Preference Classification Method Using EEG Analysis Based on Gray Theory and Personality Analysis

• Ito, Shin-ichi

• Ito, Momoyo

• Sato, Katsuya

• Fujisawa, Shoichiro

• Fukumi, Minoru

抄録

This paper introduces a method to classify the preference patterns of sounds on the basis of an electroencephalogram (EEG) analysis and a personality analysis. We analyze the EEG of the left prefrontal cortex by single-point sensing. For EEG recording, a dry-type sensor and few electrodes were used. The proposed feature extraction method employs gray relational grade detection on the frequency bands of EEG and egogram. The gray relational grade is used for extracting the EEG feature. The egogram is extracted for quantifying the subject’s personality. The preference patterns generated when the subject is hearing a sound are classified using the nearest neighbor method. To show the effectiveness of the proposed method, we conduct experiments using real EEG data. These results show that the accuracy rate of the preference classification using the proposed method is better than that using the method that does not to consider the subject’s personality.

収録刊行物

• The Online Journal on Computer Science and Information Technology

  The Online Journal on Computer Science and Information Technology 4 (3), 276-280, 2014-07

  International Foundation for Modern Education and Scientific Research

キーワード

• electroencephalogram
• individual difference
• gray relational grade
• nearest neighbor method
• personality
• egogram
• preference

詳細情報

• CRID: 1050564287703212160
• NII論文ID: 120006532443
• ISSN: 20904517
• Web Site: https://repo.lib.tokushima-u.ac.jp/112402
• 本文言語コード: en
• 資料種別: journal article
• データソース種別:
  • IRDB
  • CiNii Articles