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Article Information:
Effectiveness of Statistical Features for Human Emotions Classification using EEG Biosensors
Chai Tong Yuen, Woo San San, Jee-Hou Ho and M. Rizon
Corresponding Author: Chai Tong Yuen
Submitted: October 22, 2012
Accepted: December 14, 2012
Published: May 20, 2013 |
Abstract:
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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.
Key words: EEG, human emotions, neural network, statistical features, , ,
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Abstract
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Cite this Reference:
Chai Tong Yuen, Woo San San, Jee-Hou Ho and M. Rizon, . Effectiveness of Statistical Features for Human Emotions Classification using EEG Biosensors. Research Journal of Applied Sciences, Engineering and Technology, (21): 5083-5089.
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ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
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