Title of article :
Classification of Human Emotions from EEG Signals using Statistical Features and Neural Network
Author/Authors :
Yuen, Chai Tong Universiti Tunku Abdul Rahman - Faculty of Engineering and Science - Department of Mechatronics and Biomedical Engineering , San, Woo San Universiti Tunku Abdul Rahman - Faculty of Engineering and Science - Department of Mechatronics and Biomedical Engineering , Rizon, Mohamed King Saud University - Department of Electrical Engineering, Saudi Arabia , Seong, Tan Ching Universiti Tunku Abdul Rahman - Faculty of Engineering and Science - Department of Mechatronics and Biomedical Engineering
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
Keywords :
EEG , Human emotions , Neural network , Statistical features
Journal title :
International Journal of Integrated Engineering
Journal title :
International Journal of Integrated Engineering
