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Classifying Different Emotional States by Means of EEG-Based Functional Connectivity Patterns

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  • You-Yun Lee
  • Shulan Hsieh

Abstract

This study aimed to classify different emotional states by means of EEG-based functional connectivity patterns. Forty young participants viewed film clips that evoked the following emotional states: neutral, positive, or negative. Three connectivity indices, including correlation, coherence, and phase synchronization, were used to estimate brain functional connectivity in EEG signals. Following each film clip, participants were asked to report on their subjective affect. The results indicated that the EEG-based functional connectivity change was significantly different among emotional states. Furthermore, the connectivity pattern was detected by pattern classification analysis using Quadratic Discriminant Analysis. The results indicated that the classification rate was better than chance. We conclude that estimating EEG-based functional connectivity provides a useful tool for studying the relationship between brain activity and emotional states.

Suggested Citation

  • 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.
    • Handle: RePEc:plo:pone00:0095415
    DOI: 10.1371/journal.pone.0095415
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    Cited by:

    1. 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.
    2. Hayato Maki & Sakriani Sakti & Hiroki Tanaka & Satoshi Nakamura, 2018. "Quality prediction of synthesized speech based on tensor structured EEG signals," PLOS ONE, Public Library of Science, vol. 13(6), pages 1-13, June.

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