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

Incomplete Cognitive Assessment in Post-Acute Stroke Rehabilitation: Recovering Clinically Interpretable Relationships from Routine Neuropsychological Data.

NeuroRehabilitation·2026
Same author

Changes in Motor, Functional Independence, and Gait Recovery After Incomplete Spinal Cord Injury with Transcutaneous Spinal Cord Stimulation: A Randomized Controlled Trial with a Partial Crossover Design.

Biomedicines·2026
Same author

Uses of assistive technology incorporating smart camera features in the rehabilitation of people living with disabilities: a scoping review.

Disability and rehabilitation. Assistive technology·2026
Same author

An online brain-computer interface for detecting incongruity in augmented reality applications.

Journal of neural engineering·2026
Same author

Turning motor intentions into words: an MRCP-based BCI speller for motor-impaired users enhanced by task-specific calibration.

Journal of neural engineering·2026
Same author

Artificial intelligence for tailoring complex clinical information to patients and families: A technical perspective.

The journal of spinal cord medicine·2026

Related Experiment Video

Updated: Apr 27, 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

A co-adaptive brain-computer interface for end users with severe motor impairment.

Josef Faller1, Reinhold Scherer1, Ursula Costa2

  • 1Institute for Knowledge Discovery, Graz University of Technology, Graz, Austria.

Plos One
|July 12, 2014
PubMed
Summary

Co-adaptive brain-computer interface (BCI) training effectively improved performance in severely motor-impaired users. This novel BCI system, utilizing event-related desynchronization, offers a promising path toward intuitive, self-paced control for individuals with disabilities.

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: Apr 27, 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
  • Biomedical Engineering
  • Rehabilitation Technology

Background:

  • Co-adaptive training enhances brain-computer interface (BCI) performance in healthy individuals.
  • The efficacy of these paradigms for users with severe motor impairments remains under-explored.
  • Existing BCIs often require extensive setup and expert calibration.

Purpose of the Study:

  • To evaluate a novel cue-guided, co-adaptive BCI training paradigm for individuals with severe motor impairment.
  • To assess a self-paced BCI training paradigm utilizing an auto-calibrated classifier.
  • To determine the feasibility of intuitive, self-paced control through a non-control state.

Main Methods:

  • Utilized electroencephalogram (EEG) from three bipolar derivations (C3, Cz, C4) for online analysis.
  • Participants performed right-hand movement imagery (MI), left-hand MI, and a non-control state (eyes open relaxation).
  • Employed auto-calibration, regular recalibration, trial-based outlier rejection, and linear discriminant analysis with logarithmic band-power features.

Main Results:

  • The co-adaptive BCI significantly outperformed chance for 18 of 22 users within 24 minutes.
  • The self-paced BCI paradigm performed significantly better than chance for 11 of 20 users.
  • The co-adaptive BCI demonstrated rapid auto-calibration (under 5 minutes) and effective classification against the non-control state.

Conclusions:

  • The developed co-adaptive BCI is effective for severely motor-impaired users, offering a significant improvement over chance.
  • The system supports a non-control state, requires minimal setup, and operates online with few electrodes.
  • Preliminary results suggest the potential for improved self-paced BCI systems for disabled users.