Emotion Recognition System Based on EEG Signals Using Relative Wavelet Energy Features and a Modified Radial Basis Function Neural Networks

被引：0
|
作者：
Krisnandhika, Bharasaka [1 ]
Faqih, Akhmad [1 ]
Purnamasari, Prima Dewi [1 ]
Kusumoputro, Benyamin [1 ]
机构：
[1] Univ Indonesia, Dept Elect Engn, Depok, Indonesia
来源：
2017 INTERNATIONAL CONFERENCE ON CONSUMER ELECTRONICS AND DEVICES (ICCED 2017) | 2017年
关键词：
emotion recognition; emotion classification; relative wavelet energy; modified radial basis function neural networks;
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：
Emotion recognition is very important, especially on its application for patient monitoring and in the treatment management system of that patient. In this paper, an EEG-based emotion recognition system is developed that consists of a feature extraction subsystem and a classifier subsystem. In this research, we have studied the utilization of a relative wavelet energy as the feature extraction, and a modified radial basis function neural networks is then implemented as the classifier. Experimental result shows that the relative wavelet energy and the modified radial basis function neural networks achieved an average recognition rate of 76% when using a 50% of the data in the training stage.
引用
收藏
页码：50 / 54
页数：5
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