An enhanced computational model based on social spider optimisation algorithm for EEG- based emotion recognition

Emotion is an element of human communication psychology that influences logical behaviour. Recently, human emotions are recognised by analysing signals of brain via electroencephalogram (EEG), however most studies show poor accuracy results. This is due to the limitations of existing feature extract...

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Main Author:	Al-Qammaz, Abdullah Yousef Awwad
Format:	Thesis
Language:	eng eng eng
Published:	2019
Subjects:	QA Mathematics
Online Access:	https://etd.uum.edu.my/9047/1/s900489_01.pdf https://etd.uum.edu.my/9047/2/s900489_02.pdf https://etd.uum.edu.my/9047/3/s900489_references.docx
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id	my-uum-etd.9047
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spelling	my-uum-etd.90472022-02-08T02:38:55Z An enhanced computational model based on social spider optimisation algorithm for EEG- based emotion recognition 2019 Al-Qammaz, Abdullah Yousef Awwad Kabir Ahmad, Farzana Yusof, Yuhanis Awang Had Salleh Graduate School of Arts & Sciences Awang Had Salleh Graduate School of Arts & Sciences QA Mathematics Emotion is an element of human communication psychology that influences logical behaviour. Recently, human emotions are recognised by analysing signals of brain via electroencephalogram (EEG), however most studies show poor accuracy results. This is due to the limitations of existing feature extraction, feature selection and classification methods to address complex, chaotic and non-stationary EEG signals. Therefore, this research aims to develop an improved computational model for human emotion recognition model based on EEG multi-dimensional signals. This research has enhanced emotion recognition model in three parts. First part is feature extraction by proposing nearest-neighbour Grubbs based Discrete Wavelet Packet Transform (DWPT), where outliers are detected by Grubbs test and replaced to its nearest neighbour signal. Second part involves the feature selection method through developing an Improved Social Spider Optimisation (ISSO). This method is enhanced by incorporating Particle Swarm Optimization (PSO) global search towards better solution of spider movement behaviour. Third part is concerned on the development of Eagle Strategy Social Spider Optimisation (ESSO) for tuning parameters of Least Square Support Vector Machine (LSSVM) to avoid local optima problem. In this research, the proposed model is tested on the pre-processed EEG data obtained from Database for Emotion Analysis Using Physiological (DEAP) data set. The data was split into two groups according to subjects which are Data 1 and Data 2. Results showed that the proposed model outperforms the existing model. The maximum valence and arousal accuracies based on Data 1 were 76.39% and 83.33% respectively. While, the maximum valence and arousal accuracies in Data 2 were 72.22% and 81.94% respectively. The proposed EEG-based emotion recognition model contributes to the growing needs of an intelligent Brain-Computer Interface (BCI) and can facilitate the development of healthcare applications. 2019 Thesis https://etd.uum.edu.my/9047/ https://etd.uum.edu.my/9047/1/s900489_01.pdf text eng public https://etd.uum.edu.my/9047/2/s900489_02.pdf text eng public https://etd.uum.edu.my/9047/3/s900489_references.docx text eng public other doctoral Universiti Utara Malaysia
institution	Universiti Utara Malaysia
collection	UUM ETD
language	eng eng eng
advisor	Kabir Ahmad, Farzana Yusof, Yuhanis
topic	QA Mathematics
spellingShingle	QA Mathematics Al-Qammaz, Abdullah Yousef Awwad An enhanced computational model based on social spider optimisation algorithm for EEG- based emotion recognition
description	Emotion is an element of human communication psychology that influences logical behaviour. Recently, human emotions are recognised by analysing signals of brain via electroencephalogram (EEG), however most studies show poor accuracy results. This is due to the limitations of existing feature extraction, feature selection and classification methods to address complex, chaotic and non-stationary EEG signals. Therefore, this research aims to develop an improved computational model for human emotion recognition model based on EEG multi-dimensional signals. This research has enhanced emotion recognition model in three parts. First part is feature extraction by proposing nearest-neighbour Grubbs based Discrete Wavelet Packet Transform (DWPT), where outliers are detected by Grubbs test and replaced to its nearest neighbour signal. Second part involves the feature selection method through developing an Improved Social Spider Optimisation (ISSO). This method is enhanced by incorporating Particle Swarm Optimization (PSO) global search towards better solution of spider movement behaviour. Third part is concerned on the development of Eagle Strategy Social Spider Optimisation (ESSO) for tuning parameters of Least Square Support Vector Machine (LSSVM) to avoid local optima problem. In this research, the proposed model is tested on the pre-processed EEG data obtained from Database for Emotion Analysis Using Physiological (DEAP) data set. The data was split into two groups according to subjects which are Data 1 and Data 2. Results showed that the proposed model outperforms the existing model. The maximum valence and arousal accuracies based on Data 1 were 76.39% and 83.33% respectively. While, the maximum valence and arousal accuracies in Data 2 were 72.22% and 81.94% respectively. The proposed EEG-based emotion recognition model contributes to the growing needs of an intelligent Brain-Computer Interface (BCI) and can facilitate the development of healthcare applications.
format	Thesis
qualification_name	other
qualification_level	Doctorate
author	Al-Qammaz, Abdullah Yousef Awwad
author_facet	Al-Qammaz, Abdullah Yousef Awwad
author_sort	Al-Qammaz, Abdullah Yousef Awwad
title	An enhanced computational model based on social spider optimisation algorithm for EEG- based emotion recognition
title_short	An enhanced computational model based on social spider optimisation algorithm for EEG- based emotion recognition
title_full	An enhanced computational model based on social spider optimisation algorithm for EEG- based emotion recognition
title_fullStr	An enhanced computational model based on social spider optimisation algorithm for EEG- based emotion recognition
title_full_unstemmed	An enhanced computational model based on social spider optimisation algorithm for EEG- based emotion recognition
title_sort	enhanced computational model based on social spider optimisation algorithm for eeg- based emotion recognition
granting_institution	Universiti Utara Malaysia
granting_department	Awang Had Salleh Graduate School of Arts & Sciences
publishDate	2019
url	https://etd.uum.edu.my/9047/1/s900489_01.pdf https://etd.uum.edu.my/9047/2/s900489_02.pdf https://etd.uum.edu.my/9047/3/s900489_references.docx
_version_	1747828516160798720

An enhanced computational model based on social spider optimisation algorithm for EEG- based emotion recognition

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