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

An online brain-computer interface for detecting incongruity in augmented reality applications.

Journal of neural engineering·2026
Same author

Turning motor intentions into words: an MRCP-based BCI speller for motor-impaired users enhanced by task-specific calibration.

Journal of neural engineering·2026
Same author

Rigid control of motor unit firing rates in the human tibialis anterior muscle persists during neurofeedback.

Journal of neurophysiology·2026
Same author

Source localization of simulated neural signals in a cervical spinal cord model.

Journal of neural engineering·2026
Same author

Biomechanical Analysis of an Elite Para Standing Cross-Country Skier Using Lower Limb Prostheses: A Case Study.

Sensors (Basel, Switzerland)·2026
Same author

Dareplane: a modular open-source software platform for BCI research with application in closed-loop deep brain stimulation.

Journal of neural engineering·2025

Related Experiment Video

Updated: Feb 23, 2026

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

44.2K

Decoding natural reach-and-grasp actions from human EEG.

Andreas Schwarz1, Patrick Ofner1, Joana Pereira1

  • 1Institute of Neural Engineering, Graz University of Technology, Stremayrgasse 16/IV, 8010 Graz, Austria.

Journal of Neural Engineering
|August 31, 2017
PubMed
Summary

Researchers can now differentiate common hand grasps using electroencephalography (EEG) brain signals. This advance in understanding neural correlates of reach-and-grasp actions could lead to intuitive neuroprosthetic control.

More Related Videos

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants
11:14

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants

Published on: October 4, 2015

11.6K
Decoding Natural Behavior from Neuroethological Embedding
08:00

Decoding Natural Behavior from Neuroethological Embedding

Published on: October 3, 2025

755

Related Experiment Videos

Last Updated: Feb 23, 2026

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

44.2K
A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants
11:14

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants

Published on: October 4, 2015

11.6K
Decoding Natural Behavior from Neuroethological Embedding
08:00

Decoding Natural Behavior from Neuroethological Embedding

Published on: October 3, 2025

755

Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Human-Computer Interaction

Background:

  • The human hand performs many daily tasks using a limited repertoire of grasps like palmar, pincer, and lateral.
  • Understanding the neural basis of these grasps is crucial for developing advanced assistive technologies.

Purpose of the Study:

  • To discriminate between three common executed reach-and-grasp actions (palmar, pincer, lateral) using their electroencephalography (EEG) neural correlates.
  • To investigate the feasibility of using EEG for controlling neuroprosthetics.

Main Methods:

  • A cue-guided experiment involving 15 healthy individuals performing reach-and-grasp actions with everyday objects.
  • Recording EEG data during palmar, pincer, lateral grasps, and a no-movement condition.
  • Utilizing low-frequency time-domain features (0.3-3 Hz) for classification analysis.

Main Results:

  • Binary classification between grasp types achieved 72.4% accuracy.
  • Classification between grasps and no-movement reached a peak performance of 93.5%.
  • Multiclass classification (including no-movement) peaked at 65.9% accuracy using a 1000ms feature extraction window.

Conclusions:

  • Non-invasive EEG can effectively discriminate between common executed reach-and-grasp actions.
  • Significant differences in neural correlates were observed across all tested conditions.
  • Findings support the development of intuitive neuroprosthetic control for individuals with motor impairments.