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 Unit Stimulation01:20

Motor Unit Stimulation

2.0K
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...
2.0K
Motor and Sensory Areas of the Cortex01:14

Motor and Sensory Areas of the Cortex

4.6K
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....
4.6K
Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

142
A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
142

You might also read

Related Articles

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

Sort by
Same author

Upregulation of CDO1 by theaflavin-3,3'-gallate induces apoptosis and inhibits cell proliferation in TNBC.

International immunopharmacology·2026
Same author

Dynamic Remodeling of Plant Cytoskeleton in Response to Environmental Stress.

Biology·2026
Same author

Identification of Misdiagnosis Factors in Allergic Bronchopulmonary Mycosis Using Explainable Machine Learning.

Journal of asthma and allergy·2026
Same author

Design, Synthesis, and Antifungal Evaluation of Novel α-Methylene-γ-Butyrolactone Derivatives Bearing a Diaryl Ether Moiety for Phytopathogen Control.

Journal of agricultural and food chemistry·2026
Same author

Non-invasive CT-based Deep Learning for Human Papillomavirus Status Prediction in Oropharyngeal Cancer.

Academic radiology·2026
Same author

Comparison of short- and long-term efficacy between percutaneous and open pedicle screw fixation for thoracolumbar vertebral fracture.

American journal of translational research·2026
Same journal

Olfactory Perception and Neural Rhythms: A Simulation-Based EEG Analysis Using Power Spectral Density FeaturesOlfactory perception and neural rhythms: a simulation-based eeg analysis using power spectral density features.

Cognitive neurodynamics·2026
Same journal

An event-related potentials account of brain predictive coding.

Cognitive neurodynamics·2026
Same journal

A recurrent neural network model for a decision-making task based on sequential evidence accumulation.

Cognitive neurodynamics·2026
Same journal

Synaptic neurotransmitter concentration modulation during learning in bio-inspired spiking neural network.

Cognitive neurodynamics·2026
Same journal

A two-neuron HETUF-memristive hopfield neural network and its application in image encryption.

Cognitive neurodynamics·2026
Same journal

MEK-ERK inhibition enhances synaptic input-output coupling and neuronal excitability in the rat dentate gyrus: association with site-specific Kv4.2 phosphorylation.

Cognitive neurodynamics·2026
See all related articles

Related Experiment Video

Updated: Sep 13, 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

1.2K

DSTA-Net: dynamic spatio-temporal feature augmentation network for motor imagery classification.

Liang Chang1, Banghua Yang1, Jiayang Zhang2

  • 1School of Mechatronic Engineering and Automation, Research Center of Brain-Computer Engineering, Shanghai University, Shanghai, 200444 China.

Cognitive Neurodynamics
|July 28, 2025
PubMed
Summary
This summary is machine-generated.

The Dynamic Spatio-Temporal Feature Augmentation Network (DSTA-Net) improves motor imagery decoding accuracy for stroke rehabilitation by enhancing spatio-temporal feature extraction from EEG data.

Keywords:
Constrained grouped spatial convolutionDynamic spatio-temporal feature augmentation network (DSTA-Net)Motor imageryMulti-level spatial featuresStroke rehabilitation

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

1.5K

Related Experiment Videos

Last Updated: Sep 13, 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

1.2K
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

1.5K

Area of Science:

  • Neuroscience
  • Biomedical Engineering
  • Machine Learning

Background:

  • Accurate decoding of Motor Imagery (MI) is crucial for advancing MI applications in stroke rehabilitation.
  • Nonstationarity and high intra-class variability in MI-Electroencephalography (EEG) present challenges for reliable spatio-temporal feature extraction.
  • Existing methods struggle to effectively capture the complex dynamics of MI-EEG signals.

Purpose of the Study:

  • To develop a novel network, DSTA-Net, for enhanced Motor Imagery (MI) decoding.
  • To improve the accuracy and interpretability of MI-EEG signal analysis for stroke rehabilitation.
  • To address the challenges of nonstationarity and high intra-class variability in MI-EEG data.

Main Methods:

  • Proposed the Dynamic Spatio-Temporal Feature Augmentation Network (DSTA-Net) integrating DSTA and Spatio-Temporal Convolution (STC) modules.
  • DSTA module utilizes multi-scale temporal convolutional kernels for α and β bands and Grouped Spatial Convolutions for multi-level spatial features.
  • STC module further extracts features for classification, with DeepLIFT, Common Spatial Pattern, and t-SNE used for interpretability analysis.

Main Results:

  • DSTA-Net demonstrated significant accuracy improvements over ShallowConvNet across multiple public and self-collected stroke datasets (e.g., 6.29% on BCI-IV-2a, 3.99% on OpenBMI).
  • Achieved average accuracy gains of 6.29% (p < 0.01), 3.05% (p < 0.01), 5.26% (p < 0.01), and 2.25% in tenfold cross-validation.
  • Interpretability analysis confirmed the model's ability to identify key EEG channels and spatial patterns.

Conclusions:

  • DSTA-Net effectively enhances spatio-temporal feature extraction for improved Motor Imagery (MI) decoding accuracy.
  • The network's superiority offers promising new insights for MI-based stroke rehabilitation research and applications.
  • The developed model provides a robust framework for analyzing complex MI-EEG signals.