ID | 112404 |
Author |
Ito, Shin-ichi
The University of Tokushima
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Ito, Momoyo
The University of Tokushima
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Fukumi, Minoru
The University of Tokushima
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Keywords | electroencephalogram
matching patterns
personality
egogram
Yatabe-Guilford personality inventory
Kretschmer type personality inventory
k - nearest neighbour method
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Content Type |
Journal Article
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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.
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Journal Title |
The Online Journal on Computer Science and Information Technology
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ISSN | 20904517
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Publisher | International Foundation for Modern Education and Scientific Research
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Volume | 5
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Issue | 3
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Start Page | 341
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End Page | 345
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Published Date | 2015-07
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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)
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EDB ID | |
FullText File | |
language |
eng
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TextVersion |
Publisher
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departments |
Science and Technology
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