GA-based feature selection for QoE estimation using EEG during video viewing

• Kasumi Kitao

  Osaka University

• Daichi Kominami

  Osaka University

• Masayuki Murata

  Osaka University

抄録

In recent years, Quality of Experiment (QoE) becomes an important factor for video viewing users and QoE-based video delivery control methods have been getting a lot of interests. In order to use the user's QoE for video delivery control, it is necessary to be able to measure the QoE suitable for the individual in real time. In this paper, we present a support vector machine based estimation method for the QoE of video viewing user using the user's EEG. We extracted over 400 features from the EEG measurements, but we show that the number of features does not need to be so large in the estimation. We also show that the feature selection based on the genetic algorithm(GA) can improve the accuracy of QoE estimation by an average of 6% compared to the random selection.

収録刊行物

• IEICE Proceeding Series

  IEICE Proceeding Series 63 E3-3-, 2020-12-02

  The Institute of Electronics, Information and Communication Engineers

キーワード

• QoE estimation
• EEG
• Genetic Algorithm

詳細情報

• CRID: 1390287673853664256
• NII論文ID: 230000012378
• DOI: 10.34385/proc.63.e3-3
• ISSN: 21885079
• 本文言語コード: en
• データソース種別:
  • JaLC
  • CiNii Articles
  • KAKEN
• 抄録ライセンスフラグ: 使用不可