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     Research Journal of Applied Sciences, Engineering and Technology


Effectiveness of Statistical Features for Human Emotions Classification using EEG Biosensors

1Chai Tong Yuen, 2Woo San San, 3Jee-Hou Ho and 4M. Rizon
1Department of Mechatronics and Biomedical Engineering, University Tunku Abdul Rahman, 53300, Malaysia
2Materialise Sdn Bhd, 47820, Malaysia
3Department of Mechanical, Materials and Manufacturing Engineering, the University of Nottingham Malaysia Campus, 43500, Malaysia
4King Saud University, Riyadh 11433, Kingdom of Saudi Arabia
Research Journal of Applied Sciences, Engineering and Technology  2013  21:5083-5089
http://dx.doi.org/10.19026/rjaset.5.4401  |  © The Author(s) 2013
Received: October 22, 2012  |  Accepted: December 14, 2012  |  Published: May 20, 2013

Abstract

This study proposes a statistical features-based classification system for human emotions by using Electroencephalogram (EEG) bio-sensors. A total of six statistical features are computed from the EEG data and Artificial Neural Network is applied for the classification of emotions. The system is trained and tested with the statistical features extracted from the psychological signals acquired under emotions stimulation experiments. The effectiveness of each statistical feature and combinations of statistical features in classifying different types of emotions has been studied and evaluated. In the experiment of classifying four main types of emotions: Anger, Sad, Happy and Neutral, the overall classification rate as high as 90% is achieved.

Keywords:

EEG, human emotions, neural network, statistical features,


References


Competing interests

The authors have no competing interests.

Open Access Policy

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Copyright

The authors have no competing interests.

ISSN (Online):  2040-7467
ISSN (Print):   2040-7459
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