Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

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

Establishment of a versatile virus-based system for elucidating herbicide resistance in Galium aparine.

Plant physiology·2026

Same author

Improvement of speech perception following articulatory-target based production training for second language acquisition.

Frontiers in human neuroscience·2026

Same author

Coniferyl aldehyde from the phenylpropanoid pathway targets pyruvate kinase in Dactylobotrys graminicola to confer sheath rot resistance in hulless barley.

Journal of integrative plant biology·2026

Same author

Clear cell renal cell carcinoma combined with intravascular large B-cell lymphoma: a case report and review of the literature.

Frontiers in oncology·2026

Same author

Graph-Based and Multi-Stage Constraints for Hand-Object Reconstruction.

Sensors (Basel, Switzerland)·2026

Same author

Domain Adaptive Multiple Instance Self-Training for Intraoperative Anomaly Detection.

IEEE transactions on medical imaging·2026

Same journal

Cortex-anchored sensor-space harmonics for event-related EEG.

Journal of neural engineering·2026

Same journal

Neural mechanisms of mixed speech and grasp representation in sensorimotor cortices.

Journal of neural engineering·2026

Same journal

Developing a binary communication protocol between biological neural networks using virtual white matter.

Journal of neural engineering·2026

Same journal

Spatiotemporally distinctive astrocytic and neuronal responses to repetitive intracortical microstimulation.

Journal of neural engineering·2026

Same journal

A neural mass modelling framework for evaluating EEG source localisation of seizure activity.

Journal of neural engineering·2026

Same journal

Functional and effective connectivity methods from SEEG for characterizing epileptogenic networks in refractory epilepsy: a comprehensive review and future directions.

Journal of neural engineering·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Aug 12, 2025

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

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

Published on: October 24, 2012

Emotion recognition using spatial-temporal EEG features through convolutional graph attention network.

Zhongjie Li¹, Gaoyan Zhang¹, Longbiao Wang¹

¹Tianjin Key Laboratory of Cognitive Computing and Application, College of Intelligence and Computing, Tianjin University, Tianjin 300350, People's Republic of China.

Journal of Neural Engineering

|January 31, 2023

Summary

This summary is machine-generated.

This study introduces a novel Spatio-Temporal Feature Fused Convolutional Graph Attention Network (STFCGAT) for human emotion recognition using electroencephalogram (EEG) signals, achieving state-of-the-art accuracy.

Keywords:

EEG brain functional connectivity brain–computer interaction (BCI)convolutional graph attention network emotion recognition

More Related Videos

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

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

Published on: June 30, 2018

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

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

Published on: June 27, 2013

Related Experiment Videos

Last Updated: Aug 12, 2025

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

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

Published on: October 24, 2012

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

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

Published on: June 30, 2018

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

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

Published on: June 27, 2013

Area of Science:

Neuroscience
Artificial Intelligence
Biomedical Engineering

Background:

Human emotion recognition is crucial for advancing brain-computer interfaces and machine intelligence.
Electroencephalogram (EEG) signals offer a rich source of data for understanding emotional states.
Developing efficient models for EEG-based emotion recognition remains a significant challenge.

Purpose of the Study:

To develop an efficient human emotion recognition model using multi-channel EEG signals.
To integrate temporal and spatial information from EEG for improved emotion classification.
To enhance the generalization ability of emotion recognition models.

Main Methods:

A Spatio-Temporal Feature Fused Convolutional Graph Attention Network (STFCGAT) was proposed.
Combined single-channel differential entropy (DE) and cross-channel functional connectivity (FC) features.
Employed a convolutional graph attention network with a multi-headed attention mechanism for feature fusion and extraction.

Main Results:

The STFCGAT model achieved high classification accuracies on the SEED and DEAP datasets (e.g., 99.11% on SEED subject-dependent).
Demonstrated state-of-the-art performance in cross-subject emotion recognition tasks.
Ablation studies and feature analysis confirmed the model's effectiveness and highlighted brain's spatial-temporal differences across emotions.

Conclusions:

The STFCGAT architecture is highly effective for human emotion recognition from EEG signals.
The fusion of DE and FC features significantly enhances model performance.
Distinct spatial-temporal brain characteristics are associated with different emotional states.