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Related Experiment Video

Updated: May 28, 2026

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

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Bi-Hemispheric Adversarial Domain Adaptation Neural Network for EEG-Based Emotion Recognition.

Yuqi Chen1, Ming Meng2

  • 1School of Hangzhou Dianzi University ITMO Joint Institute, Hangzhou Dianzi University, Hangzhou 310027, China.

Brain Sciences
|May 27, 2026
PubMed
Summary

Bi-Hemispheric Adversarial Domain Adaptation (BiHADA) improves electroencephalogram (EEG) emotion recognition by aligning data across domains and considering brain hemisphere differences. This method enhances accuracy in cross-subject and cross-session tasks.

Keywords:
EEG signaladversarial domain adaptationclass-informed discriminatoremotion recognition

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Area of Science:

  • Neuroscience
  • Machine Learning
  • Signal Processing

Background:

  • Electroencephalogram (EEG)-based emotion recognition faces challenges due to individual differences and signal non-stationarity.
  • Existing adversarial domain adaptation methods often overlook the multimodal structure of EEG data and hemispheric asymmetries.
  • Global distribution alignment using binary discriminators may not fully capture complex emotional EEG characteristics.

Purpose of the Study:

  • To propose a Bi-Hemispheric Adversarial Domain Adaptation Neural Network (BiHADA) for enhanced EEG-based emotion recognition.
  • To address limitations of existing methods by incorporating label structure and modeling hemispheric differences.
  • To improve the transferability and discriminability of EEG emotional features across domains.

Main Methods:

  • Developed a multimodal discriminator integrating source-domain label structure for improved domain alignment.
  • Implemented dual adversarial domain adaptation branches to separately model left and right hemispheric processing.
  • Introduced discriminator-derived perplexity for adaptive weighting of hemisphere classifiers based on alignment quality.

Main Results:

  • BiHADA achieved 86.82% accuracy in cross-subject tasks and 92.71% in cross-session tasks on the SEED dataset.
  • Demonstrated significant improvements in the transferability and discriminability of EEG emotional features.
  • The method effectively handles different domain adaptation scenarios in EEG emotion recognition.

Conclusions:

  • BiHADA enhances EEG emotion recognition by combining class-structure-guided domain alignment and hemispheric asymmetry modeling.
  • Incorporating source-domain label structure and hemisphere-specific adaptation boosts cross-domain performance.
  • The proposed approach offers a more robust framework for understanding and recognizing emotions from EEG signals.