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ID 113282
著者
藤澤, 正一郎 The University of Tokushima KAKEN研究者をさがす
キーワード
Preference
Egogram
Electroencephalogram
Individual Difference
Self-organizing Map
Pattern Classification
資料タイプ
学術雑誌論文
抄録
This paper introduces a method of preference analysis based on electroencephalogram (EEG) analysis of prefrontal cortex activity. The proposed method applies the relationship between EEG activity and the Egogram. The EEG senses a single point and records readings by means of a dry-type sensor and a small number of electrodes. The EEG analysis adapts the feature mining and the clustering on EEG patterns using a self-organizing map (SOM). EEG activity of the prefrontal cortex displays individual difference. To take the individual difference into account, we construct a feature vector for input modality of the SOM. The input vector for the SOM consists of the extracted EEG feature vector and a human character vector, which is the human character quantified through the ego analysis using psychological testing. In preprocessing, we extract the EEG feature vector by calculating the time average on each frequency band: θ, low-β, and high-β. To prove the effectiveness of the proposed method, we perform experiments using real EEG data. These results show that the accuracy rate of the EEG pattern classification is higher than it was before the improvement of the input vector.
掲載誌名
International Journal of Advances in Psychology
出版者
Science and Engineering Publishing Company
3
3
開始ページ
86
終了ページ
93
発行日
2014-06-18
権利情報
This article is available at the CC BY-NC-ND 2.5 website: http://creativecommons.org/licenses/by-nc-nd/2.5/
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