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

Metal-Free Ferromagnetism in Triangulene Two-Dimensional Frameworks.

Journal of the American Chemical Society·2026
Same author

Tailoring Electronic Properties of Precision Graphene Nanoribbons via Nanopore Engineering.

Angewandte Chemie (International ed. in English)·2026
Same author

Diketopyrrolopyrrole-based two-dimensional poly(arylene vinylene)s with high charge carrier mobility.

Nature communications·2026
Same author

Maintaining the Quality and Nutritional Integrity of Chilled <i>Cordyceps sinensis</i>: Comparative Effects and Mechanisms of Modified Atmosphere Packaging and UV-Based Interventions.

Foods (Basel, Switzerland)·2025
Same author

Simple and robust analysis of cefuroxime in human plasma and bone tissues by LC-MS/MS.

Analytical methods : advancing methods and applications·2025
Same author

Exploring Three-Dimensional Porphyrin-Based Covalent Organic Frameworks with Outstanding Solar Energy Conversion.

Journal of the American Chemical Society·2025

Related Experiment Video

Updated: Jun 14, 2025

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

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

Published on: December 15, 2023

2.6K

CSA-SA-CRTNN: A Dual-Stream Adaptive Convolutional Cyclic Hybrid Network Combining Attention Mechanisms for EEG

Ren Qian1, Xin Xiong1, Jianhua Zhou1

  • 1Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China.

Brain Sciences
|August 29, 2024
PubMed
Summary

This study introduces a novel Convolutional-Recurrent Hybrid Network (CSA-SA-CRTNN) for advanced electroencephalogram (EEG)-based emotion recognition, significantly improving accuracy and efficiency.

Keywords:
EEGadaptiveattention mechanismdual-stream modelemotion recognitionhybrid network

More Related Videos

Exploring the Use of Isolated Expressions and Film Clips to Evaluate Emotion Recognition by People with Traumatic Brain Injury
05:51

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

Published on: May 15, 2016

9.0K
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

13.1K

Related Experiment Videos

Last Updated: Jun 14, 2025

Author Spotlight: Addressing Technical and Subjective Challenges in Measuring Classroom Attention
06:37

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

Published on: December 15, 2023

2.6K
Exploring the Use of Isolated Expressions and Film Clips to Evaluate Emotion Recognition by People with Traumatic Brain Injury
05:51

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

Published on: May 15, 2016

9.0K
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

13.1K

Area of Science:

  • Neuroscience
  • Artificial Intelligence
  • Machine Learning

Background:

  • EEG-based emotion recognition faces challenges with model efficiency and information loss.
  • Existing methods often struggle to balance accuracy with computational cost.

Purpose of the Study:

  • To enhance EEG-based emotion recognition accuracy.
  • To reduce computational costs in emotion classification.
  • To fully leverage emotional information from EEG signals.

Main Methods:

  • Proposed a Convolutional-Recurrent Hybrid Network with a dual-stream adaptive approach and attention mechanism (CSA-SA-CRTNN).
  • Utilized a CSAM module for EEG channel weighting.
  • Employed adaptive dual-stream convolutional-recurrent networks (SA-CRNN, MHSA-CRNN) for local feature extraction.
  • Integrated a multi-head self-attention temporal convolutional network (MHSA-TCN) for global information capture.

Main Results:

  • Achieved high accuracy on the DEAP dataset: 99.26% (binary arousal), 99.15% (binary valence), 97.69% (ternary arousal), 98.05% (ternary valence).
  • Attained 98.63% accuracy on the SEED dataset, outperforming existing algorithms.
  • Demonstrated significantly higher model efficiency with lower resource consumption compared to other models.

Conclusions:

  • The CSA-SA-CRTNN model effectively improves EEG-based emotion recognition accuracy.
  • The proposed network offers a computationally efficient solution for emotion classification.
  • This approach maximizes the utilization of emotional information from EEG data.