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

Association of total sedentary behaviour and television viewing with risk of overweight/obesity, type 2 diabetes and hypertension: A dose-response meta-analysis.

Diabetes, obesity & metabolism·2019

Same author

AAV-ie enables safe and efficient gene transfer to inner ear cells.

Nature communications·2019

Same author

The effect of the overexpression of miR-490 on the biological function of prostate cancer cell PC-3.

Journal of B.U.ON. : official journal of the Balkan Union of Oncology·2019

Same author

Treatment response and outcome of children with T-cell acute lymphoblastic leukemia expressing the gamma-delta T-cell receptor.

Oncoimmunology·2019

Same author

Hypertension is a modifiable risk factor for osteonecrosis in acute lymphoblastic leukemia.

Blood·2019

Same author

Patients' experiences of coping with multiple chronic conditions: A qualitative descriptive study.

Journal of clinical nursing·2019

Search research articles

Related Experiment Video

Updated: Sep 1, 2025

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

EEG-Based Emotion Recognition Using Spatial-Temporal Graph Convolutional LSTM With Attention Mechanism.

Lin Feng, Cheng Cheng, Mingyan Zhao

IEEE Journal of Biomedical and Health Informatics

|August 15, 2022

Summary

This summary is machine-generated.

This study introduces ST-GCLSTM, a novel hybrid model for electroencephalogram (EEG) based emotion recognition. It effectively utilizes brain region topology and spatial-temporal features for improved accuracy.

More Related Videos

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

Exploring the Use of Isolated Expressions and Film Clips to Evaluate Emotion Recognition by People with Traumatic Brain Injury

Exploring the Use of Isolated Expressions and Film Clips to Evaluate Emotion Recognition by People with Traumatic Brain Injury

Published on: May 15, 2016

Related Experiment Videos

Last Updated: Sep 1, 2025

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention

Published on: December 15, 2023

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

Exploring the Use of Isolated Expressions and Film Clips to Evaluate Emotion Recognition by People with Traumatic Brain Injury

Exploring the Use of Isolated Expressions and Film Clips to Evaluate Emotion Recognition by People with Traumatic Brain Injury

Published on: May 15, 2016

Area of Science:

Neuroscience
Artificial Intelligence
Biomedical Engineering

Background:

Emotion recognition from electroencephalogram (EEG) signals is challenged by the dynamic relationships between brain regions.
Existing deep learning models often underutilize the vital biological topological information inherent in multi-channel EEG data.
Extracting time-varying spatial and temporal characteristics from EEG remains a significant hurdle.

Purpose of the Study:

To develop an advanced hybrid model, ST-GCLSTM, for enhanced EEG-based emotion recognition.
To effectively integrate biological topological information with spatial-temporal features from EEG signals.
To improve the accuracy and robustness of emotion recognition systems.

Main Methods:

A hybrid model, ST-GCLSTM, combining a spatial-graph convolutional network (SGCN) and an attention-enhanced bi-directional Long Short-Term Memory (LSTM) module was designed.
Two-layer SGCN with adjacency matrices was employed to learn intrinsic connections among EEG channels.
An attention mechanism within the bi-directional LSTM module was used to extract crucial spatial-temporal features.

Main Results:

The ST-GCLSTM model demonstrated superior performance in extracting representative spatial-temporal features by considering brain region topology.
Extensive experiments on DEAP, SEED, and SEED-IV datasets confirmed the model's effectiveness.
The proposed model achieved absolute performance improvements over existing state-of-the-art methods.

Conclusions:

The ST-GCLSTM model successfully leverages biological topological information for more effective EEG-based emotion recognition.
The integration of SGCN and attention-enhanced bi-LSTM modules offers a promising approach for capturing complex spatial-temporal dynamics in EEG.
The findings highlight the importance of incorporating neuro-anatomical constraints into deep learning models for brain-computer interfaces.