ArticleBrain sciences2025
Real-Time Mobile Robot Obstacles Detection and Avoidance Through EEG Signals.
Article in Brain sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- A review of full-stack autonomous obstacle avoidance for assistive robots for the disabled.Frontiers in robotics and AI · 2026Review
- Error-related potentials detection to enhance human-robot collaboration: a mini review.Frontiers in neuroergonomics · 2026Review
- RGB-D Cameras and Brain-Computer Interfaces for Human Activity Recognition: An Overview.Sensors (Basel, Switzerland) · 2025Review
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
BACKGROUND/
objectivesThe 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.
methodsThe 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.
resultsThe 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.
conclusionsThis 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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.