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EEG-based Emotion Classification using Bayesian Belief Networks in Pleasant and Unpleasant Odorant Stimuli

Title
EEG-based Emotion Classification using Bayesian Belief Networks in Pleasant and Unpleasant Odorant Stimuli
Authors
Kwon, Hyeong OhKang, Won SeokYun, Sang Hun
DGIST Authors
Kwon, Hyeong Oh; Kang, Won Seok; Yun, Sang Hun
Issue Date
2014
Citation
Global Journal on Technology, 5, 53-58
Type
Article
ISSN
2147-5369
Abstract
In this paper, we proposes an emotion classification method based on Bayesian Belief Networks (BBN) to classify the EEG signals which are induced by the olfactory stimuli. In order to stimulate the olfactory organ, the citralva is used as the pleasant smell and 2-melcaptoethanol is used as the unpleasant smell. We placed the 4-channel EEG electrodes on F3, F4 at frontal lobe and T3, T4 at temporal lobe to acquire EEG signals in according to the standard electrode placement, which is called international 10-20 system. The participants are five high school students (4 male and 1 female) whose ages are from 17 to 18. To extract features from EEG signals, the timefrequency analysis is performed by using the Event-Related Spectral Perturbation (ERSP). The average values of relative power of the frequency in each time domain are used as the features for the BBN classifier. To evaluate the performance of the proposed method, we compared the performance of the BBN and Naïve Bayesian Networks (BN). As a result of the comparison, we confirm that the classification rate of the BBN is increased by approximately 11%.
URI
http://hdl.handle.net/20.500.11750/13372
http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.736.7519&rep=rep1&type=pdf
Publisher
Academic World Education & Research Center
Related Researcher
  • Author Kang, Won-Seok  
  • Research Interests Data Mining & Machine Learning for Text & Multimedia, Brain-Sense-ICTConvergence Computing, Computational Olfaction Measurement, Simulation&Modeling
Files:
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Collection:
ETC1. Journal Articles
Division of Electronics & Information System1. Journal Articles


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