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Attention-Based Transfer Enhancement Network for Cross-Corpus EEG Emotion Recognition.

Zongni Li1,2, Kin-Yeung Wong1, Chan-Tong Lam1

  • 1Faculty of Applied Sciences, Macao Polytechnic University, Macao 999078, China.

Sensors (Basel, Switzerland)
|September 27, 2025
PubMed
Summary

This study introduces the Cross-corpus Attention-based Transfer Enhancement network (CATE) to improve EEG emotion recognition across datasets. CATE enhances model generalization by learning robust, domain-invariant features through a novel dual-view pre-training strategy.

Keywords:
cross-corpusdomain adaptationemotion recognitionself-supervised learning

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Area of Science:

  • Neuroscience
  • Machine Learning
  • Signal Processing

Background:

  • EEG-based emotion recognition faces poor cross-dataset generalization due to domain shifts.
  • Traditional methods struggle with overfitting or bridging large dataset discrepancies.
  • Developing robust models for cross-corpus EEG emotion recognition is crucial for practical applications.

Purpose of the Study:

  • To propose a novel framework, the Cross-corpus Attention-based Transfer Enhancement network (CATE), to address the generalization challenge in EEG emotion recognition.
  • To develop a two-stage framework with a dual-view self-supervised pre-training strategy for learning domain-invariant representations.
  • To significantly improve the accuracy and robustness of cross-corpus EEG emotion recognition.

Main Methods:

  • Introduced a two-stage framework: CATE.
  • Employed a dual-view self-supervised pre-training strategy: Noise-Enhanced Representation Modeling (NERM) and Wavelet Transform Representation Modeling (WTRM).
  • Utilized attention-based mechanisms in the supervised fine-tuning stage for classification.

Main Results:

  • CATE achieved state-of-the-art performance on six transfer tasks across SEED, SEED-IV, and SEED-V datasets.
  • Reported accuracies ranging from 68.01% to 81.65%.
  • Outperformed prior methods by up to 15.65 percentage points, demonstrating superior generalization capabilities.

Conclusions:

  • The proposed CATE framework effectively learns transferable features from distinct, synergistic views.
  • CATE significantly advances the practical applicability of cross-corpus EEG emotion recognition.
  • The dual-view pre-training strategy enhances model resilience to domain shifts and improves recognition accuracy.