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ID 112402
著者
藤澤, 正一郎 The University of Tokushima KAKEN研究者をさがす
キーワード
electroencephalogram
individual difference
gray relational grade
nearest neighbor method
personality
egogram
preference
資料タイプ
学術雑誌論文
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.
掲載誌名
The Online Journal on Computer Science and Information Technology
ISSN
20904517
出版者
International Foundation for Modern Education and Scientific Research
4
3
開始ページ
276
終了ページ
280
発行日
2014-07
権利情報
This work is licenced under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.(CC BY-NC-ND)
EDB ID
フルテキストファイル
言語
eng
著者版フラグ
出版社版
部局
理工学系