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

Classification of Skeletal Muscle Fibers01:48

Classification of Skeletal Muscle Fibers

Skeletal muscles continuously produce ATP to provide the energy that enables muscle contractions. Skeletal muscle fibers can be categorized into three types based on differences in their contraction speed and how they produce ATP, as well as physical differences related to these factors. Most human muscles contain all three muscle fiber types, albeit in varying proportions.
Slow-Twitch Muscle Fibers
Slow oxidative, muscle fibers appear red due to large numbers of capillaries and high levels of...

You might also read

Related Articles

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

Sort by
Same author

Long-term unsupervised recalibration of cursor-based intracortical brain-computer interfaces using a hidden Markov model.

Nature biomedical engineering·2025
Same author

The dynamics and geometry of choice in the premotor cortex.

Nature·2025
Same author

Plug-and-Play Stability for Intracortical Brain-Computer Interfaces: A One-Year Demonstration of Seamless Brain-to-Text Communication.

Advances in neural information processing systems·2024
Same author

Learning leaves a memory trace in motor cortex.

Current biology : CB·2024
Same author

Preparatory activity and the expansive null-space.

Nature reviews. Neuroscience·2024
Same author

Brain control of bimanual movement enabled by recurrent neural networks.

Scientific reports·2024

Related Experiment Video

Updated: Jun 6, 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

Low-dimensional neural features predict muscle EMG signals.

Zuley Rivera-Alvidrez1, Rachel S Kalmar, Stephen I Ryu

  • 1Department of Electrical Engineering, Stanford University, CA 94305, USA. zuley@stanford.edu

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
|November 25, 2010
PubMed
Summary

Researchers found a simple way to link brain signals from the motor cortex to muscle activity. This discovery could improve brain-computer interfaces and our understanding of movement control.

More Related Videos

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

Repeated Measurement of Respiratory Muscle Activity and Ventilation in Mouse Models of Neuromuscular Disease
09:24

Repeated Measurement of Respiratory Muscle Activity and Ventilation in Mouse Models of Neuromuscular Disease

Published on: April 17, 2017

Related Experiment Videos

Last Updated: Jun 6, 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

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

Repeated Measurement of Respiratory Muscle Activity and Ventilation in Mouse Models of Neuromuscular Disease
09:24

Repeated Measurement of Respiratory Muscle Activity and Ventilation in Mouse Models of Neuromuscular Disease

Published on: April 17, 2017

Area of Science:

  • Neuroscience
  • Motor Control
  • Biomedical Engineering

Background:

  • Decoding electromyography (EMG) from neural data is crucial for understanding motor control and developing neural prostheses.
  • Existing decoders often suffer from overfitting due to numerous parameters, limiting generalizability and interpretability.

Purpose of the Study:

  • To establish a more robust and interpretable method for relating neural activity in the motor cortex to muscle activity.
  • To investigate the relationship between mean neural activity and mean EMG during an arm-reaching task.

Main Methods:

  • Recorded simultaneous neural activity from monkey motor cortices (M1/PMd) and arm muscle EMG during a reaching task.
  • Reduced dimensionality of neural data and identified the curvature of low-dimensional neural activity as a signature for muscle activity.
  • Derived neural axes from limited data (reaches to one target) to predict EMG across multiple targets.

Main Results:

  • A low-dimensional neural activity signature effectively predicted muscle EMG across different movement targets (average R(2) = 0.65).
  • Identified a predictive lag of 47.5 ms between cortical activity and muscle activation.
  • Demonstrated that fundamental axes in neural space correlate with specific muscle activation patterns.

Conclusions:

  • Cortical population activity is tightly coupled with muscle EMG.
  • The derived neural axes offer a generalizable method for predicting muscle activity from neural signals.
  • This approach enhances understanding of neural control of movement and has implications for neural prosthesis design.