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

Hierarchy of Motor Control01:18

Hierarchy of Motor Control

2.4K
The hierarchy of motor control refers to the different levels of organization and processing involved in controlling movement in the body. These levels range from higher cortical areas involved in planning and decision-making to lower spinal cord reflexes that respond automatically to external stimuli.
2.4K
Motor and Sensory Areas of the Cortex01:14

Motor and Sensory Areas of the Cortex

2.7K
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...
2.7K

You might also read

Related Articles

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

Sort by
Same author

Neutrophil-to-lymphocyte ratio is associated with diabetic peripheral neuropathy in type 2 diabetes patients.

Diabetes research and clinical practice·2017
Same author

Genetic variation and phylogeographic structure of the cotton aphid, Aphis gossypii, based on mitochondrial DNA and microsatellite markers.

Scientific reports·2017
Same author

Tetramethylpyrazine blocks TFAM degradation and up-regulates mitochondrial DNA copy number by interacting with TFAM.

Bioscience reports·2017
Same author

Mitochondrial LON protease-dependent degradation of cytochrome c oxidase subunits under hypoxia and myocardial ischemia.

Biochimica et biophysica acta. Bioenergetics·2017
Same author

The efficacy and safety of epinephrine for postoperative bleeding in total joint arthroplasty: A PRISMA-compliant meta-analysis.

Medicine·2017
Same author

The correlation between histological gastritis staging- 'OLGA/OLGIM' and serum pepsinogen test in assessment of gastric atrophy/intestinal metaplasia in China.

Scandinavian journal of gastroenterology·2017

Related Experiment Video

Updated: May 20, 2025

Author Spotlight: Enhancing Neurorehabilitation Through EEG, Motor Imagery, and Virtual Reality
10:14

Author Spotlight: Enhancing Neurorehabilitation Through EEG, Motor Imagery, and Virtual Reality

Published on: May 10, 2024

834

Dynamic Hierarchical Convolutional Attention Network for Recognizing Motor Imagery Intention.

Bin Lu, Fuwang Wang, Junxiang Chen

    IEEE Transactions on Cybernetics
    |March 25, 2025
    PubMed
    Summary

    This study introduces a novel dynamic hierarchical convolutional attention network (DH-CAN) for electroencephalogram (EEG) decoding. The DH-CAN model effectively captures both local and global brain signal features, improving brain-computer interface performance.

    More Related Videos

    Author Spotlight: Using Motor Imagery Brain-Computer Interface to Improve Motor and Cognitive Function in Stroke Patients
    09:42

    Author Spotlight: Using Motor Imagery Brain-Computer Interface to Improve Motor and Cognitive Function in Stroke Patients

    Published on: September 1, 2023

    1.1K
    The "Motor" in Implicit Motor Sequence Learning: A Foot-stepping Serial Reaction Time Task
    10:39

    The "Motor" in Implicit Motor Sequence Learning: A Foot-stepping Serial Reaction Time Task

    Published on: May 3, 2018

    8.4K

    Related Experiment Videos

    Last Updated: May 20, 2025

    Author Spotlight: Enhancing Neurorehabilitation Through EEG, Motor Imagery, and Virtual Reality
    10:14

    Author Spotlight: Enhancing Neurorehabilitation Through EEG, Motor Imagery, and Virtual Reality

    Published on: May 10, 2024

    834
    Author Spotlight: Using Motor Imagery Brain-Computer Interface to Improve Motor and Cognitive Function in Stroke Patients
    09:42

    Author Spotlight: Using Motor Imagery Brain-Computer Interface to Improve Motor and Cognitive Function in Stroke Patients

    Published on: September 1, 2023

    1.1K
    The "Motor" in Implicit Motor Sequence Learning: A Foot-stepping Serial Reaction Time Task
    10:39

    The "Motor" in Implicit Motor Sequence Learning: A Foot-stepping Serial Reaction Time Task

    Published on: May 3, 2018

    8.4K

    Area of Science:

    • Neuroscience
    • Biomedical Engineering
    • Machine Learning

    Background:

    • Accurate decoding of brain intentions from electroencephalogram (EEG) signals relies on understanding neural activity patterns in localized brain regions.
    • Current deep learning models for EEG decoding often overlook local spatial features, focusing primarily on global patterns, which can limit decoding accuracy.
    • There is a need for advanced models that can integrate both local and global spatial information, along with time-frequency dynamics, for improved EEG signal analysis.

    Purpose of the Study:

    • To propose a novel dynamic hierarchical convolutional attention network (DH-CAN) for comprehensive EEG decoding.
    • To enhance EEG decoding by capturing discriminative information from global and local spatial domains, as well as time-frequency domains.
    • To improve the understanding of motor imagery patterns through advanced feature extraction and regional connectivity analysis.

    Main Methods:

    • Developed a dynamic hierarchical convolutional attention network (DH-CAN) incorporating multiscale convolutional blocks for time-frequency feature extraction.
    • Mapped EEG signal channels to distinct brain regions and hierarchically extracted global and local spatial features.
    • Utilized a graph attention network to model regional connectivity and shared network parameters between symmetrical brain regions to capture asymmetrical patterns.

    Main Results:

    • The proposed DH-CAN model demonstrated excellent performance across multiple evaluation metrics on two benchmark datasets.
    • The model significantly outperformed existing benchmark methods in EEG decoding tasks.
    • The findings highlight the effectiveness of integrating local and global spatial features, time-frequency information, and regional connectivity.

    Conclusions:

    • The DH-CAN model offers a novel and effective approach for EEG decoding by comprehensively analyzing neural activity.
    • Integrating local spatial information and regional connectivity alongside global features significantly enhances decoding performance.
    • This research provides a new perspective for developing advanced brain-computer interfaces and understanding neural mechanisms.