Discrete wavelet packet transform for electroencephalogram based valence-arousal emotion recognition

Electroencephalogram (EEG) based emotion recognition has received considerable attention as it is a non-invasive method of acquiring physiological signals from the brain and it could directly reflect emotional states. However, the challenging issues regarding EEG-based emotional state recognition is...

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Main Author:	Olakunle, Oyenuga Wasiu
Format:	Thesis
Language:	eng eng
Published:	2015
Subjects:	QA71-90 Instruments and machines
Online Access:	https://etd.uum.edu.my/5281/1/s815456.pdf https://etd.uum.edu.my/5281/2/s815456_abstract.pdf
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id	my-uum-etd.5281
record_format	uketd_dc
institution	Universiti Utara Malaysia
collection	UUM ETD
language	eng eng
advisor	Kabir Ahmad, Farzana
topic	QA71-90 Instruments and machines
spellingShingle	QA71-90 Instruments and machines Olakunle, Oyenuga Wasiu Discrete wavelet packet transform for electroencephalogram based valence-arousal emotion recognition
description	Electroencephalogram (EEG) based emotion recognition has received considerable attention as it is a non-invasive method of acquiring physiological signals from the brain and it could directly reflect emotional states. However, the challenging issues regarding EEG-based emotional state recognition is that it requires well-designed methods and algorithms to extract necessary features from the complex, chaotic, and multichannel EEG signal in order to achieve optimum classification performance. The aim of this study is to discover the feature extraction method and the combination of electrode channels that optimally implements EEG-based valencearousal emotion recognition. Based on this, two emotion recognition experiments were performed to classify human emotional states into high/low valence or high/low arousal. The first experiment was aimed to evaluate the performance of Discrete Wavelet Packet Transform (DWPT) as a feature extraction method. The second experiment was aimed at identifying the combination of electrode channels that optimally recognize emotions based on the valence-arousal model in EEG emotion recognition. In order to evaluate the results of this study, a benchmark EEG dataset was used to implement the emotion classification. In the first experiment, the entropy features of the theta, alpha, beta, and gamma bands through the 10 EEG channels Fp1, Fp2, F3, F4, T7, T8, P3, P4, O1, and O2 were extracted using DWPT and Radial Basis Function-Support Vector Machine (RBF-SVM) was used as the classifier. In the second experiment, the classification experiments were repeated using the 4 EEG frontal channels Fp1, Fp2, F3, and F4. The result of the first experiment showed that entropy features extracted using DWPT are better than bandpower features. While the result of the second classification experiment shows that the combination of the 4 frontal channels is more significant than the combination of the 10 channels
format	Thesis
qualification_name	masters
qualification_level	Master's degree
author	Olakunle, Oyenuga Wasiu
author_facet	Olakunle, Oyenuga Wasiu
author_sort	Olakunle, Oyenuga Wasiu
title	Discrete wavelet packet transform for electroencephalogram based valence-arousal emotion recognition
title_short	Discrete wavelet packet transform for electroencephalogram based valence-arousal emotion recognition
title_full	Discrete wavelet packet transform for electroencephalogram based valence-arousal emotion recognition
title_fullStr	Discrete wavelet packet transform for electroencephalogram based valence-arousal emotion recognition
title_full_unstemmed	Discrete wavelet packet transform for electroencephalogram based valence-arousal emotion recognition
title_sort	discrete wavelet packet transform for electroencephalogram based valence-arousal emotion recognition
granting_institution	Universiti Utara Malaysia
granting_department	Awang Had Salleh Graduate School of Arts & Sciences
publishDate	2015
url	https://etd.uum.edu.my/5281/1/s815456.pdf https://etd.uum.edu.my/5281/2/s815456_abstract.pdf
_version_	1747827899376861184
spelling	my-uum-etd.52812021-03-18T08:37:28Z Discrete wavelet packet transform for electroencephalogram based valence-arousal emotion recognition 2015 Olakunle, Oyenuga Wasiu Kabir Ahmad, Farzana Awang Had Salleh Graduate School of Arts & Sciences Awang Had Salleh Graduate School of Arts and Sciences QA71-90 Instruments and machines Electroencephalogram (EEG) based emotion recognition has received considerable attention as it is a non-invasive method of acquiring physiological signals from the brain and it could directly reflect emotional states. However, the challenging issues regarding EEG-based emotional state recognition is that it requires well-designed methods and algorithms to extract necessary features from the complex, chaotic, and multichannel EEG signal in order to achieve optimum classification performance. The aim of this study is to discover the feature extraction method and the combination of electrode channels that optimally implements EEG-based valencearousal emotion recognition. Based on this, two emotion recognition experiments were performed to classify human emotional states into high/low valence or high/low arousal. The first experiment was aimed to evaluate the performance of Discrete Wavelet Packet Transform (DWPT) as a feature extraction method. The second experiment was aimed at identifying the combination of electrode channels that optimally recognize emotions based on the valence-arousal model in EEG emotion recognition. In order to evaluate the results of this study, a benchmark EEG dataset was used to implement the emotion classification. In the first experiment, the entropy features of the theta, alpha, beta, and gamma bands through the 10 EEG channels Fp1, Fp2, F3, F4, T7, T8, P3, P4, O1, and O2 were extracted using DWPT and Radial Basis Function-Support Vector Machine (RBF-SVM) was used as the classifier. In the second experiment, the classification experiments were repeated using the 4 EEG frontal channels Fp1, Fp2, F3, and F4. The result of the first experiment showed that entropy features extracted using DWPT are better than bandpower features. While the result of the second classification experiment shows that the combination of the 4 frontal channels is more significant than the combination of the 10 channels 2015 Thesis https://etd.uum.edu.my/5281/ https://etd.uum.edu.my/5281/1/s815456.pdf text eng public https://etd.uum.edu.my/5281/2/s815456_abstract.pdf text eng public masters masters Universiti Utara Malaysia Aftanas, L. I., Reva, N. V., Varlamov, A. A., Pavlov, S. V., & Makhnev, V. P. (2004). 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Discrete wavelet packet transform for electroencephalogram based valence-arousal emotion recognition

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