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

Neuroscience-Inspired Hierarchical GNN for Grasping Attempt Classification.

IEEE journal of biomedical and health informatics·2026
Same author

Discovering Interpretable Semantics from Radio Signals for Contactless Cardiac Monitoring.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026
Same author

Enhancing the performance of a deep convolutional neural network model for motor imagery classification using EEG channel-wise attention module.

Medical engineering & physics·2026
Same author

Decoupled Hierarchical Distillation for Multimodal Emotion Recognition.

IEEE transactions on pattern analysis and machine intelligence·2026
Same author

EEG-to-gait decoding via phase-aware representation learning.

Neural networks : the official journal of the International Neural Network Society·2026
Same author

Decoding Covert Speech From EEG by Functional Areas Spatio-Temporal Transformer.

IEEE journal of biomedical and health informatics·2026

Related Experiment Video

Updated: Mar 31, 2026

Using an EEG-Based Brain-Computer Interface for Virtual Cursor Movement with BCI2000
12:07

Using an EEG-Based Brain-Computer Interface for Virtual Cursor Movement with BCI2000

Published on: July 29, 2009

18.6K

Adaptive estimation of hand movement trajectory in an EEG based brain-computer interface system.

Neethu Robinson1, Cuntai Guan, A P Vinod

  • 1School of Computer Engineering, Nanyang Technological University, Singapore.

Journal of Neural Engineering
|October 27, 2015
PubMed
Summary

Researchers developed a new electroencephalography (EEG) brain-computer interface (BCI) to decode hand movement speed and position in real-time. This advanced BCI system offers improved control and efficiency for motor tasks.

More Related Videos

Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding
11:25

Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

Published on: July 26, 2013

44.3K
Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision
08:15

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision

Published on: March 28, 2025

1.4K

Related Experiment Videos

Last Updated: Mar 31, 2026

Using an EEG-Based Brain-Computer Interface for Virtual Cursor Movement with BCI2000
12:07

Using an EEG-Based Brain-Computer Interface for Virtual Cursor Movement with BCI2000

Published on: July 29, 2009

18.6K
Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding
11:25

Simultaneous Scalp Electroencephalography EEG, Electromyography EMG, and Whole-body Segmental Inertial Recording for Multi-modal Neural Decoding

Published on: July 26, 2013

44.3K
Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision
08:15

Capturing Dynamic Finger Gesturing with High-resolution Surface Electromyography and Computer Vision

Published on: March 28, 2025

1.4K

Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Signal Processing

Background:

  • Decoding hand movement parameters like trajectory and speed from brain activity is crucial for brain-computer interface (BCI) research.
  • Utilizing non-invasive electroencephalography (EEG) for decoding presents challenges due to the deep neural encoding of movement parameters.
  • Real-time continuous control in BCI systems is more practical than discrete classifications, requiring adaptive reconstruction of movement.

Purpose of the Study:

  • To adaptively reconstruct and estimate parameters of two-dimensional hand movement, specifically speed and position, from multi-channel EEG recordings.
  • To develop an efficient method for defining and selecting informative predictor variables from neural data for improved BCI control.
  • To enhance the real-time control capabilities of EEG-based BCI systems for motor tasks.

Main Methods:

  • Collected EEG data during center-out right-hand movement tasks at varying speeds and directions.
  • Employed a Kalman filter to model the relationship between brain activity and movement parameters.
  • Proposed a novel method for defining predictor variables, incorporating spatial, spectral, and temporal neural information, and selecting optimal subsets.

Main Results:

  • Achieved a correlation of (0.60 ± 0.07) between recorded and estimated hand movement data.
  • Optimized predictor subset selection resulted in a correlation of (0.57 ± 0.07, p < 0.004), with a 76% reduction in predictors.
  • Demonstrated significant gains in system stability and computational efficiency.

Conclusions:

  • The proposed system enables real-time hand movement control (speed and position) using EEG-BCI.
  • The method significantly outperforms existing EEG-based techniques in estimating movement parameters.
  • The findings highlight the potential for efficient continuous motor control via advanced EEG-BCI.