ID 112404
Author
Fujisawa, Shoichiro The University of Tokushima KAKEN Search Researchers
Keywords
electroencephalogram
matching patterns
personality
egogram
Yatabe-Guilford personality inventory
Kretschmer type personality inventory
k - nearest neighbour method
Content Type
Journal Article
Description
In this paper we introduce a method to classify matching patterns between music and human mood using an electroencephalogram (EEG) analysis technique and considering personality. We analyse the EEG of the left prefrontal cortex by single-point sensing. The EEG recording device uses dry-type sensors. The feature vector is created by connecting the personality quantification results and the EEG features. Egograms—the Yatabe-Guilford personality inventory and a Kretschmer-type personality inventory are used to quantify personality. The EEG features are extracted using fast Fourier transform. Then, the matching patterns are classified using the k -nearest neighbour method. To show the effectiveness of the proposed method, we conduct experiments using real EEG data.
Journal Title
The Online Journal on Computer Science and Information Technology
ISSN
20904517
Publisher
International Foundation for Modern Education and Scientific Research
Volume
5
Issue
3
Start Page
341
End Page
345
Published Date
2015-07
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