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Related Experiment Videos

A Supervised Contrastive Variational Autoencoder with Probabilistic Latent Alignment for Cross-Domain EEG Emotion

Linna Wu1, Yong Yang2, Wenhao Wang1

  • 1School of Electronic Information and Electrical Engineering, Chengdu University, Chengdu 610106, China.

Sensors (Basel, Switzerland)
|May 27, 2026
PubMed
Summary

Related Concept Videos

Labeling Emotion01:20

Labeling Emotion

Emotional labeling is a cognitive process that involves identifying and naming one's emotions, such as anger, fear, happiness, or sadness. It allows individuals to recognize and express their internal emotional states, a critical aspect of emotional regulation and communication. Labeling emotions requires more than mere recognition; it also involves drawing upon memory and contextual cues to understand the current situation and apply a corresponding emotional label. For instance, feeling...

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This summary is machine-generated.

This study introduces a novel deep learning approach, the Supervised Contrastive Variational AutoEncoder Network (SCVAE-Net), for more accurate cross-domain emotion recognition using electroencephalogram (EEG) signals. The method effectively reduces distribution differences, enhancing feature consistency across diverse datasets.

Area of Science:

  • Neuroscience
  • Artificial Intelligence
  • Machine Learning

Background:

  • Cross-domain emotion recognition using electroencephalogram (EEG) is hindered by significant signal distribution differences across subjects and time.
  • Developing deep learning models that can learn common feature spaces and minimize domain discrepancies is crucial for improving performance.

Purpose of the Study:

  • To propose a novel deep learning framework, the Supervised Contrastive Variational AutoEncoder Network (SCVAE-Net), for enhanced cross-domain EEG emotion recognition.
  • To improve the extraction of consistent and transferable features across different EEG data domains.

Main Methods:

  • Utilizing the reconstruction and latent space probabilization of Variational Autoencoders (VAE) to generate consistent intermediate features.
  • Employing Maximum Mean Discrepancy (MMD) loss to reduce feature distribution discrepancies between domains.
Keywords:
EEG-based emotion recognitiondomain adaptionmulti-view supervised contrastive learningvariational autoencoder

Related Experiment Videos

  • Incorporating multi-view supervised contrastive learning to enhance intra-class consistency and inter-class separability in latent feature spaces.

    • Main Results:

    • SCVAE-Net achieved high accuracies in cross-subject settings (95.01% on SEED, 74.94% on SEED-IV).
    • The model also demonstrated strong performance in cross-session settings (96.84% on SEED, 79.44% on SEED-IV).
    • Experimental results validate the effectiveness of SCVAE-Net in addressing cross-domain challenges in EEG emotion recognition.

    Conclusions:

    • The proposed SCVAE-Net effectively extracts consistent features across domains, significantly improving cross-domain EEG emotion recognition.
    • The combination of VAE, MMD loss, and supervised contrastive learning offers a robust solution for domain adaptation in EEG analysis.
    • The method shows promising potential for real-world applications requiring reliable emotion recognition from diverse EEG data.