An Electroencephalogram Analysis Method to Detect Preference Using Gray Association Degree

被引：0

作者：

Ito, Shin-ichi ^{[1
]}

Ito, Momoyo ^{[1
]}

Fukumi, Minoru ^{[1
]}

机构：

[1] Tokushima Univ, Tokushima, Tokushima, Japan

来源：

2018 INTERNATIONAL CONFERENCE ON ELECTRONICS, INFORMATION, AND COMMUNICATION (ICEIC) | 2018年

关键词：

electroencephalogram; preference; gray association degree; support vector machine;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

This paper introduces an electroencephalogram (EEG) analysis method to detect human preference. The proposed method consists of three phases; EEG recording, EEG feature extraction and preference detection. In EEG recording, we employ the simple electroencephalograph. The measurement position to record the EEG is left frontal lobe (FP1). The gray association degree is used to extract the EEG feature. The support vector machine is used to detect human preference on sounds listened to. In order to show the effectiveness of the proposed method, we conduct the experiments. In the experimental results, the mean of the accuracy rate of the favorite sound detection was higher than 88%.

引用

页码：40 / 41

页数：2