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

Polarity Inversion-Driven Band Structure Modulation, Strain Engineering, and Electrical Property Analysis on GaN/4H-SiC Heterojunctions.

ACS omega·2026
Same author

Diagnostic Accuracy of MiRNA Panels for Endometrial Cancer: A Systematic Review and Meta-Analysis.

Journal of lower genital tract disease·2026
Same author

rSiglec-10(V set) armed oncolytic adenovirus improves the effects of virotherapy through enhancing oncolysis and antitumor immunity.

International immunopharmacology·2026
Same author

Developmental trajectories of prefrontal activation underlying emotional inhibitory control from adolescence to young adulthood: a functional near-infrared spectroscopy study.

Behavioral and brain functions : BBF·2026
Same author

Advances in vascularized organoids.

Chinese medical journal·2026
Same author

Addressing interfacial chemical corrosion in lithium metal batteries: a ferroelectric-dipole-regulation route.

Chemical science·2026
Same journal

Solvent Extraction of Metals in the Circular Economy: Enhancing Resource Efficiency and Sustainability.

TheScientificWorldJournal·2026
Same journal

Agronomic Performance and Nutritive Value Evaluation of Desho Grass Varieties Under Supplementary Irrigation in Western Oromia, Ethiopia.

TheScientificWorldJournal·2026
Same journal

Physicians' and Hospital Administrators' Perspectives of Diagnosis-Related Groups (DRGs) in High-Income Countries: A Systematic Review.

TheScientificWorldJournal·2026
Same journal

The Eco-Friendly Preparation of Se, Zn, and Ag MONPs and Their Current Medical Applications and Drug Delivery for AD Diseases.

TheScientificWorldJournal·2026
Same journal

Fear of COVID-19: A Comparative Study Among University Students in Peru.

TheScientificWorldJournal·2026
Same journal

Opportunities and Challenges of Integrating Ethiopian Traditional Medicine System Into Modern Medicine: A Narrative Review.

TheScientificWorldJournal·2026
See all related articles

Related Experiment Video

Updated: Apr 26, 2026

Assessment and Communication for People with Disorders of Consciousness
07:37

Assessment and Communication for People with Disorders of Consciousness

Published on: August 1, 2017

11.4K

Enhanced performance by time-frequency-phase feature for EEG-based BCI systems.

Baolei Xu1, Yunfa Fu2, Gang Shi3

  • 1State Key Laboratory of Robotics, Shenyang Institute of Automation (SIA), Chinese Academy of Sciences (CAS), Shenyang 110016, China ; University of Chinese Academy of Sciences, Beijing 100049, China.

Thescientificworldjournal
|July 22, 2014
PubMed
Summary
This summary is machine-generated.

A new motor imagery paradigm using clench speed and force shows that time-frequency-phase features improve Brain-Computer Interface (BCI) accuracy. Scaled features and mRMR selection enhance classification rates, reaching 92% accuracy.

More Related Videos

Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis
08:22

Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis

Published on: April 26, 2024

3.4K
A Single-Channel and Non-Invasive Wearable Brain-Computer Interface for Industry and Healthcare
06:34

A Single-Channel and Non-Invasive Wearable Brain-Computer Interface for Industry and Healthcare

Published on: July 7, 2023

3.6K

Related Experiment Videos

Last Updated: Apr 26, 2026

Assessment and Communication for People with Disorders of Consciousness
07:37

Assessment and Communication for People with Disorders of Consciousness

Published on: August 1, 2017

11.4K
Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis
08:22

Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis

Published on: April 26, 2024

3.4K
A Single-Channel and Non-Invasive Wearable Brain-Computer Interface for Industry and Healthcare
06:34

A Single-Channel and Non-Invasive Wearable Brain-Computer Interface for Industry and Healthcare

Published on: July 7, 2023

3.6K

Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Signal Processing

Background:

  • Brain-Computer Interfaces (BCIs) enable control through neural signals.
  • Motor imagery, specifically clenching, is a common BCI paradigm.
  • Extracting robust features is crucial for accurate BCI control.

Purpose of the Study:

  • To introduce a novel motor parameter imagery paradigm using clench speed and force.
  • To investigate the effectiveness of time-frequency-phase features for motor imagery classification.
  • To compare feature optimization methods and machine learning algorithms for BCI.

Main Methods:

  • Extracted time-frequency-phase features from mu and beta rhythms.
  • Optimized features using MIFS and mRMR criteria with scaled and no-scaled approaches.
  • Classified clench speed vs. clench force imagery using Support Vector Machines (SVMs) and Extreme Learning Machines (ELMs).

Main Results:

  • No significant difference in classification rate between SVMs and ELMs.
  • Scaled features improved classification accuracy (p<0.01) compared to no-scaled features.
  • mRMR feature selection yielded higher classification rates (p<0.01) than MIFS.
  • Time-frequency-phase features improved classification by ~20% over time-frequency features.
  • Achieved a maximum classification accuracy of 92% for clench speed vs. force imagery.

Conclusions:

  • The motor parameter imagery paradigm enhances direct control commands for BCIs.
  • Time-frequency-phase features significantly improve BCI classification accuracy.
  • Feature scaling and mRMR selection are effective strategies for optimizing BCI performance.