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Visualizing Emotional States: A Method Based on Human Brain Activity

  • Yanfang Long
  • Wanzeng KongEmail author
  • Xuanyu Jin
  • Jili Shang
  • Can Yang
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1072)

Abstract

Recently, how to infer one’s emotional state by nonverbal components has attracted great attention from the scientific community. If we can decode and visualize the emotional states from human brain activity, what an amazing thing that would be? The research in this paper found a way to decode and visualize different emotional states from human brain activity. In our experiments, at first, the power spectral density (PSD) was extracted from EEG signals evoked by visual stimulation of different emotional facial images. PSD can be viewed as a clue containing specific emotional states in human brain. After that, we use the conditional variational auto-encoder (VAE) to decode and visualize the emotional state, which takes the extracted PSD feature as input and generates the corresponding image. Specifically, VAE is a framework consisting of an encoder and a decoder. The former is used to learn low-dimension potential features of specific emotional state from the input PSD, and the later outputs an image containing the corresponding emotional state. Finally, our method was trained and tested on the EEG data from six subjects while they were looking at images from the Chinese Facial Affective Picture System (CFAPS) and obtained some promising results.

Keywords

Human brain activity Emotional states PSD VAE 

Notes

Acknowledgement

This work was supported by National Key R&D Program of China for Intergovernmental International Science and Technology Innovation Cooperation Project (2017YFE0116800), National Natural Science Foundation of China (61671193), Key Science and Technology Program of Zhejiang Province (2018C04012), Science and technology platform construction project of Fujian science and Technology Department (2015Y2001).

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Yanfang Long
    • 1
  • Wanzeng Kong
    • 1
    • 2
    Email author
  • Xuanyu Jin
    • 1
  • Jili Shang
    • 1
  • Can Yang
    • 3
  1. 1.Hangzhou Dianzi UniversityHangzhouChina
  2. 2.Fujian Key Laboratory of Rehabilitation TechnologyFuzhouChina
  3. 3.Hong Kong University of Science and TechnologyKowloonHong Kong, China

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