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A knowledge-driven self-supervised learning method for enhancing EEG-based emotion recognition.

Hanqi Wang1, Jingyu Zhang1, Peng Ye2

  • 1College of Intelligent Robotics and Advanced Manufacturing, Fudan University, Shanghai, 200433, China.

Neural Networks : the Official Journal of the International Neural Network Society
|February 10, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a new self-supervised learning framework for emotion recognition using electroencephalography (EEG) brain-computer interfaces (BCIs). The method enhances emotion recognition by preserving key data and reducing variability between individuals.

Keywords:
EEG emotion recognitionKnowledge-drivenSelf-supervised learning

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Area of Science:

  • Neuroscience
  • Computer Science
  • Artificial Intelligence

Background:

  • Emotion recognition using electroencephalography (EEG) brain-computer interfaces (BCIs) is vital for human-computer interaction, medicine, and neuroscience.
  • Limited labeled EEG data hinders progress, making self-supervised learning a promising alternative.
  • Existing self-supervised methods struggle with preserving emotion-related information and overcoming inter-subject variability.

Purpose of the Study:

  • To propose a novel knowledge-driven self-supervised learning framework for EEG-based emotion recognition.
  • To address the challenges of preserving emotion-related information and reducing inter-subject variability.
  • To improve the performance and generalizability of EEG emotion recognition systems.

Main Methods:

  • A knowledge-driven self-supervised learning framework incorporating domain knowledge to approximate differential entropy (DE) extraction.
  • A two-component cascaded framework: multi-branch convolutional differential entropy learning (MCDEL) and contrastive entropy alignment (CEA).
  • MCDEL simulates DE extraction to preserve emotion-related and generalizable information; CEA exposes complex emotional semantics in high-dimensional space.

Main Results:

  • The proposed method achieved superior performance compared to existing self-supervised approaches.
  • Subject-independent mean accuracy reached 84.48% ± 5.79% on SEED and 67.64% ± 6.35% (Arousal) and 68.63% ± 7.77% (Valence) on DREAMER.
  • Ablation studies confirmed the contribution of each component, and t-SNE visualization demonstrated reduced inter-subject variability and improved emotion discrimination.

Conclusions:

  • The novel knowledge-driven self-supervised framework effectively addresses key challenges in EEG emotion recognition.
  • The method demonstrates significant improvements in accuracy and generalizability, outperforming existing self-supervised techniques.
  • The framework shows promise for advancing human-computer interaction, medicine, and neuroscience through more robust emotion recognition.