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

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

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;

Full Text:

PDF


Copyright (c)

Copyright International Journal of Integrated Engineering (IJIE) 2013.

ISSN : 2229-838X

ijie@uthm.edu.my

Creative Commons License
This OJS site and its metadata are licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.