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

Brain Imaging01:14

Brain Imaging

Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans),  magnetic resonance imaging (MRI),  functional magnetic resonance imaging (fMRI), and Transcranial Magnetic Stimulation (TMS).

You might also read

Related Articles

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

Sort by
Same author

Predicting Motor Imagery BCI Performance Based on EEG Microstate Analysis.

Brain sciences·2023
Same author

LDER: a classification framework based on ERP enhancement in RSVP task.

Journal of neural engineering·2023
Same author

The effect of speech-gesture asynchrony on the neural coupling of interlocutors in interpreter-mediated communication.

Social cognitive and affective neuroscience·2023
Same author

A transfer learning-based feedback training motivates the performance of SMR-BCI.

Journal of neural engineering·2022
Same author

Effluent lipopolysaccharide is a prompt marker of peritoneal dialysis-related gram-negative peritonitis.

Peritoneal dialysis international : journal of the International Society for Peritoneal Dialysis·2020
Same author

Current Status and Prospects in the Treatment of Erectile Dysfunction by Adipose-Derived Stem Cells in the Diabetic Animal Model.

Sexual medicine reviews·2020

Related Experiment Video

Updated: Jun 29, 2026

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

15.7K

A Neurophysiological Stratification Framework for Intermediate Motor Imagery-BCI Users Based on Independent

Xu Duan1, Songyun Xie2, Yujie Cui1

  • 1Key Laboratory for Artificial Intelligence and Cognitive Neuroscience of Language, Xi'an International Studies University, Xi'an 710128, China.

Brain Sciences
|February 27, 2026
PubMed
Summary

This study introduces a new method to classify motor imagery brain-computer interface (MI-BCI) users, identifying distinct groups including those with unilateral proficiency. This stratification can help tailor training for better BCI control.

Keywords:
BCI illiteracyEEG characterizationbrain-computer interfaceindependent component analysismotor imageryneurophysiological stratification

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

2.2K
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

1.9K

Related Experiment Videos

Last Updated: Jun 29, 2026

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

15.7K
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

2.2K
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

1.9K

Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Human-Computer Interaction

Background:

  • Motor imagery-based brain-computer interfaces (MI-BCIs) offer communication for individuals with motor impairments.
  • Many users exhibit intermediate performance, failing to achieve reliable control despite accuracies above chance.
  • Asymmetric motor imagery abilities in intermediate users necessitate improved early assessment and training.

Purpose of the Study:

  • To develop a refined method for stratifying users of motor imagery-based brain-computer interfaces (MI-BCIs).
  • To identify distinct user groups beyond 'good' and 'poor' performers, particularly those with unilateral MI proficiency.
  • To inform the design of personalized training protocols for MI-BCI users.

Main Methods:

  • Independent Component Analysis (ICA) was used to extract event-related desynchronization/synchronization (ERD/ERS) components from EEG data.
  • A rule-based automated framework integrated dipole localization, ERD/ERS characteristics, and frequency-band power features.
  • Power spectral parameterization and statistical methods were applied for accurate ERD estimation, followed by clustering for participant categorization.

Main Results:

  • Participants were stratified into four groups: good performers (24.8%), poor performers (35.8%), left-hand proficient/right-hand poor (LgoodRpoor, 27.5%), and left-hand poor/right-hand proficient (LpoorRgood, 11.9%).
  • Unilateral performers showed no significant differences in contralateral ERD.
  • Substantial differences in ipsilateral ERS were observed in unilateral performers.

Conclusions:

  • The independent event-related brain dynamics model provides a more granular stratification of MI-BCI users.
  • Characterizing users based on unilateral motor imagery proficiency is crucial.
  • Findings support the development of graded training protocols tailored to individual user capabilities, especially for those with asymmetric abilities.