Enhanced EEG-Based Emotion Detection Technique using Deep Belief Network and Wavelet Transform

Authors(2) :-Sahar Jodat, Khosrow Amirizadeh

Today's, the role of emotion in communication , brain-computer interface, brain diseases and mental states, car driver monitoring and recommendation systems is proven. Therefore, automatic emotions detection has become one of the most challenging issue. Until now, numerous studies have been addressed different technique on improving automatic emotion detection.In this study, to achieve bether validation in classification of emotion by EEG signals, we combined wavelet transform with deep belief network. For, non-stationary and time-varying are the most important properties of EEG signals, we decided to use discrete wavelet transform (sym8) for extracting features such as power, then applied deep belief network as a classifier to classify emotions according to two-dimensional arousal-valence model. To examine the effectiveness of the method, we used DEAP database and mapped different emotions on two different classes of valence and arousal. Final results show an acceptable enhancement with the accuracy of 75.52% and 81.03% for valence and arousal, respectively.

Authors and Affiliations

Sahar Jodat

Khosrow Amirizadeh

EEG signals, Discrete Wavelet Transform, Deep Belief Network, two-dimensional arousal-valence model, DEAP

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Publication Details

Published in : Volume 2 | Issue 7 | September 2017
Date of Publication : 2017-09-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 56-67
Manuscript Number : CSEIT174408
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

Sahar Jodat, Khosrow Amirizadeh, "Enhanced EEG-Based Emotion Detection Technique using Deep Belief Network and Wavelet Transform", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 2, Issue 7, pp.56-67, September-2017.
Journal URL : http://ijsrcseit.com/CSEIT174408

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