sabrina Iarlori; Francesco Vella; Andrea Monteriù

doi:10.3390/brainsci15040359

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

Brain Sci. 2025 Mar 30;15(4):359. doi: 10.3390/brainsci15040359.

Authors

Karameldeen Omer^{1

2}, Francesco Ferracuti¹, Alessandro Freddi¹, Sabrina Iarlori¹, Francesco Vella¹, Andrea Monteriù¹

Affiliations

¹ Department of Information Engineering, Università Politecnica delle Marche, 60131 Ancona, Italy.
² Mechanical Department, University of Khartoum, Khartoum 11115, Sudan.

Abstract

Background/objectives: The study explores the integration of human feedback into the control loop of mobile robots for real-time obstacle detection and avoidance using EEG brain-computer interface (BCI) methods. The goal is to assess the possible paradigms applicable to the most current navigation system to enhance safety and interaction between humans and robots.

Methods: The research explores passive and active brain-computer interface (BCI) technologies to enhance a wheelchair-mobile robot's navigation. In the passive approach, error-related potentials (ErrPs), neural signals triggered when users comment or perceive errors, enable automatic correction of the robot navigation mistakes without direct input or command from the user. In contrast, the active approach leverages steady-state visually evoked potentials (SSVEPs), where users focus on flickering stimuli to control the robot's movements directly. This study evaluates both paradigms to determine the most effective method for integrating human feedback into assistive robotic navigation. This study involves experimental setups where participants control a robot through a simulated environment, and their brain signals are recorded and analyzed to measure the system's responsiveness and the user's mental workload.

Results: The results show that a passive BCI requires lower mental effort but suffers from lower engagement, with a classification accuracy of 72.9%, whereas an active BCI demands more cognitive effort but achieves 84.9% accuracy. Despite this, task achievement accuracy is higher in the passive method (e.g., 71% vs. 43% for subject S2) as a single correct ErrP classification enables autonomous obstacle avoidance, whereas SSVEP requires multiple accurate commands.

Conclusions: This research highlights the trade-offs between accuracy, mental load, and engagement in BCI-based robot control. The findings support the development of more intuitive assistive robotics, particularly for disabled and elderly users.

Keywords: EEG-based human–robot interaction; active and passive BCI; assistive robotic technologies; brain–computer interface (BCI); cognitive load in BCI systems; error-related potentials (ErrPs); human feedback in robotics; real-time obstacle detection; steady-state visually evoked potentials (SSVEPs).