Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Predicting time across age: comparing performance and neural dynamics of younger and older adults in a temporal prediction task.

Frontiers in aging neuroscience·2026
Same author

Cognitive effects of STN-DBS on mental rotation performance in Parkinson's disease.

Scientific reports·2026
Same author

Low-rank tensor decomposition for cross-bispectral analysis of EEG data.

Journal of neuroscience methods·2026
Same author

Oscillatory multi-timescale mechanisms underlying audiovisual sequence prediction.

Imaging neuroscience (Cambridge, Mass.)·2026
Same author

Prefrontal Speaker-Listener Neural Coupling Supports Speech-in-Noise Comprehension in Normal-Hearing Older Adults: An fNIRS Study.

The European journal of neuroscience·2026
Same author

Modulation of Non-Rhythmic Temporal Prediction by Subthalamic Nucleus Deep Brain Stimulation (STN-DBS) in Parkinson's Disease.

Movement disorders : official journal of the Movement Disorder Society·2026

Related Experiment Video

Updated: Aug 16, 2025

SSVEP-based Experimental Procedure for Brain-Robot Interaction with Humanoid Robots
11:01

SSVEP-based Experimental Procedure for Brain-Robot Interaction with Humanoid Robots

Published on: November 24, 2015

13.2K

Coordinating human-robot collaboration by EEG-based human intention prediction and vigilance control.

Jianzhi Lyu1, Alexander Maýe2, Michael Görner1

  • 1TAMS Group, Department of Informatics, University of Hamburg, Hamburg, Germany.

Frontiers in Neurorobotics
|December 19, 2022
PubMed
Summary

This study introduces a brain-computer interface (BCI) to predict human actions in human-robot collaboration. By detecting attention focus and alertness, the BCI enhances both collaboration efficiency and safety.

Keywords:
brain-computer interfacecollision avoidancehuman-robot collaborationintention predictiontrajectory optimization

More Related Videos

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

4.0K
Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface
11:54

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface

Published on: May 8, 2021

4.6K

Related Experiment Videos

Last Updated: Aug 16, 2025

SSVEP-based Experimental Procedure for Brain-Robot Interaction with Humanoid Robots
11:01

SSVEP-based Experimental Procedure for Brain-Robot Interaction with Humanoid Robots

Published on: November 24, 2015

13.2K
Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

4.0K
Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface
11:54

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface

Published on: May 8, 2021

4.6K

Area of Science:

  • Robotics
  • Neuroscience
  • Human-Computer Interaction

Background:

  • Human-robot collaboration requires balancing performance with safety, often limiting robot speed.
  • Reactive control based on human pose is insufficient for complex tasks and fast human movements.
  • Predicting human intention is crucial for timely robot adaptation but remains challenging.

Purpose of the Study:

  • To develop an adaptive human-robot collaboration system using a novel brain-computer interface (BCI).
  • To leverage early detection of human attention focus as a predictor for impending actions.
  • To adjust robot movement velocity based on human vigilance levels.

Main Methods:

  • Utilized a brain-computer interface (BCI) to detect the focus of human overt attention.
  • Integrated stimuli projection onto the workspace for seamless BCI application.
  • Adjusted robot velocity based on the signal-to-noise ratio of brain responses, reflecting human alertness.

Main Results:

  • The BCI system successfully predicted impending human actions by detecting attention focus.
  • Robot movement velocity was adaptively adjusted based on human vigilance levels.
  • Physical robot experiments demonstrated improvements in both collaboration efficiency and safety margins.

Conclusions:

  • Employing a BCI to predict human intention and alertness significantly enhances human-robot collaboration.
  • This approach offers a promising method for improving safety and efficiency in shared workspaces.
  • Adaptive robot behavior based on brain signals represents a key advancement in collaborative robotics.