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

Association Areas of the Cortex01:21

Association Areas of the Cortex

5.2K
Association areas are regions of the cerebral cortex that do not have a specific sensory or motor function. Instead, they integrate and interpret information from various sources to enable higher cognitive processes such as memory, learning, and decision-making. Some key association areas include the following:
Prefrontal Association Area: This area is located in the frontal lobe and is involved in planning, decision-making, and moderating social behavior. It connects with primary motor areas,...
5.2K

You might also read

Related Articles

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

Sort by
Same author

Six anthropometric indices and all-cause mortality in ageing populations: an observational cohort study in six longitudinal studies.

BMC public health·2026
Same author

A Wearable Bioelectronic Patch With Adaptive Photodynamic and Electrical Stimulation for Drug-Resistant Bacteria Infected Burn Wound Healing.

Small (Weinheim an der Bergstrasse, Germany)·2026
Same author

Integrated Proteomic and Metabolomic Profiling for Developing Novel Plasma-Based Diagnostic Models of Sarcopenia.

Journal of cachexia, sarcopenia and muscle·2026
Same author

Targeting TFAP2β condensation suppresses the development of esophageal squamous cell carcinoma.

Cell·2025
Same author

Plasma Proteomic High-Performance Biomarkers for Early Diagnosis of Colorectal Cancer.

Journal of proteome research·2025
Same author

Macrophage Notch1 signaling modulates regulatory T cells via the TGFB axis in early MASLD.

JHEP reports : innovation in hepatology·2024

Related Experiment Video

Updated: Jun 13, 2025

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging
11:28

Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging

Published on: June 30, 2018

11.6K

Cortical ROI Importance Improves MI Decoding From EEG Using Fused Light Neural Network.

Linlin Wang, Mingai Li, Dongqin Xu

    IEEE Transactions on Neural Systems and Rehabilitation Engineering : a Publication of the IEEE Engineering in Medicine and Biology Society
    |September 16, 2024
    PubMed
    Summary

    This study introduces a new method for decoding motor imagery (MI) brain signals using representative dipoles (RDs) and a lightweight deep learning network. This approach enhances brain-computer interface accuracy for intelligent rehabilitation applications.

    More Related Videos

    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

    15.1K
    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

    1.2K

    Related Experiment Videos

    Last Updated: Jun 13, 2025

    Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging
    11:28

    Concurrent EEG and Functional MRI Recording and Integration Analysis for Dynamic Cortical Activity Imaging

    Published on: June 30, 2018

    11.6K
    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

    15.1K
    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

    1.2K

    Area of Science:

    • Neuroscience
    • Biomedical Engineering
    • Machine Learning

    Background:

    • Deep learning for motor imagery (MI) decoding in the brain shows promise for intelligent rehabilitation.
    • Extracting personalized features from numerous dipoles is complex and requires sophisticated neural networks.

    Purpose of the Study:

    • To develop a novel, efficient method for decoding motor imagery (MI) at the cortical level.
    • To simplify feature extraction by representing regions of interest (ROIs) with representative dipoles (RDs).

    Main Methods:

    • Proposed representing each ROI with a single representative dipole (RD) to capture comprehensive regional activity.
    • Utilized Random Forest for ROI importance (RI) quantification and reinforced sub-band spectral powers.
    • Developed an ensemble representation of RD feature image sequences (ERDFIS) and a lightweight 2D separable convolution and gated recurrent unit (2DSCG) network for feature extraction and classification.

    Main Results:

    • Achieved high decoding accuracies of 89.89% and 94.35% on two public datasets using the ERDFIS-2DSCG method.
    • Demonstrated that RDs effectively represent ROI properties across time-frequency-space domains.
    • Showcased the utility of ROI importance (RI) in highlighting subject-specific MI-EEG characteristics.

    Conclusions:

    • The proposed RD approach simplifies feature extraction while maintaining comprehensive representation.
    • The ERDFIS-2DSCG method offers an effective and lightweight solution for cortical-level MI decoding.
    • This technique holds significant potential for advancing brain-computer interfaces in intelligent rehabilitation.