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

The Transdiagnostic Role of Emotion Regulation Difficulties and Repetitive Negative Thinking in Depression, Anxiety, and Their Comorbidity.

Depression and anxiety·2026
Same author

An EEG correlation framework to study state anxiety and learning under uncertainty.

Journal of neural engineering·2026
Same author

Changes in repetitive negative thinking and stress perception mediate treatment effects of a transdiagnostic exercise intervention.

Psychological medicine·2026
Same author

Extension of voxel-based lesion mapping to multidimensional neurophysiological data.

Scientific reports·2025
Same author

Cardiac cycle modulates alpha and beta suppression during motor imagery.

Cerebral cortex (New York, N.Y. : 1991)·2024
Same author

Sensorimotor brain-computer interface performance depends on signal-to-noise ratio but not connectivity of the mu rhythm in a multiverse analysis of longitudinal data.

Journal of neural engineering·2024

Related Experiment Video

Updated: Oct 29, 2025

Author Spotlight: Enhancing Neurorehabilitation Through EEG, Motor Imagery, and Virtual Reality
10:14

Author Spotlight: Enhancing Neurorehabilitation Through EEG, Motor Imagery, and Virtual Reality

Published on: May 10, 2024

1.4K

Improving motor imagery classification during induced motor perturbations.

C Vidaurre1,2,3, T Jorajuría1,3, A Ramos-Murguialday4,5

  • 1Department of Statistics, Computer Science and Mathematics, Public University of Navarre, Pamplona, Spain.

Journal of Neural Engineering
|July 7, 2021
PubMed
Summary

This study improved brain-computer interfaces (BCIs) by developing methods to reduce performance drops caused by involuntary limb movements during motor imagery tasks. These techniques enhance BCI reliability for users.

Keywords:
afferent signalsbrain-computer interfacingfeedback contingencyinduced movementsmotor disturbancesmotor imageryneuro-muscular electrical stimulation

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

1.6K
Methods to Explore the Influence of Top-down Visual Processes on Motor Behavior
09:49

Methods to Explore the Influence of Top-down Visual Processes on Motor Behavior

Published on: April 16, 2014

26.5K

Related Experiment Videos

Last Updated: Oct 29, 2025

Author Spotlight: Enhancing Neurorehabilitation Through EEG, Motor Imagery, and Virtual Reality
10:14

Author Spotlight: Enhancing Neurorehabilitation Through EEG, Motor Imagery, and Virtual Reality

Published on: May 10, 2024

1.4K
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

1.6K
Methods to Explore the Influence of Top-down Visual Processes on Motor Behavior
09:49

Methods to Explore the Influence of Top-down Visual Processes on Motor Behavior

Published on: April 16, 2014

26.5K

Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Rehabilitation Technology

Background:

  • Motor imagery (MI) is crucial for brain-computer interfaces (BCIs), simulating movements to modulate brain activity.
  • Current BCIs face challenges with performance robustness due to involuntary movements, often caused by peripheral stimulation.
  • This research addresses the need to improve BCI system reliability under movement-induced perturbations.

Purpose of the Study:

  • To test and enhance the robustness of motor imagery-based BCIs against artificially generated limb movements.
  • To investigate the performance decrease in BCIs caused by movement perturbations.
  • To develop computational strategies for mitigating accuracy drops in BCIs during motor imagery.

Main Methods:

  • Conducted BCI sessions with ten participants performing motor imagery of three limbs.
  • Introduced neuromuscular stimulation to induce limb movements during specific trials.
  • Analyzed 2-class motor imagery classifications with and without induced movement perturbations.
  • Applied spatial filtering techniques to reduce neural noise from stimulation.

Main Results:

  • BCI performance remained similar to control conditions when induced movements did not involve the imagined limb.
  • Spatial filtering significantly alleviated performance drops when induced movements affected the imagined limb.
  • Reduced sensorimotor rhythm power correlated with BCI accuracy loss, and residual power predicted user performance under disturbances.

Conclusions:

  • Developed methods to ameliorate or eliminate motor-related afferent disturbances in motor imagery tasks.
  • Demonstrated that spatial filtering can significantly improve BCI robustness against movement perturbations.
  • The findings contribute to enhancing the reliability and practical application of motor imagery-based BCIs.