• Title/Summary/Keyword: Desired Emotion Matching Score

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Study on Data Normalization and Representation for Quantitative Analysis of EEG Signals (뇌파 신호의 정량적 분석을 위한 데이터 정규화 및 표현기법 연구)

  • Hwang, Taehun;Kim, Jin Heon
    • Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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    • v.9 no.6
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    • pp.729-738
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    • 2019
  • Recently, we aim to improve the quality of virtual reality contents based on quantitative analysis results of emotions through combination of emotional recognition field and virtual reality field. Emotions are analyzed based on the participant's vital signs. Much research has been done in terms of signal analysis, but the methodology for quantifying emotions has not been fully discussed. In this paper, we propose a normalization function design and expression method to quantify the emotion between various bio - signals. Use the Brute force algorithm to find the optimal parameters of the normalization function and improve the confidence score of the parameters found using the true and false scores defined in this paper. As a result, it is possible to automate the parameter determination of the bio-signal normalization function depending on the experience, and the emotion can be analyzed quantitatively based on this.