ID 112402
Author
Fujisawa, Shoichiro The University of Tokushima KAKEN Search Researchers
Keywords
electroencephalogram
individual difference
gray relational grade
nearest neighbor method
personality
egogram
preference
Content Type
Journal Article
Description
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.
Journal Title
The Online Journal on Computer Science and Information Technology
ISSN
20904517
Publisher
International Foundation for Modern Education and Scientific Research
Volume
4
Issue
3
Start Page
276
End Page
280
Published Date
2014-07
Rights
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
FullText File
language
eng
TextVersion
Publisher
departments
Science and Technology