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Related Experiment Videos

Path Adversarial Dual-Branch Network for EEG Emotion Recognition.

Yuqing Cai1, Yicheng Qian1, Wei Zheng1

  • 1Ocean College, Jiangsu University of Science and Technology, Zhenjiang 212100, China.

Sensors (Basel, Switzerland)
|July 15, 2026
PubMed
Summary

This study introduces a novel Path Adversarial Dual-Branch Network (PADB-Net) for improved electroencephalogram (EEG)-based emotion recognition. The PADB-Net effectively addresses domain shift and enhances feature fusion, achieving superior performance in classifying emotions.

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Area of Science:

  • Neuroscience
  • Machine Learning
  • Biomedical Engineering

Background:

  • Electroencephalogram (EEG)-based emotion recognition faces challenges with cross-subject domain shift.
  • Insufficient complementary fusion of time-frequency information hinders accurate emotion classification.
  • Existing models struggle with aligning feature distributions across different domains and modalities.

Purpose of the Study:

  • To propose a novel multi-task adversarial network, the Path Adversarial Dual-Branch Network (PADB-Net), for robust EEG-based emotion recognition.
  • To address cross-subject domain shift and improve the fusion of time-frequency information in EEG signals.
  • To align feature distributions across time and frequency domains and between source and target domains.

Main Methods:

Keywords:
dual-adversarial mechanismelectroencephalography (EEG)emotion recognitionmulti-task adversarial networkpath adversarialtime-frequency fusion

Related Experiment Videos

  • A dual-branch parallel architecture processes raw EEG waveforms (time domain) and differential entropy features (frequency domain).
  • Lightweight depthwise separable convolutions and channel attention are employed for discriminative feature extraction.
  • A path adversarial module and a domain adversarial module are introduced to align feature distributions within a unified framework.

    • Main Results:

    • PADB-Net significantly outperforms single-adversarial and non-adversarial baselines in accuracy, AUC, F1-score, sensitivity, and specificity.
    • On the HybridBCI dataset, PADB-Net achieved 77.80% accuracy, 84.50% AUC, and 79.40% F1-score with minimal parameters.
    • The model demonstrated strong cross-dataset generalizability on the SEED dataset, achieving high F1-scores for negative, neutral, and positive emotions.

    Conclusions:

    • The proposed PADB-Net effectively mitigates cross-subject domain shift and enhances time-frequency information fusion in EEG emotion recognition.
    • The synergistic gain of the dual-adversarial mechanism is verified, leading to significant performance improvements.
    • PADB-Net shows promising results and strong generalizability for real-world emotion recognition applications.