EEG emotion recognition using improved graph neural network with channel selection.

Journal: Computer methods and programs in biomedicine
Published Date: Feb 1, 2023

Abstract

BACKGROUND AND OBJECTIVE: Emotion classification tasks based on electroencephalography (EEG) are an essential part of artificial intelligence, with promising applications in healthcare areas such as autism research and emotion detection in pregnant women. However, the complex data acquisition environment provides a variable number of EEG channels, which interferes with the model to simulate the process of information transfer in the human brain. Therefore, this paper proposes an improved graph convolution model with dynamic channel selection.

Authors

  • Xuefen Lin
    School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, PR China.
  • Jielin Chen
    School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, PR China. Electronic address: chenjielin98@qq.com.
  • Weifeng Ma
    School of Information and Electronic Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, PR China.
  • Wei Tang
    Hepato-Biliary-Pancreatic Surgery Division, Department of Surgery, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan.
  • Yuchen Wang
    College of Management, University of Massachusetts Boston, Boston, MA, USA.

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