Classification of Human Emotions from EEG Signals using Statistical Features and Neural Network

Authors

  • Chai Tong Yuen
  • Woo San San
  • Tan Ching Seong
  • Mohamed Rizon

Keywords:

EEG, Human emotions, Neural network, Statistical features,

Abstract

A statistical based system for human emotions classification by using electroencephalogram (EEG) is proposed in this paper. The data used in this study is acquired using EEG and the emotions are elicited from six human subjects under the effect of emotion stimuli. This paper also proposed an emotion stimulation experiment using visual stimuli. From the EEG data, a total of six statistical features are computed and back-propagation neural network is applied for the classification of human emotions. In the experiment of classifying five types of emotions: Anger, Sad, Surprise, Happy, and Neutral. As result the overall classification rate as high as 95% is achieved.

Downloads

Download data is not yet available.

Author Biographies

Chai Tong Yuen

Department of Mechatronics and Biomedical Engineering, Faculty of Engineering and Science,
Universiti Tunku Abdul Rahman

Woo San San

Department of Mechatronics and Biomedical Engineering, Faculty of Engineering and Science,
Universiti Tunku Abdul Rahman

Tan Ching Seong

Department of Mechatronics and Biomedical Engineering, Faculty of Engineering and Science,
Universiti Tunku Abdul Rahman

Mohamed Rizon

Department of Electrical Engineering, King Saud University

Downloads

How to Cite

Yuen, C. T., San, W. S., Seong, T. C., & Rizon, M. (2009). Classification of Human Emotions from EEG Signals using Statistical Features and Neural Network. International Journal of Integrated Engineering, 1(3). Retrieved from https://publisher.uthm.edu.my/ojs/index.php/ijie/article/view/118

Issue

Section

Issue on Electrical and Electronic Engineering