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EEG-Based Emotion Recognition via Knowledge-Integrated Interpretable Method

Author

Listed:
  • Ying Zhang

    (College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China
    These authors contributed equally to this work.)

  • Chen Cui

    (College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China
    These authors contributed equally to this work.)

  • Shenghua Zhong

    (College of Computer Science and Software Engineering, Shenzhen University, Shenzhen 518060, China)

Abstract

Despite achieving success in many domains, deep learning models remain mostly black boxes, especially in electroencephalogram (EEG)-related tasks. Meanwhile, understanding the reasons behind model predictions is quite crucial in assessing trust and performance promotion in EEG-related tasks. In this work, we explore the use of representative interpretable models to analyze the learning behavior of convolutional neural networks (CNN) in EEG-based emotion recognition. According to the interpretable analysis, we find that similar features captured by our model and state-of-the-art model are consistent with previous brain science findings. Next, we propose a new model by integrating brain science knowledge with the interpretability analysis results in the learning process. Our knowledge-integrated model achieves better recognition accuracy on standard EEG-based recognition datasets.

Suggested Citation

  • Ying Zhang & Chen Cui & Shenghua Zhong, 2023. "EEG-Based Emotion Recognition via Knowledge-Integrated Interpretable Method," Mathematics, MDPI, vol. 11(6), pages 1-18, March.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:6:p:1424-:d:1098153
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