ID 113282
Author
Fujisawa, Shoichiro The University of Tokushima KAKEN Search Researchers
Keywords
Preference
Egogram
Electroencephalogram
Individual Difference
Self-organizing Map
Pattern Classification
Content Type
Journal Article
Description
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.
Journal Title
International Journal of Advances in Psychology
Publisher
Science and Engineering Publishing Company
Volume
3
Issue
3
Start Page
86
End Page
93
Published Date
2014-06-18
Rights
This article is available at the CC BY-NC-ND 2.5 website: http://creativecommons.org/licenses/by-nc-nd/2.5/
EDB ID
FullText File
language
eng
TextVersion
Publisher
departments
Science and Technology