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Related Experiment Video

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Exploring the Use of Isolated Expressions and Film Clips to Evaluate Emotion Recognition by People with Traumatic Brain Injury
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Dynamic bi-domain discriminator adversarial network for EEG emotion recognition.

Leyang Yu1, Qingshan She1, Zizhuo Wu2

  • 1School of Automation, Hangzhou Dianzi University, Hangzhou, 310018 Zhejiang China.

Cognitive Neurodynamics
|July 6, 2026
PubMed
Summary

This study introduces a novel dynamic bi-domain discriminator adversarial network (DBDAN) for unsupervised cross-domain electroencephalogram (EEG) emotion recognition. The method effectively addresses data variability, achieving competitive accuracy across datasets.

Keywords:
Domain adaptation (DA)Dual-domain discriminatorElectroencephalogram (EEG)Emotion recognition

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Area of Science:

  • Neuroscience
  • Artificial Intelligence
  • Machine Learning

Background:

  • Emotion recognition from electroencephalogram (EEG) signals has vast applications in human-computer interaction, healthcare, and intelligent systems.
  • Challenges in EEG emotion recognition include limited labeled data and significant inter-subject and intra-subject variability.
  • Domain adaptation (DA) techniques offer a promising solution for unsupervised cross-domain EEG emotion recognition.

Purpose of the Study:

  • To develop an effective unsupervised domain adaptation method for cross-domain EEG emotion recognition.
  • To address limitations of existing DA methods that overlook domain and subdomain weight differences.
  • To improve the precision and robustness of EEG emotion recognition across diverse datasets.

Main Methods:

  • Proposed a dynamic bi-domain discriminator adversarial network (DBDAN) for unsupervised cross-domain EEG emotion recognition.
  • Employed a feature extractor for low-level, domain-invariant EEG features and a multi-branch extractor for domain-specific features.
  • Utilized a dual-domain discriminator for adversarial learning across global and subdomains, incorporating dynamic factors for precise adaptation.

Main Results:

  • Achieved cross-subject accuracy rates of 89.43% on SEED, 75.09% on SEED-IV, and 63.42% on DEAP.
  • Demonstrated competitive performance compared to existing EEG transfer-learning baselines.
  • Validated the efficacy of dynamic global-subdomain alignment for cross-domain EEG emotion recognition.

Conclusions:

  • The proposed DBDAN method effectively handles unsupervised cross-domain EEG emotion recognition challenges.
  • Dynamic adjustment of adversarial learning balances coarse-grained and fine-grained adaptation for improved accuracy.
  • The findings highlight the significance of global-subdomain alignment in advancing EEG-based emotion recognition technologies.