Convolutional Neural Network for Emotional EEG Decoding and Visualization

Jiaying Lin, Lu Li, Abdelkader Nasreddine Belkacem, Jun Liang, Chao Chen

Research output: Chapter in Book/Report/Conference proceedingConference contribution


In recent years, deep learning has been increasingly utilized in affective Brain-Computer Interface (aBCI) research. The application of Convolutional Neural Networks (ConvNets) for end-to-end analysis of electroencephalographic (EEG) signals has become a common approach in deep learning-based aBCI. However, limited research has been conducted on a better understanding of how to design and train ConvNets for end-to-end emotional EEG decoding. This study explores three kinds of ConvNets architectures, including shallow, middle, and deep configuration, to evaluate their design and training schemes. The findings of this paper demonstrate that, for aBCI, it is crucial to ensure an adequate sample size for model training while maintaining the stability of EEG signals. Additionally, achieving a balance between sample length and size is crucial for effective model training. Notably, EEGNet outperforms the other two models in terms of classification accuracy, indicating that an excessively shallow number of convolutional layers leads to insufficient feature extraction, while an excessively deep number of convolutional layers increases the risk of overfitting.

Original languageEnglish
Title of host publicationICCPR 2023 - Proceedings of the 2023 12th International Conference on Computing and Pattern Recognition
PublisherAssociation for Computing Machinery
Number of pages6
ISBN (Electronic)9798400707988
Publication statusPublished - Oct 27 2023
Event12th International Conference on Computing and Pattern Recognition, ICCPR 2023 - Qingdao, China
Duration: Oct 27 2023Oct 29 2023

Publication series

NameACM International Conference Proceeding Series


Conference12th International Conference on Computing and Pattern Recognition, ICCPR 2023


  • convolutional neural network
  • deep learning
  • electroencephalographic
  • emotion recognition

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition
  • Software


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