Construction of an Emotion Estimation Model Using EEG and Heart Rate Variability Indices as Features by Machine Learning

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Other Title
  • 機械学習による脳波指標と心拍変動指標を特徴量とした感情推定モデルの構築

Description

<p>Several human emotion estimation which apply machine learning have been under studying. These studies are expected to be applied to healthcare and medical diagnosis. Recently, EEG and heart rate variability indices were used for constructing emotion estimation model. The model accuracy was 0.80 in classifying the four emotions of joy, anger, sorrow, and pleasure. However, for applications in health care and medical diagnosis, the model with an accuracy of 0.80 may not be sufficient. Therefore, for the purpose of improving accuracy, this study extracted and select feature of EEG and heart rate variability indices. Then, the emotion estimation model was constructed by deep learning. As a result, the accuracy was 0.98 in this study while it was 0.50 in the features used by the previous study. Therefore, it was confirmed that the accuracy was improved.</p>

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Details 詳細情報について

  • CRID
    1390851320457540736
  • NII Article ID
    130008051866
  • DOI
    10.11517/pjsai.jsai2021.0_3f2gs10j03
  • ISSN
    27587347
  • Text Lang
    ja
  • Data Source
    • JaLC
    • CiNii Articles
  • Abstract License Flag
    Disallowed

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