Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Brain Imaging

Brain Imaging

Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans), magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), and Transcranial Magnetic Stimulation (TMS).

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

EEG-Controlled Exoskeleton for Walking and Standing: A Longitudinal Multimodal Dataset of Healthy Individuals.

Scientific data·2026

Same author

A Systematic Review and Meta-Analysis of EEG, fMRI, and fNIRS Studies on the Psychological Impact of Nature on Well-Being.

International journal of environmental research and public health·2026

Same author

The state of science convergence in implantable brain-computer interface clinical trials.

Journal of neural engineering·2025

Same author

Mobile Brain-Body Imaging and Visual Data of Theatrical Actors During Rehearsal and Performance.

Scientific data·2025

Same author

At-Home Stroke Neurorehabilitation: Early Findings with the NeuroExo BCI System.

Sensors (Basel, Switzerland)·2025

Same author

Neural Dynamics of Creative Movements During the Rehearsal and Performance of "LiveWire".

Scientific data·2024

Same journal

Analysis of End-Tidal CO2 Variability During Plateau Waves Episodes: An Information Theoretic Approach<sup></sup>.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025

Same journal

AI and Tomosynthesis for Breast Cancer Molecular Subtyping: A step toward precision medicine<sup></sup>.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025

Same journal

Towards Sustainable Protein Recovery from Biological Waste: Assessing Polyethersulfone-based Microfiltration.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025

Same journal

Analysis of the cardiovascular response to standardized polymicrobial peritonitis experimental model.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025

Same journal

Automated Wrist Ultrasound Image Bone Enhancement and Segmentation Using Deep Learning.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025

Same journal

A Deep Learning approach for Depressive Symptoms assessment in Parkinson's disease patients using facial videos.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference·2025

See all related articles

Search research articles

Related Experiment Video

Updated: May 25, 2026

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

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

Published on: July 7, 2023

Design principles for noninvasive brain-machine interfaces.

José L Contreras-Vidal¹, Trent J Bradberry

¹Department of Kinesiology, and the Graduate Programs in Bioengineering, and Neuroscience and Cognitive Science, University of Maryland, College Park, MD 20742, USA. pepeum@umd.edu

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

|January 19, 2012

Summary

This summary is machine-generated.

This study proposes design principles for a noninvasive brain-machine interface (BMI) using electroencephalography (EEG) to control advanced prosthetic limbs. The goal is to achieve dexterous control for individuals with upper limb disarticulation.

More Related Videos

Assessment and Communication for People with Disorders of Consciousness

Assessment and Communication for People with Disorders of Consciousness

Published on: August 1, 2017

A Neural Implant Design Toolbox for Nonhuman Primates

A Neural Implant Design Toolbox for Nonhuman Primates

Published on: February 9, 2024

Related Experiment Videos

Last Updated: May 25, 2026

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

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

Published on: July 7, 2023

Assessment and Communication for People with Disorders of Consciousness

Assessment and Communication for People with Disorders of Consciousness

Published on: August 1, 2017

A Neural Implant Design Toolbox for Nonhuman Primates

A Neural Implant Design Toolbox for Nonhuman Primates

Published on: February 9, 2024

Area of Science:

Neuroscience and Biomedical Engineering
Prosthetics and Rehabilitation

Background:

Sophisticated prosthetic limbs require advanced control systems.
Neural measurements offer potential for intuitive prosthetic control.
Noninvasive methods are preferred for widespread adoption.

Purpose of the Study:

To propose design principles for a noninvasive EEG-based brain-machine interface (BMI).
To enable dexterous control of high-degree-of-freedom prosthetic limbs.
To address the control challenges in upper limb disarticulation.

Main Methods:

Focus on electroencephalography (EEG) as the primary neural measurement.
Design principles for a closed-loop control system.
Integration with biologically realistic prosthetic limb models.

Main Results:

Design principles for a noninvasive EEG-BMI system.
Framework for achieving dexterous prosthetic limb control.
Potential for improved functional outcomes in amputees.

Conclusions:

Noninvasive EEG-based BMI is a viable approach for prosthetic limb control.
The proposed design principles can guide future development.
This technology holds promise for restoring function after upper limb loss.