Human Emotion Variation Analysis Based on EEG Signal and POMS Scale

  • Youjun Li
  • Haiyan Zhou
  • Jianhui Chen
  • Jiajin Huang
  • Meng Chen
  • Yan Liu
  • Ning Zhong
Conference paper
First Online:
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9919)

Abstract

Emotion is considered as a critical aspect of human brain behavior. In this paper, we investigate human normal emotion variation for a long period without stimuli. Eight subjects participated in the experiment for seven days. The EEG signal and POMS scale of the subjects were collected in the experiment. After data collection and preprocessing, Pearson correlation analysis and multiple linear regression analysis were carried out between EEG features and POMS emotion components. The results of Pearson correlation analysis show that the correlation coefficient of EEG features and POMS emotion component range from 0.367 to 0.610 at 0.01 significant levels. Based on this, multiple linear regression models are built between POMS emotion components and EEG features. With these models, the POMS scales of the subjects can be predicted such that the R2 between the prediction scale and real scale ranges from 0.329 to 0.772; the emotion of ‘Depression-Dejection’ has the lowest R2 (0.329); and the ‘Negative Emotion’ has the highest R2 (0.772).

Keywords

Multiple Linear Regression Analysis Data Collection System Emotion Component Spectral Entropy Total Mood Disturbance 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Notes

Acknowledgement

This work was partly supported by the National Basic Research Program of China under grant no. 2014CB744600, by the International Science & Technology Cooperation Program of China under grant no. 2013DFA32180, by the National Natural Science Foundation of China grant no. 61272345, by the Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, and by the Beijing Municipal Commission of Education.

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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Youjun Li
    • 1
    • 2
    • 3
    • 4
  • Haiyan Zhou
    • 1
    • 2
    • 3
    • 4
  • Jianhui Chen
    • 1
    • 2
    • 3
    • 4
  • Jiajin Huang
    • 1
    • 2
    • 3
    • 4
  • Meng Chen
    • 2
  • Yan Liu
    • 2
  • Ning Zhong
    • 1
    • 2
    • 3
    • 4
    • 5
  1. 1.Beijing Advanced Innovation Center for Future Internet TechnologyBeijingChina
  2. 2.International WIC InstituteBeijing University of TechnologyBeijingChina
  3. 3.Beijing International Collaboration Base on Brain Informatics and Wisdom ServicesBeijingChina
  4. 4.Beijing Key Laboratory of MRI and Brain InformaticsBeijingChina
  5. 5.Department of Life Science and InformaticsMaebashi Institute of TechnologyMaebashiJapan

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