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

Artificial vibrotactile feedback elicits neural correlates of sense of agency.

Journal of neuroengineering and rehabilitation·2026
Same author

Music Familiarization Elicits Functional Connectivity Between Right Frontal/Temporal and Parietal Areas in the Theta and Alpha Bands.

Brain topography·2024
Same author

Identifying alterations in hand movement coordination from chronic stroke survivors using a wearable high-density EMG sleeve.

Journal of neural engineering·2024
Same author

Upper limb intention tremor assessment: opportunities and challenges in wearable technology.

Journal of neuroengineering and rehabilitation·2024
Same author

Listening to familiar music induces continuous inhibition of alpha and low-beta power.

Journal of neurophysiology·2023
Same author

Printed Silk Microelectrode Arrays for Electrophysiological Recording and Controlled Drug Delivery.

Advanced healthcare materials·2023

Related Experiment Video

Updated: Jul 23, 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

Human-robot collaborative task planning using anticipatory brain responses.

Stefan K Ehrlich1, Emmanuel Dean-Leon2, Nicholas Tacca3

  • 1Chair for Cognitive Systems, Department of Electrical Engineering, TUM School of Computation, Information and Technology, Technical University of Munich, Munich, Germany.

Plos One
|July 11, 2023
PubMed
Summary
This summary is machine-generated.

This study shows electroencephalogram (EEG) measures can enable robots to learn dynamic task assignments from human partners. This brain-computer interface approach facilitates adaptive human-robot collaboration for complex tasks.

More Related Videos

Correlating Behavioral Responses to fMRI Signals from Human Prefrontal Cortex: Examining Cognitive Processes Using Task Analysis
10:33

Correlating Behavioral Responses to fMRI Signals from Human Prefrontal Cortex: Examining Cognitive Processes Using Task Analysis

Published on: June 20, 2012

12.8K
Functional Near Infrared Spectroscopy of the Sensory and Motor Brain Regions with Simultaneous Kinematic and EMG Monitoring During Motor Tasks
11:31

Functional Near Infrared Spectroscopy of the Sensory and Motor Brain Regions with Simultaneous Kinematic and EMG Monitoring During Motor Tasks

Published on: December 5, 2014

15.2K

Related Experiment Videos

Last Updated: Jul 23, 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
Correlating Behavioral Responses to fMRI Signals from Human Prefrontal Cortex: Examining Cognitive Processes Using Task Analysis
10:33

Correlating Behavioral Responses to fMRI Signals from Human Prefrontal Cortex: Examining Cognitive Processes Using Task Analysis

Published on: June 20, 2012

12.8K
Functional Near Infrared Spectroscopy of the Sensory and Motor Brain Regions with Simultaneous Kinematic and EMG Monitoring During Motor Tasks
11:31

Functional Near Infrared Spectroscopy of the Sensory and Motor Brain Regions with Simultaneous Kinematic and EMG Monitoring During Motor Tasks

Published on: December 5, 2014

15.2K

Area of Science:

  • Robotics
  • Neuroscience
  • Human-Computer Interaction

Background:

  • Human-robot interaction (HRI) requires adaptable robotic systems for seamless collaboration.
  • Dynamic subtask assignment is a key challenge in HRI, especially when human intentions are not explicit.
  • Robots often struggle to infer human partners' task choices for efficient collaboration.

Purpose of the Study:

  • To investigate the feasibility of using electroencephalogram (EEG) based neuro-cognitive measures for online robot learning of dynamic subtask assignment.
  • To develop and validate a reinforcement learning algorithm that utilizes EEG feedback for adaptive HRI.
  • To demonstrate the potential of brain-computer interfaces in enhancing human-robot collaborative task planning.

Main Methods:

  • An experimental study with a UR10 robotic manipulator and human subjects performing a joint task.
  • Utilized electroencephalogram (EEG) measures to detect human anticipation of task takeovers.
  • Developed a reinforcement learning algorithm using EEG signals as neuronal feedback for dynamic subtask assignment.
  • Validated the algorithm through simulation studies.

Main Results:

  • EEG measures were found to be indicative of human anticipation in human-robot task transitions.
  • The reinforcement learning algorithm demonstrated successful robot learning of subtask assignment with around 80% accuracy within 17 minutes.
  • The system showed feasibility for scalability to more subtasks, with increased learning times.
  • Even with moderate decoding accuracies, effective robot learning of dynamic task assignments was achieved.

Conclusions:

  • EEG-based neuro-cognitive measures are a viable tool for mediating dynamic subtask assignment in HRI.
  • This approach offers a promising solution for the complex challenge of adaptive human-robot collaborative task planning.
  • Brain-computer interfaces can significantly enhance the flexibility and efficiency of robotic partners in collaborative tasks.