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

Sensory-guided human-machine joint learning accelerates the acquisition of motor imagery brain computer interface control.

Nature communications·2026
Same author

Adaptive Neural Reorganization Enables Real-Time Finger-Level Robotic Control in BCI-Naïve Stroke Survivors.

bioRxiv : the preprint server for biology·2026
Same author

Cell-Type-Specific Bidirectional Modulation of the Cortico-Thalamo-Cortical Sensory Pathway by Transcranial Focused Ultrasound (tFUS).

bioRxiv : the preprint server for biology·2026
Same author

EEG Foundation Model Improves Online Directional Motor Imagery Brain-computer Interface Control.

bioRxiv : the preprint server for biology·2026
Same author

Low-intensity transcranial focused ultrasound engages parvalbumin-positive GABAergic interneurons in a humanized mouse model of chronic pain: from electrophysiology to cellular investigation.

Journal of neural engineering·2026
Same author

Enhanced Spread of Carbapenem-Resistant <i>Pseudomonas aeruginosa</i> in ICU Environment During a COVID-19 Upsurge Period in China.

Infection and drug resistance·2026

Related Experiment Video

Updated: Sep 17, 2025

Brain-Computer Interface-controlled Upper Limb Robotic System for Enhancing Daily Activities in Stroke Patients
06:11

Brain-Computer Interface-controlled Upper Limb Robotic System for Enhancing Daily Activities in Stroke Patients

Published on: April 18, 2025

838

EEG-based brain-computer interface enables real-time robotic hand control at individual finger level.

Yidan Ding1, Chalisa Udompanyawit2, Yisha Zhang1

  • 1Department of Biomedical Engineering, Carnegie Mellon University, Pittsburgh, PA, USA.

Nature Communications
|June 30, 2025
PubMed
Summary

This study introduces a brain-computer interface (BCI) for controlling robotic fingers using brain signals. The noninvasive electroencephalography (EEG)-BCI achieved high accuracy in decoding intended finger movements for enhanced robotic hand control.

More Related Videos

The Bionic Clicker Mark I & II
08:23

The Bionic Clicker Mark I & II

Published on: August 14, 2017

16.5K
Author Spotlight: Enhancing Grasping Abilities for Hemiplegic Patients with Flexible Robotic Limbs
03:55

Author Spotlight: Enhancing Grasping Abilities for Hemiplegic Patients with Flexible Robotic Limbs

Published on: October 27, 2023

2.3K

Related Experiment Videos

Last Updated: Sep 17, 2025

Brain-Computer Interface-controlled Upper Limb Robotic System for Enhancing Daily Activities in Stroke Patients
06:11

Brain-Computer Interface-controlled Upper Limb Robotic System for Enhancing Daily Activities in Stroke Patients

Published on: April 18, 2025

838
The Bionic Clicker Mark I & II
08:23

The Bionic Clicker Mark I & II

Published on: August 14, 2017

16.5K
Author Spotlight: Enhancing Grasping Abilities for Hemiplegic Patients with Flexible Robotic Limbs
03:55

Author Spotlight: Enhancing Grasping Abilities for Hemiplegic Patients with Flexible Robotic Limbs

Published on: October 27, 2023

2.3K

Area of Science:

  • Neuroscience
  • Robotics
  • Biomedical Engineering

Background:

  • Noninvasive brain-computer interfaces (BCIs) offer potential for individuals with motor impairments but face challenges in control precision.
  • Current electroencephalography (EEG)-BCI systems often struggle with intuitive mappings and accurate control of complex movements.

Purpose of the Study:

  • To develop and evaluate a real-time noninvasive BCI system for controlling robotic fingers using individual finger movements.
  • To advance EEG-BCI technology by decoding brain signals for intended finger movements into precise robotic actions.

Main Methods:

  • Utilized a noninvasive electroencephalography (EEG)-BCI system incorporating movement execution (ME) and motor imagery (MI) for individual finger control.
  • Employed a deep neural network for decoding brain signals, with fine-tuning to enhance BCI performance.
  • Tested the system with 21 able-bodied, experienced BCI users performing two- and three-finger tasks.

Main Results:

  • Achieved real-time decoding accuracies of 80.56% for two-finger motor imagery tasks.
  • Attained 60.61% accuracy for three-finger motor imagery tasks.
  • Demonstrated successful decoding of intended finger movements into corresponding robotic motions.

Conclusions:

  • The developed noninvasive EEG-BCI system enables naturalistic robotic hand control at the individual finger level.
  • The findings highlight the feasibility of using ME and MI for precise, real-time robotic control via brain signals.
  • This research contributes to the advancement of assistive technologies for individuals with motor disabilities.