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

Muscles of the Eye

Muscles of the Eye

The muscles of the eye are sophisticated structures that control eye movement and focus, allowing for the precise and rapid adjustments necessary for vision. The human eye is controlled by ten muscles — six extraocular muscles, three intraocular muscles, and one primary eyelid retractor muscle.
Extraocular Muscles
The six extraocular muscles surround the eyeball and control its movements. They are responsible for a wide range of eye motions, including looking up, down, left, right, and...

You might also read

Related Articles

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

Sort by

Same author

Duration-modulated neural population dynamics in humans during BMI controls.

Communications biology·2026

Same author

The Compositional Encoding of Hand-Eye Coordinated Movements for Single Neurons in the Posterior Parietal Cortex.

bioRxiv : the preprint server for biology·2026

Same author

Real-time brain-computer interface control of walking exoskeleton with bilateral sensory feedback.

Brain stimulation·2026

Same author

Hierarchical and Context-Dependent Encoding of Actions in Human Posterior Parietal and Motor Cortex.

bioRxiv : the preprint server for biology·2025

Same author

Charge density of multi-channel intra-cortical micro-stimulation modulates intensity and naturalness of evoked somatosensations.

Journal of neural engineering·2025

Same author

Visual context affects the perceived timing of tactile sensations elicited through intracortical microstimulation: a case study of two participants.

Journal of neurophysiology·2025

Same journal

Chemotactic self-organization captures the dynamics of mammalian hair follicle patterning.

Proceedings of the National Academy of Sciences of the United States of America·2026

Same journal

Tomographic imaging of superconducting order using particle-hole interference.

Proceedings of the National Academy of Sciences of the United States of America·2026

Same journal

Inhibitory potential of autologous neutralizing antibodies sets quantitative limits on the rebound-competent HIV-1 reservoir.

Proceedings of the National Academy of Sciences of the United States of America·2026

Same journal

Inferring epidemiological parameters under an infectious phylogeography model with visitor dynamics.

Proceedings of the National Academy of Sciences of the United States of America·2026

Same journal

Analytical modeling for suction cup designs for skin-interfaced wearable devices.

Proceedings of the National Academy of Sciences of the United States of America·2026

Same journal

Improving cell-free metabolism through direct integration of artificial respiratory chains.

Proceedings of the National Academy of Sciences of the United States of America·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Apr 20, 2026

VisualEyes: A Modular Software System for Oculomotor Experimentation

VisualEyes: A Modular Software System for Oculomotor Experimentation

Published on: March 25, 2011

Brain-machine interface for eye movements.

Arnulf B A Graf¹, Richard A Andersen¹

¹Division of Biology and Biological Engineering, California Institute of Technology, Pasadena, CA 91125 graf@vis.caltech.edu andersen@vis.caltech.edu.

Proceedings of the National Academy of Sciences of the United States of America

|November 26, 2014

Summary

This summary is machine-generated.

Brain-machine interfaces (BMIs) can now decode intended eye movements from neuronal activity in nonhuman primates. This advancement shows promise for assisting paralyzed patients by controlling devices with eye movement plans.

Keywords:

brain–machine interface eye movements lateral intraparietal area learning saccades

More Related Videos

Simultaneous Eye Tracking and Single-Neuron Recordings in Human Epilepsy Patients

Simultaneous Eye Tracking and Single-Neuron Recordings in Human Epilepsy Patients

Published on: June 17, 2019

Eye Movement Monitoring of Memory

Eye Movement Monitoring of Memory

Published on: August 15, 2010

Related Experiment Videos

Last Updated: Apr 20, 2026

VisualEyes: A Modular Software System for Oculomotor Experimentation

VisualEyes: A Modular Software System for Oculomotor Experimentation

Published on: March 25, 2011

Simultaneous Eye Tracking and Single-Neuron Recordings in Human Epilepsy Patients

Simultaneous Eye Tracking and Single-Neuron Recordings in Human Epilepsy Patients

Published on: June 17, 2019

Eye Movement Monitoring of Memory

Eye Movement Monitoring of Memory

Published on: August 15, 2010

Area of Science:

Neuroscience
Neuroprosthetics
Brain-Computer Interfaces

Background:

Brain-machine interfaces (BMIs) have successfully decoded reach intentions in humans and nonhuman primates (NHPs).
Previous research has not explored applying BMIs to decode intended eye movements.

Purpose of the Study:

To investigate the feasibility of using BMIs to decode intended eye movements from neuronal activity.
To assess the potential of such BMIs to aid paralyzed patients.

Main Methods:

Recorded neuronal activity from the lateral intraparietal area (LIP) in NHPs.
Used Bayesian inference to predict eye movement plans in real time from LIP neuronal ensembles.
Decoded eye movement plans without requiring the animal to make an actual eye movement.

Main Results:

Real-time prediction of eye movement plans was achieved using small ensembles of LIP neurons.
Prediction accuracy improved through learning at the neuronal ensemble level, especially for challenging predictions.
Population learning involved BMI parameter updates and changes in individual neuron responses.
Decoded eye movement plans were used to control a computer cursor.

Conclusions:

Neuronal ensemble responses can be shaped to optimize BMI prediction accuracy.
BMIs for decoding eye movements are a promising assistive technology for individuals with paralysis.
This study provides strong evidence for the potential of eye-movement-based BMIs.