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

Doping Manipulation of Donor/Acceptor by Perovskite Quantum Dots Enables >20.5% Organic Nonfullerene Solar Cells.

Advanced materials (Deerfield Beach, Fla.)·2026
Same author

RAN: A randomness-anchored watermark attacking network with stealth and effectiveness.

Scientific reports·2026
Same author

Non-invasive CT-based Deep Learning for Human Papillomavirus Status Prediction in Oropharyngeal Cancer.

Academic radiology·2026
Same author

Brain-Computer Interface Combined with Functional Electrical Stimulation for Post-Stroke Upper Limb Motor Recovery: A Systematic Review and Meta-Analysis.

Clinical EEG and neuroscience·2026
Same author

Achieving Maximum Chirality and Enhancing Third-Harmonic Generation via Quasi-Bound States in the Continuum in Nonlinear Metasurfaces.

Nanomaterials (Basel, Switzerland)·2026
Same author

Effect of Tai Chi Yunshou motor imagery training on upper limb motor dysfunction with stroke patients.

BMC complementary medicine and therapies·2026

Related Experiment Video

Updated: May 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

1.1K

A multi-day and high-quality EEG dataset for motor imagery brain-computer interface.

Banghua Yang1,2, Fenqi Rong3, Yunlong Xie3

  • 1School of Mechatronic Engineering and Automation, Research Center of Brain-Computer Engineering, Shanghai University, Shanghai, China. yangbanghua@shu.edu.cn.

Scientific Data
|March 24, 2025
PubMed
Summary

This study introduces a large electroencephalography (EEG) dataset for brain-computer interface (BCI) research. The dataset aids in developing robust motor imagery (MI) BCI systems that perform reliably across multiple days and subjects.

More Related Videos

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

834
Multifunctional Setup for Studying Human Motor Control Using Transcranial Magnetic Stimulation, Electromyography, Motion Capture, and Virtual Reality
08:09

Multifunctional Setup for Studying Human Motor Control Using Transcranial Magnetic Stimulation, Electromyography, Motion Capture, and Virtual Reality

Published on: September 3, 2015

10.8K

Related Experiment Videos

Last Updated: May 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

1.1K
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

834
Multifunctional Setup for Studying Human Motor Control Using Transcranial Magnetic Stimulation, Electromyography, Motion Capture, and Virtual Reality
08:09

Multifunctional Setup for Studying Human Motor Control Using Transcranial Magnetic Stimulation, Electromyography, Motion Capture, and Virtual Reality

Published on: September 3, 2015

10.8K

Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Machine Learning

Background:

  • Developing robust electroencephalography (EEG)-based brain-computer interfaces (BCIs) faces challenges with inter-day variability and low signal-to-noise ratios, particularly for motor imagery (MI) tasks.
  • Large, reliable datasets are crucial for training BCI models that generalize across sessions and subjects, mitigating inherent EEG signal instability.

Purpose of the Study:

  • To introduce a comprehensive motor imagery (MI) dataset collected during the 2019 World Robot Conference Contest-BCI Robot Contest.
  • To facilitate research in cross-session and cross-subject motor imagery BCI by providing raw and preprocessed EEG data.

Main Methods:

  • Collected EEG data from 62 healthy participants across three recording sessions.
  • Included two experimental paradigms: two-class (left/right hand grasping) and three-class (left/right hand grasping, foot hooking).
  • Utilized deep learning models (EEGNet for two-class, deepConvNet for three-class) to evaluate dataset performance.

Main Results:

  • Achieved an average classification accuracy of 85.32% for two-class MI tasks using EEGNet.
  • Attained an average classification accuracy of 76.90% for three-class MI tasks using deepConvNet.
  • The dataset includes both raw and preprocessed EEG data for diverse research applications.

Conclusions:

  • The presented MI dataset is a valuable resource for advancing EEG-based BCI research.
  • This dataset will aid in addressing critical challenges related to cross-session and cross-subject variability in motor imagery BCI systems.
  • The availability of this dataset encourages further development and validation of BCI algorithms.