EEG-based emotion classification using wavelet based features and support vector machine classifier

As technology and the understanding of emotions are evolving, there are numerous opportunities for classification of emotion due to the high demand in the psychophysiological research. The researches need an efficient mechanism to recognise the various emotions precisely with less computation comple...

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Main Author:	M. Razali, Normasliza
Format:	Thesis
Language:	English
Published:	2015
Subjects:	QA75 Electronic computers Computer science
Online Access:	http://eprints.utm.my/id/eprint/53780/25/NormaslizaMRazaliMFC2015.pdf
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spelling	my-utm-ep.537802020-09-02T06:17:36Z EEG-based emotion classification using wavelet based features and support vector machine classifier 2015-02 M. Razali, Normasliza QA75 Electronic computers. Computer science As technology and the understanding of emotions are evolving, there are numerous opportunities for classification of emotion due to the high demand in the psychophysiological research. The researches need an efficient mechanism to recognise the various emotions precisely with less computation complexity. The current methods available are too complex with higher computational time. This study proposes a classification of human emotion using electroencephalogram signals (EEG). The study utilised electroencephalogram signals (EEG) to classify emotions which is positive/negative arousal, valence and normal emotions. Electroencephalogram signals (EEG) are analysed from 4 different participants from the dataset that acquire from the public data source. These dataset go through several processes before the derivation of the features such as preprocessing using band pass filtering and artifacts removals, segmentation of the signals and Multiwavelet Transform (MWT) analysis of the processed data. The signals are decomposed up to level 3 decomposition and detail coefficients are used for features extraction. Statistical and power spectral density (PSD) features are computed and feed into the classifiers. Simple classification methods Support Vector Machine (SVM) is used to classify the emotion and their performances are evaluated. The experimental results report that statistical features and Support Vector Machine (SVM) achieved better accuracy up to 75.8%, 72.3% and 74.0% for arousal, valence and normal class respectively. In conclusion this research suggests the use of Multiwavelet Analysis for future work on recognizing various emotions from the Electroencephalogram signals (EEG). 2015-02 Thesis http://eprints.utm.my/id/eprint/53780/ http://eprints.utm.my/id/eprint/53780/25/NormaslizaMRazaliMFC2015.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:85237 masters Universiti Teknologi Malaysia, Faculty of Computing Faculty of Computing
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	QA75 Electronic computers Computer science
spellingShingle	QA75 Electronic computers Computer science M. Razali, Normasliza EEG-based emotion classification using wavelet based features and support vector machine classifier
description	As technology and the understanding of emotions are evolving, there are numerous opportunities for classification of emotion due to the high demand in the psychophysiological research. The researches need an efficient mechanism to recognise the various emotions precisely with less computation complexity. The current methods available are too complex with higher computational time. This study proposes a classification of human emotion using electroencephalogram signals (EEG). The study utilised electroencephalogram signals (EEG) to classify emotions which is positive/negative arousal, valence and normal emotions. Electroencephalogram signals (EEG) are analysed from 4 different participants from the dataset that acquire from the public data source. These dataset go through several processes before the derivation of the features such as preprocessing using band pass filtering and artifacts removals, segmentation of the signals and Multiwavelet Transform (MWT) analysis of the processed data. The signals are decomposed up to level 3 decomposition and detail coefficients are used for features extraction. Statistical and power spectral density (PSD) features are computed and feed into the classifiers. Simple classification methods Support Vector Machine (SVM) is used to classify the emotion and their performances are evaluated. The experimental results report that statistical features and Support Vector Machine (SVM) achieved better accuracy up to 75.8%, 72.3% and 74.0% for arousal, valence and normal class respectively. In conclusion this research suggests the use of Multiwavelet Analysis for future work on recognizing various emotions from the Electroencephalogram signals (EEG).
format	Thesis
qualification_level	Master's degree
author	M. Razali, Normasliza
author_facet	M. Razali, Normasliza
author_sort	M. Razali, Normasliza
title	EEG-based emotion classification using wavelet based features and support vector machine classifier
title_short	EEG-based emotion classification using wavelet based features and support vector machine classifier
title_full	EEG-based emotion classification using wavelet based features and support vector machine classifier
title_fullStr	EEG-based emotion classification using wavelet based features and support vector machine classifier
title_full_unstemmed	EEG-based emotion classification using wavelet based features and support vector machine classifier
title_sort	eeg-based emotion classification using wavelet based features and support vector machine classifier
granting_institution	Universiti Teknologi Malaysia, Faculty of Computing
granting_department	Faculty of Computing
publishDate	2015
url	http://eprints.utm.my/id/eprint/53780/25/NormaslizaMRazaliMFC2015.pdf
_version_	1747817625563430912

EEG-based emotion classification using wavelet based features and support vector machine classifier

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