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

Emotion Recognition Using Multi-View EEG-fNIRS and Cross-Attention Feature Fusion.

Biosensors·2026
Same author

EEG-fNIRS Cross-Subject Emotion Recognition Based on Attention Graph Isomorphism Network and Contrastive Learning.

Brain sciences·2026
Same author

MFA-CNN: An Emotion Recognition Network Integrating 1D-2D Convolutional Neural Network and Cross-Modal Causal Features.

Brain sciences·2025
Same author

Emotion Recognition Based on a EEG-fNIRS Hybrid Brain Network in the Source Space.

Brain sciences·2025
Same author

EEG-fNIRS-Based Emotion Recognition Using Graph Convolution and Capsule Attention Network.

Brain sciences·2024
Same author

Diagnostic Predictive Value of Tryptase, Serum Amyloid A and Lipoprotein-Associated Phospholipase A2 Biomarker Groups for Large Atherosclerotic Cerebral Infarction.

Emergency medicine international·2022

Related Experiment Video

Updated: Aug 16, 2025

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy
11:15

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

Published on: June 27, 2013

33.8K

Granger-Causality-Based Multi-Frequency Band EEG Graph Feature Extraction and Fusion for Emotion Recognition.

Jing Zhang1, Xueying Zhang1, Guijun Chen1

  • 1College of Information and Computer, Taiyuan University of Technology, Taiyuan 030024, China.

Brain Sciences
|December 23, 2022
PubMed
Summary

This study introduces a novel Graph Convolutional Neural Network (GCN) method using Granger Causality (GC) for electroencephalogram (EEG) emotion recognition. The approach enhances feature extraction and fusion across multiple frequency bands, improving accuracy.

Keywords:
Granger causalityemotion recognitionfeature fusiongraph convolutional neural networkgraph feature extraction

More Related Videos

Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis
08:22

Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis

Published on: April 26, 2024

2.0K
Reliable Acquisition of Electroencephalography Data during Simultaneous Electroencephalography and Functional MRI
11:00

Reliable Acquisition of Electroencephalography Data during Simultaneous Electroencephalography and Functional MRI

Published on: March 19, 2021

4.5K

Related Experiment Videos

Last Updated: Aug 16, 2025

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy
11:15

Applications of EEG Neuroimaging Data: Event-related Potentials, Spectral Power, and Multiscale Entropy

Published on: June 27, 2013

33.8K
Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis
08:22

Author Spotlight: Advancing the Study of Brain-Heart Interplay with a Comprehensive EEGLAB Plugin for Multimodal Signal Analysis

Published on: April 26, 2024

2.0K
Reliable Acquisition of Electroencephalography Data during Simultaneous Electroencephalography and Functional MRI
11:00

Reliable Acquisition of Electroencephalography Data during Simultaneous Electroencephalography and Functional MRI

Published on: March 19, 2021

4.5K

Area of Science:

  • Neuroscience
  • Machine Learning
  • Signal Processing

Background:

  • Graph Convolutional Neural Networks (GCNs) are widely used for electroencephalogram (EEG) emotion recognition.
  • Existing GCN methods often fail to fully utilize causal connections between EEG signals across different frequency bands.
  • The construction of adjacency matrices in current GCNs does not adequately represent the complex relationships within EEG data.

Purpose of the Study:

  • To propose a novel multi-frequency band EEG graph feature extraction and fusion method for improved emotion recognition.
  • To leverage Granger Causality (GC) analysis to capture causal connectivity between EEG channels.
  • To enhance GCN performance by integrating multi-frequency band graph information.

Main Methods:

  • Calculated Granger Causality (GC) matrices for EEG signals within each frequency band.
  • Converted GC matrices to asymmetric binary matrices using an optimal threshold.
  • Developed a GC-based GCN (GC-GCN) using differential entropy features and binary GC matrices.
  • Proposed a multi-frequency band fusion method (GC-F-GCN) integrating graph information from different frequency bands.

Main Results:

  • The proposed GC-F-GCN method demonstrated superior performance compared to existing state-of-the-art GCN methods.
  • Achieved high average accuracies: 97.91% for arousal, 98.46% for valence, and 98.15% for arousal-valence classification.
  • Effectively integrated multi-frequency band information for more robust EEG emotion recognition.

Conclusions:

  • The GC-F-GCN method offers a significant advancement in EEG-based emotion recognition by incorporating causal connectivity.
  • The fusion of multi-frequency band graph features enhances the discriminative power of the model.
  • This approach provides a promising direction for developing more accurate and reliable emotion recognition systems.