ResNet-50 based technique for EEG image characterization due to varying environmental stimuli.

Journal: Computer methods and programs in biomedicine
Published Date: Aug 28, 2022

Abstract

BACKGROUND AND OBJECTIVE: Emotion is an important factor affecting a person's physical and mental health, but there are few ways to detect a patient's emotions in daily life. Negative emotions not only affect recovery after treatment, but also cause poor health. Current emotion classification research based on EEG image recognition is highly accurate, making the development of an emotion detector feasible. Using emotion data from the SEED, this study trained a detection model using the residual neural network ResNet-50 with a SAM and SE-block double attention mechanism, and used quantitative parameters based on the Russell emotion cycle model to construct a human-computer interactive health detector for emotion recognition in EEG images induced by environmental stimuli.

Authors

  • Tingyi Tian
    School of Art and Design, Shanghai University of Engineering Science, Shanghai, 201620, PR. China. Electronic address: tty0622@163.com.
  • Le Wang
    Brainnetome Center & National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China; CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
  • Man Luo
    School of Art and Design, Shanghai University of Engineering Science, Shanghai, 201620, PR. China.
  • Yiping Sun
    Shanghai Institute of Science & Technology Management, Shanghai, 201815, PR. China.
  • Xiaoyan Liu
    College of Information Technology, Jilin Agricultural University, Changchun, China.

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