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Related Experiment Video

Updated: May 16, 2025

Exploring the Use of Isolated Expressions and Film Clips to Evaluate Emotion Recognition by People with Traumatic Brain Injury
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HBUED: An EEG dataset for emotion recognition.

Shuaiqi Liu1, Xinrui Wang1, Yanling An2

  • 1College of Electronic and Information Engineering, Hebei University, Machine Vision Technology Innovation Center of Hebei Province, Baoding 071002, China.

Journal of Affective Disorders
|May 14, 2025
PubMed
Summary

Researchers developed a large-scale electroencephalogram (EEG) dataset for emotion recognition and a deep learning method to improve accuracy. This approach efficiently handles complex data, enhancing human-computer interaction. The dataset and code are publicly available.

Keywords:
Deep learningEEG datasetsEmotion recognitionParallel feature extraction

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Area of Science:

  • Neuroscience
  • Computer Science
  • Artificial Intelligence

Background:

  • Emotion recognition using electroencephalogram (EEG) data is vital for advancing human-computer interaction.
  • Current EEG datasets often lack sufficient subject data for robust model training and validation.
  • This limitation hinders the development of effective emotion recognition systems.

Purpose of the Study:

  • Introduce the Hebei University Emotional EEG Dataset (HBUED), a large-scale dataset designed for emotion recognition research.
  • Present a novel deep learning methodology to enhance EEG-based emotion recognition performance.
  • Address the challenge of handling complex EEG samples in emotion recognition models.

Main Methods:

  • Developed a dual-input network architecture for extracting discriminative EEG signal features from multiple perspectives.
  • Implemented a parallel feature extraction module to expand network width, capturing comprehensive features while preventing overfitting.
  • Incorporated a topological feature extraction module to better analyze the topological characteristics of EEG signals.

Main Results:

  • The proposed deep learning method demonstrated effectiveness in emotion recognition tasks.
  • Validation was performed on both the newly created HBUED dataset and the public DEAP dataset.
  • Experimental results confirmed the superior performance of the proposed methodology.

Conclusions:

  • The HBUED dataset provides a valuable resource for the emotion recognition research community.
  • The novel deep learning approach offers an effective solution for improving EEG-based emotion recognition.
  • Public availability of the HBUED dataset and source code facilitates further research and development.