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
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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