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

Stability and neurophysiological validity of graph connectivity features for non-stationary motor imagery BCIs.

Journal of neural engineering·2026
Same author

Make it fun but keep it simple: EEG reveals the impact of easy yet engaging games for stroke rehabilitation.

Journal of neuroengineering and rehabilitation·2026
Same author

An out-of-the-lab evaluation of dry EEG technology on a large-scale motor imagery brain-computer interface dataset.

Journal of neural engineering·2025
Same author

Wearable technologies for assisted mobility in the real world.

Nature communications·2025
Same author

MI-CES: An explainable weak labelling approach to example selection for Motor Imagery BCI classification<sup></sup>.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025
Same author

An AI-Enabled Low-Cost Wearable to Support Musculoskeletal Rehabilitation: A Proof of Concept.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025

Related Experiment Video

Updated: May 7, 2026

Brain-Computer Interface-controlled Upper Limb Robotic System for Enhancing Daily Activities in Stroke Patients
06:11

Brain-Computer Interface-controlled Upper Limb Robotic System for Enhancing Daily Activities in Stroke Patients

Published on: April 18, 2025

1.9K

Transferring brain-computer interfaces beyond the laboratory: successful application control for motor-disabled

Robert Leeb1, Serafeim Perdikis, Luca Tonin

  • 1Chair in Non-Invasive Brain-Machine Interface, Center for Neuroprosthetics, École Polytechnique Fédérale de Lausanne, Station 11, CH-1015 Lausanne, Switzerland(1).

Artificial Intelligence in Medicine
|October 15, 2013
PubMed
Summary

Fifty percent of motor-disabled individuals successfully controlled brain-computer interfaces (BCIs) for applications like tele-presence robots and text entry. This study highlights key challenges and lessons learned in transferring BCI technology to home and clinic settings.

Keywords:
Application controlBrain–computer interface (BCI)Electroencephalogram (EEG)End-userMotor imageryTechnology transfer

More Related Videos

Author Spotlight: Using Motor Imagery Brain-Computer Interface to Improve Motor and Cognitive Function in Stroke Patients
09:42

Author Spotlight: Using Motor Imagery Brain-Computer Interface to Improve Motor and Cognitive Function in Stroke Patients

Published on: September 1, 2023

2.6K
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

4.2K

Related Experiment Videos

Last Updated: May 7, 2026

Brain-Computer Interface-controlled Upper Limb Robotic System for Enhancing Daily Activities in Stroke Patients
06:11

Brain-Computer Interface-controlled Upper Limb Robotic System for Enhancing Daily Activities in Stroke Patients

Published on: April 18, 2025

1.9K
Author Spotlight: Using Motor Imagery Brain-Computer Interface to Improve Motor and Cognitive Function in Stroke Patients
09:42

Author Spotlight: Using Motor Imagery Brain-Computer Interface to Improve Motor and Cognitive Function in Stroke Patients

Published on: September 1, 2023

2.6K
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

4.2K

Area of Science:

  • Neuroscience
  • Rehabilitation Engineering
  • Human-Computer Interaction

Background:

  • Brain-computer interfaces (BCIs) are increasingly used by patients outside laboratory settings.
  • Successful application control requires a high level of BCI proficiency.
  • The feasibility of training naïve users for home-based BCI control remains a key question.

Purpose of the Study:

  • To assess the effectiveness of training motor-disabled individuals to control applications using BCIs in real-world environments.
  • To determine the training duration needed for end-users to achieve proficient BCI control.
  • To identify challenges and lessons learned during the transfer of BCI technology from lab to home/clinic.

Main Methods:

  • Trained 24 motor-disabled participants in rehabilitation clinics and homes without BCI expert supervision.
  • Focused on enabling control of tele-presence robots and text-entry systems.
  • Documented user experiences and technical challenges encountered.

Main Results:

  • 50% of participants achieved good BCI performance, successfully controlling applications.
  • Tele-presence robot control achieved an average performance ratio of 0.87.
  • Text-entry system control yielded a mean performance of 0.93.

Conclusions:

  • BCI technology shows promise for real-world applications in home and clinic settings.
  • User training and technology transfer present significant, but surmountable, challenges.
  • Lessons learned are applicable to other groups developing end-user BCI systems.