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

RETRACTED: Yuan et al. Green Remanufacturer's Mixed Collection Channel Strategy Considering Enterprise's Environmental Responsibility and the Fairness Concern in Reverse Green Supply Chain. <i>Int. J. Environ. Res. Public Health</i><b>2021</b>, <i>18</i>, 3405.

International journal of environmental research and public health·2025
Same author

Cross-modal credibility modelling for EEG-based multimodal emotion recognition.

Journal of neural engineering·2024
Same author

Logistics Service Selection Strategy of Green Manufacturers in Green Low-Carbon Supply Chain.

International journal of environmental research and public health·2023
Same author

Optimal Decisions in Green, Low-Carbon Supply Chain Considering the Competition and Cooperation Relationships between Different Types of Manufacturers.

International journal of environmental research and public health·2022
Same author

Effects of a Mixed Emissions Control Policy on the Manufacturer's Production and Carbon Abatement Investment Decisions.

International journal of environmental research and public health·2022
Same author

Effects of Government Subsidies on Production and Emissions Reduction Decisions under Carbon Tax Regulation and Consumer Low-Carbon Awareness.

International journal of environmental research and public health·2021

Related Experiment Video

Updated: Jul 20, 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

3.8K

AutoEER: automatic EEG-based emotion recognition with neural architecture search.

Yixiao Wu1, Huan Liu1,2, Dalin Zhang3

  • 1School of Computer Science and Technology, Xi'an Jiaotong University, Xi'an, People's Republic of China.

Journal of Neural Engineering
|August 3, 2023
PubMed
Summary
This summary is machine-generated.

AutoEER automates deep learning model design for electroencephalography (EEG) emotion recognition. This framework significantly improves accuracy and reduces manual effort, advancing EEG analysis.

Keywords:
electroencephalogram (EEG)emotion recognitionneural architecture search (NAS)search space

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

13.3K
Brain Imaging Investigation of the Neural Correlates of Emotion Regulation
14:04

Brain Imaging Investigation of the Neural Correlates of Emotion Regulation

Published on: August 26, 2011

12.6K

Related Experiment Videos

Last Updated: Jul 20, 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

3.8K
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

13.3K
Brain Imaging Investigation of the Neural Correlates of Emotion Regulation
14:04

Brain Imaging Investigation of the Neural Correlates of Emotion Regulation

Published on: August 26, 2011

12.6K

Area of Science:

  • Neuroscience
  • Artificial Intelligence
  • Machine Learning

Background:

  • Emotion recognition using electroencephalography (EEG) is increasingly important for applications with portable devices.
  • Deep learning models excel at EEG emotion recognition but require time-consuming manual design and customization.
  • Neural architecture search (NAS) offers automated solutions for optimizing deep networks.

Purpose of the Study:

  • To introduce AutoEER, a framework using tailored NAS for automatic optimal network structure discovery in EEG-based emotion recognition.
  • To design a specialized search space capturing temporal and spatial EEG properties.
  • To develop a novel parameterization strategy for deriving optimal network structures.

Main Methods:

  • Proposed AutoEER framework leveraging NAS for EEG emotion recognition.
  • Developed a customized search space incorporating operators for temporal and spatial EEG features.
  • Implemented a novel parameterization strategy for network structure optimization.

Main Results:

  • AutoEER outperformed state-of-the-art manual and NAS models on DEAP and SEED datasets.
  • Achieved a 0.93% average accuracy improvement over WangNAS.
  • Achieved a 4.51% average F1 score improvement over LiNAS.
  • Generated architectures demonstrated superior transferability.

Conclusions:

  • AutoEER offers a novel, automated approach to EEG-based emotion recognition model design.
  • The specialized search space and parameterization strategy yield high-performing, transferable models.
  • AutoEER significantly reduces manual labor and time costs in EEG research, promising to advance the field.