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

You might also read

Related Articles

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

Sort by
Same author

Hierarchical Multi-Scale Mamba with Tubular Structure-Aware Convolution for Retinal Vessel Segmentation.

Entropy (Basel, Switzerland)·2025
Same author

Serum and dietary antioxidant status is associated with lower prevalence of the metabolic syndrome in a study in Shanghai, China.

Asia Pacific journal of clinical nutrition·2013
Same author

Target-responsive "sweet" hydrogel with glucometer readout for portable and quantitative detection of non-glucose targets.

Journal of the American Chemical Society·2013
Same author

Calcium oscillations-coupled conversion of actin travelling waves to standing oscillations.

Proceedings of the National Academy of Sciences of the United States of America·2013
Same author

[Relationship between skin carotenoid level and metabolic syndrome related indices].

Zhonghua yi xue za zhi·2013
Same author

Adaptive segmentation of magnetic resonance images with intensity inhomogeneity using level set method.

Magnetic resonance imaging·2013
Same journal

Anchor-based disentanglement framework for incremental multi-view clustering.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Complex-valued amplitude-phase interference modeling for adversarially robust classification.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

TraNce: Type-aware hypergraph neural network with biological mediators for drug repositioning.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Decentralized ADMM for factorization-based Low-rank matrix estimation.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Memristive neuromorphic circuit design inspired by the neural mechanisms of conditioned fear.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Q-learning based asynchronous Boolean control networks stabilization with data loss.

Neural networks : the official journal of the International Neural Network Society·2026
See all related articles

Related Experiment Video

Updated: Sep 15, 2025

Cortical Source Analysis of High-Density EEG Recordings in Children
09:32

Cortical Source Analysis of High-Density EEG Recordings in Children

Published on: June 30, 2014

21.5K

ACCNet: Adaptive cross-frequency coupling graph attention for EEG emotion recognition.

Dongyuan Tian1, Yucheng Wang2, Peiliang Gong3

  • 1College of Computer Science and Technology (CCST), Jilin University, China.

Neural Networks : the Official Journal of the International Neural Network Society
|July 17, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces ACCNet for improved EEG-based emotion recognition. The novel framework enhances personalized affective computing by adaptively analyzing brain signals, leading to more accurate and stable results.

Keywords:
Cross-frequency couplingElectroencephalography (EEG)Emotion recognitionGraph neural networks (GNNs)

More Related Videos

Conscious and Non-conscious Representations of Emotional Faces in Asperger's Syndrome
08:31

Conscious and Non-conscious Representations of Emotional Faces in Asperger's Syndrome

Published on: July 31, 2016

13.6K
Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example
08:45

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Published on: October 24, 2012

14.8K

Related Experiment Videos

Last Updated: Sep 15, 2025

Cortical Source Analysis of High-Density EEG Recordings in Children
09:32

Cortical Source Analysis of High-Density EEG Recordings in Children

Published on: June 30, 2014

21.5K
Conscious and Non-conscious Representations of Emotional Faces in Asperger's Syndrome
08:31

Conscious and Non-conscious Representations of Emotional Faces in Asperger's Syndrome

Published on: July 31, 2016

13.6K
Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example
08:45

Mapping Cortical Dynamics Using Simultaneous MEG/EEG and Anatomically-constrained Minimum-norm Estimates: an Auditory Attention Example

Published on: October 24, 2012

14.8K

Area of Science:

  • Neuroscience
  • Computer Science
  • Affective Computing

Background:

  • Electroencephalography (EEG)-based emotion recognition offers objective neural insights for personalized affective computing.
  • Graph Neural Networks (GNNs) excel at modeling spatial EEG channel relationships but face limitations in data sparsity and cross-frequency interactions for single-user applications.

Purpose of the Study:

  • To introduce ACCNet, a novel framework designed to enhance personalized emotion recognition using EEG signals.
  • To address limitations in current GNN approaches for EEG-based emotion recognition, particularly concerning data sparsity and complex frequency interactions.

Main Methods:

  • Proposed an adaptive band decomposition strategy for subject-specific EEG signal representation and individualized frequency-domain analysis.
  • Introduced a cross-frequency coupling mechanism to learn personalized frequency relationships from a node-edge perspective, focusing on low- and high-frequency interactions.
  • Enhanced GNNs' capacity to capture user-specific frequency interactions within EEG data.

Main Results:

  • ACCNet demonstrated superior performance in single-user emotion recognition tasks, outperforming existing methods.
  • Empirical evaluations confirmed the methodology's effectiveness in capturing personalized frequency interactions.
  • ACCNet exhibited exceptional resilience to labeling noise, validating its reliability for real-world applications.

Conclusions:

  • ACCNet significantly advances personalized emotion recognition by leveraging adaptive frequency analysis and cross-frequency coupling.
  • The framework offers a more accurate, stable, and robust solution for affective computing applications.
  • The developed methodology provides a reliable tool for real-world emotion recognition using EEG.