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

Motor and Sensory Areas of the Cortex01:14

Motor and Sensory Areas of the Cortex

6.0K
The cerebral cortex, the brain's outermost layer, is pivotal in processing complex cognitive tasks, emotions, and various sensory inputs and executing voluntary motor activities. This intricate structure is divided into three primary functional areas: the motor areas, sensory areas, and association areas.
Motor Areas
The motor areas located in the frontal lobe are central to controlling voluntary movements. This region is further subdivided into the primary motor cortex and the premotor cortex....
6.0K
Motor Unit Stimulation01:20

Motor Unit Stimulation

3.2K
When the neuron of a motor unit fires an action potential, it triggers a series of events, leading to a twitch contraction in the muscle fibers. The process of excitation-contraction coupling is crucial in relaying the action potential to the muscle fibers.
The latent period of contraction marks the onset of excitation-contraction coupling, when the action potential propagates across the sarcolemma, preparing the muscle fibers for contraction. As the fibers enter the contraction phase, the...
3.2K

You might also read

Related Articles

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

Sort by
Same author

Study on the metal composition characteristics of high-end bags in Korean domestic distribution using portable XRF.

Analytical methods : advancing methods and applications·2026
Same author

Altered functional hierarchical and sequential organization in individuals with schizophrenia during auditory processing.

NeuroImage·2026
Same author

Distinct temporal patterns of beta bursts differentiate successful and failed movement cancellation.

iScience·2026
Same author

Psychological Empowerment on the Streets: Designing and Validating Multisensory Experiences in Simulated Autonomous Driving.

Annals of the New York Academy of Sciences·2026
Same author

Enhancing the Performance of Event-Related Potential-Based Brain-Computer Interfaces Under Cognitive Distraction: A Multiwindow Adaptive Approach.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society·2026
Same author

Toward zero-calibration MEG brain-computer interfaces based on event-related fields.

Biomedical engineering letters·2026

Related Experiment Video

Updated: Nov 30, 2025

Functional Near Infrared Spectroscopy of the Sensory and Motor Brain Regions with Simultaneous Kinematic and EMG Monitoring During Motor Tasks
11:31

Functional Near Infrared Spectroscopy of the Sensory and Motor Brain Regions with Simultaneous Kinematic and EMG Monitoring During Motor Tasks

Published on: December 5, 2014

15.5K

Decoding Kinematic Information From Primary Motor Cortex Ensemble Activities Using a Deep Canonical Correlation

Min-Ki Kim1, Jeong-Woo Sohn2, Sung-Phil Kim1

  • 1Department of Biomedical Engineering, Ulsan National Institute of Science and Technology, Ulsan, South Korea.

Frontiers in Neuroscience
|November 12, 2020
PubMed
Summary

Deep canonical correlation analysis (DCCA) improves brain-machine interface (BMI) performance by better decoding neural activity. This novel algorithm enhances the understanding of neural and behavioral relationships for arm movement control.

Keywords:
Kalman filterdecoding algorithmdeep canonical correlation analysisintracortical brain–machine interfacelong short-term memory recurrent neural networkprimary motor cortex (M1)

More Related Videos

Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

20.3K
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

43.8K

Related Experiment Videos

Last Updated: Nov 30, 2025

Functional Near Infrared Spectroscopy of the Sensory and Motor Brain Regions with Simultaneous Kinematic and EMG Monitoring During Motor Tasks
11:31

Functional Near Infrared Spectroscopy of the Sensory and Motor Brain Regions with Simultaneous Kinematic and EMG Monitoring During Motor Tasks

Published on: December 5, 2014

15.5K
Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

20.3K
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

43.8K

Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Machine Learning

Background:

  • Intracortical brain-machine interfaces (BMIs) decode primary motor cortex (M1) neural activity for arm movement control.
  • Improving decoding algorithms is crucial for understanding neural-behavioral relationships and enhancing BMI performance.

Purpose of the Study:

  • To propose and evaluate a deep canonical correlation analysis (DCCA) algorithm for decoding M1 neural activity.
  • To investigate the effectiveness of DCCA in capturing the relationship between M1 activity and kinematic information during reaching tasks.
  • To assess if DCCA-enhanced neural representations improve decoding performance using linear and non-linear models.

Main Methods:

  • Developed a DCCA algorithm utilizing deep learning for non-linear mapping of neuronal activity to canonical variables.
  • Applied DCCA to M1 neural recordings from non-human primates performing a reaching task.
  • Compared decoding performance using DCCA-derived neural representations with traditional methods (CCA, PCA, FA, LDS) and original firing rates (FRs) with Linear Kalman Filter (LKF) and LSTM-RNN decoders.

Main Results:

  • DCCA significantly improved the decoding accuracy of arm movement velocity and position compared to linear CCA, PCA, FA, and LDS.
  • Decoding using DCCA-enhanced neural representations with LSTM-RNN showed marked improvement (6.6% for velocity, 16.0% for position) over decoding original FRs.
  • DCCA successfully identified kinematics-related canonical variables within M1 activity.

Conclusions:

  • DCCA offers an efficient method for decoding neural activity in BMIs.
  • The DCCA algorithm enhances the identification of neural representations linked to movement kinematics.
  • This approach holds promise for advancing the design of more effective decoding models for intracortical BMIs.