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

224
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...
224

You might also read

Related Articles

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

Sort by
Same author

Mitochondrial-derived peptide MOTS-c activates metabolic signaling but blunts reparative function in human mesenchymal stromal cells.

Inflammation and regeneration·2026
Same author

Comparative performance of stress hyperglycemia ratio, glycemic gap, and hemoglobin glycation index for predicting functional outcomes after intravenous thrombolysis in acute ischemic stroke.

Clinical neurology and neurosurgery·2026
Same author

Robust Decomposition of Surface EMG Signals via Lightweight Deep Learning-Based Adaptation.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society·2026
Same author

Integrating Inflammation and Lipid Metabolism Biomarkers for Early Risk Stratification in Acute Cerebral Infarction: A Nomogram-Based Approach.

Mediators of inflammation·2026
Same author

Effect of DL-3-n-Butylphthalide on Cerebral Hypoperfusion Due to Atherosclerotic Stenosis: A Multicenter, Double-Blind, Randomized Controlled, Preliminary Trial.

CNS drugs·2026
Same author

Association between CT-evaluated Poststenotic Dilatation in Human Renal Artery Stenosis and Kidney Release of MCP-1.

Radiology·2026
Same journal

Ultra-flexible wireless endovascular stimulator for cortical simulation.

Journal of neural engineering·2026
Same journal

Influence of frequency and pulse train duration on respiratory responses during transcutaneous phrenic nerve stimulation in humans.

Journal of neural engineering·2026
Same journal

Dynamic functional graph-Laplacian priors integrated with optimization for EEG source localization.

Journal of neural engineering·2026
Same journal

Unveiling subject-specific causal latency in motor imagery: a physiologically transparent BCI via Riemannian tangent space fusion.

Journal of neural engineering·2026
Same journal

Cross-subject decoding of human neural data for speech Brain Computer Interfaces.

Journal of neural engineering·2026
Same journal

Cognitive and brain function enhancement in Gen X group after personalized, AI supervised EEG-neurofeedback training.

Journal of neural engineering·2026
See all related articles

Related Experiment Video

Updated: Jun 20, 2025

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

1.2K

A model-based brain switch via periodic motor imagery modulation for asynchronous brain-computer interfaces.

Jianjun Meng1,2, Songwei Li1,2, Guangye Li1,2

  • 1Department of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, People's Republic of China.

Journal of Neural Engineering
|July 19, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a new virtual physical model brain switch that significantly reduces false positive rates (FPRs) in brain-computer interfaces (BCIs). The novel approach enhances usability and practicality for asynchronous BCIs.

Keywords:
asynchronous brain-computer interfacesbrain switchmodel-basedmotor imagery

More Related Videos

Non-Invasive Electrical Brain Stimulation Montages for Modulation of Human Motor Function
07:47

Non-Invasive Electrical Brain Stimulation Montages for Modulation of Human Motor Function

Published on: February 4, 2016

13.0K
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

937

Related Experiment Videos

Last Updated: Jun 20, 2025

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

1.2K
Non-Invasive Electrical Brain Stimulation Montages for Modulation of Human Motor Function
07:47

Non-Invasive Electrical Brain Stimulation Montages for Modulation of Human Motor Function

Published on: February 4, 2016

13.0K
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

937

Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Computer Science

Background:

  • Asynchronous brain-computer interfaces (BCIs) decode user intention without pre-programmed structures.
  • Current electroencephalography (EEG)-based brain switches suffer from high false positive rates (FPRs), limiting their practical application.
  • Improving the operating mode and usability of brain switches is crucial for advancing BCI technology.

Purpose of the Study:

  • To develop a novel brain switch with improved operating modes and usability.
  • To reduce the false positive rate (FPR) of brain switches for enhanced practicality.
  • To optimize brain switch performance for asynchronous BCI applications.

Main Methods:

  • Proposed a novel virtual physical model-based brain switch utilizing periodic active modulation.
  • Formulated an optimization problem to minimize triggering time while maintaining a required FPR.
  • Obtained numerical and analytical approximate solutions based on the developed model.
  • Applied Common Spatial Pattern (CSP) and optimization methods to further enhance the brain switch.

Main Results:

  • The motor imagery (MI)-based brain switch achieved an FPR of 0.8 FPs/h with a median triggering time of 58 seconds.
  • Online device control evaluations demonstrated substantially lower average FPRs compared to conventional brain switches.
  • The MI-CSP-based brain switch achieved an average FPR of 0.3 FPs/h and an improved average triggering time of 21.6 seconds.

Conclusions:

  • The developed brain switch significantly reduces FPRs (less than 1 FPs/h), achieving over a tenfold decrease compared to other endogenous methods.
  • The reaction time is comparable to state-of-the-art approaches, representing a significant advancement for non-invasive asynchronous BCIs.
  • This approach holds promise for widespread clinical applications of BCI technology.