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Analyzing Emotional Oscillatory Brain Network for Valence and Arousal-Based Emotion Recognition Using EEG Data

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  • Jianzhuo Yan

    (Faculty of Information Technology, Beijing University of Technology, Beijing 100124, P. R. China†The Ministry of Education P. R. C Engineering Research, Center of Digital Community, Beijing 100022, P. R. China‡Beijing Advanced Innovation Center for Future, Internet Technology, Beijing University of Technology, Beijing 100124, P. R. China)

  • Hongzhi Kuai

    (Faculty of Information Technology, Beijing University of Technology, Beijing 100124, P. R. China†The Ministry of Education P. R. C Engineering Research, Center of Digital Community, Beijing 100022, P. R. China‡Beijing Advanced Innovation Center for Future, Internet Technology, Beijing University of Technology, Beijing 100124, P. R. China)

  • Jianhui Chen

    (Faculty of Information Technology, Beijing University of Technology, Beijing 100124, P. R. China‡Beijing Advanced Innovation Center for Future, Internet Technology, Beijing University of Technology, Beijing 100124, P. R. China§Beijing International Collaboration Base on Brain, Informatics and Wisdom Services, Beijing 100124, P. R. China¶International WIC Institute, Beijing University of Technology, Beijing 100124, P. R. China∥Beijing Key Laboratory of MRI and Brain Informatics, Beijing 100124, P. R. China)

  • Ning Zhong

    (#x2021;Beijing Advanced Innovation Center for Future, Internet Technology, Beijing University of Technology, Beijing 100124, P. R. China§Beijing International Collaboration Base on Brain, Informatics and Wisdom Services, Beijing 100124, P. R. China¶International WIC Institute, Beijing University of Technology, Beijing 100124, P. R. China∥Beijing Key Laboratory of MRI and Brain Informatics, Beijing 100124, P. R. China**Knowledge Information Systems Lab, Department of Life Science and Informatics, Maebashi Institute of Technology, Maebashi 371-0816, Japan)

Abstract

Emotion recognition is a highly noteworthy and challenging work in both cognitive science and affective computing. Currently, neurobiology studies have revealed the partially synchronous oscillating phenomenon within brain, which needs to be analyzed from oscillatory synchronization. This combination of oscillations and synchronism is worthy of further exploration to achieve inspiring learning of the emotion recognition models. In this paper, we propose a novel approach of valence and arousal-based emotion recognition using EEG data. First, we construct the emotional oscillatory brain network (EOBN) inspired by the partially synchronous oscillating phenomenon for emotional valence and arousal. And then, a coefficient of variation and Welch’s t-test based feature selection method is used to identify the core pattern (cEOBN) within EOBN for different emotional dimensions. Finally, an emotional recognition model (ERM) is built by combining cEOBN-inspired information obtained in the above process and different classifiers. The proposed approach can combine oscillation and synchronization characteristics of multi-channel EEG signals for recognizing different emotional states under the valence and arousal dimensions. The cEOBN-based inspired information can effectively reduce the dimensionality of the data. The experimental results show that the previous method can be used to detect affective state at a reasonable level of accuracy.

Suggested Citation

  • Jianzhuo Yan & Hongzhi Kuai & Jianhui Chen & Ning Zhong, 2019. "Analyzing Emotional Oscillatory Brain Network for Valence and Arousal-Based Emotion Recognition Using EEG Data," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 18(04), pages 1359-1378, July.
    • Handle: RePEc:wsi:ijitdm:v:18:y:2019:i:04:n:s0219622019500238
    DOI: 10.1142/S0219622019500238
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    References listed on IDEAS

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    1. Jonah Lehrer, 2009. "Neuroscience: Making connections," Nature, Nature, vol. 457(7229), pages 524-527, January.
    2. You-Yun Lee & Shulan Hsieh, 2014. "Classifying Different Emotional States by Means of EEG-Based Functional Connectivity Patterns," PLOS ONE, Public Library of Science, vol. 9(4), pages 1-13, April.
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