BASED EMOTION RECOGNITION

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AN ENHANCED COMPUTATIONAL MODEL BASED ON SOCIAL SPIDER OPTIMISATION ALGORITHM FOR EEG-

BASED EMOTION RECOGNITION

ABDULLAH YOUSEF AWWAD AL-QAMMAZ

DOCTOR OF PHILOSOPHY UNIVERSITY UTARA MALAYSIA

2019

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Permission to Use

In presenting this thesis in fulfilment of the requirements for a postgraduate degree from Universiti Utara Malaysia, I agree that the Universiti Library may make it freely available for inspection. I further agree that permission for the copying of this thesis in any manner, in whole or in part, for scholarly purpose may be granted by my supervisor(s) or, in their absence, by the Dean of Awang Had Salleh Graduate School of Arts and Sciences. It is understood that any copying or publication or use of this thesis or parts thereof for financial gain shall not be allowed without my written permission. It is also understood that due recognition shall be given to me and to Universiti Utara Malaysia for any scholarly use which may be made of any material from my thesis.

Requests for permission to copy or to make other use of materials in this thesis, in whole or in part, should be addressed to:

Dean of Awang Had Salleh Graduate School of Arts and Sciences UUM College of Arts and Sciences

Universiti Utara Malaysia 06010 UUM Sintok

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Abstrak

Emosi adalah elemen psikologi komunikasi manusia yang mempengaruhi kelakuan logik. Kini, emosi manusia dikenalpasti dengan menganalisa isyarat otak melalui electroencephalogram (EEG), namun kebanyakkan kajian menunjukkan keputusan ketepatan yang lemah. Ini disebabkan oleh limitasi kaedah pengekstrakan ciri, pemilihan ciri dan pengelasan sedia ada untuk menangani isyarat EEG yang komplek, kacau dan tak pegun. Oleh itu, kajian ini bertujuan untuk membangunkan model komputasi yang lebih baik untuk model pengecaman emosi manusia berdasarkan isyarat pelbagai dimensi EEG. Kajian ini telah meningkatkan model pengecaman emosi dalam tiga bahagian. Bahagian pertama adalah pengekstrakan ciri dengan mencadangkan nearest-neighbour Grubbs based Discrete Wavelet Packet Transform (DWPT), di mana data asing dikesan oleh ujian Grubbs dan digantikan dengan isyarat jiran terdekatnya. Bahagian kedua melibatkan kaedah pemilihan ciri dengan membangunkan Improved Social Spider Optimisation (ISSO). Kaedah ini dipertingkatkan dengan memasukkan carian global Particle Swarm Optimization (PSO) ke arah penyelesaian yang lebih baik dalam tingkah laku pergerakan labah- labah. Bahagian ketiga berkenaan dengan pembangunan Eagle Strategy Social Spider Optimization (ESSO) untuk penataan parameter Least Square Vector Machine (LSSVM) untuk mengelakkan masalah optima tempatan. Dalam kajian ini, model yang dicadangkan diuji ke atas data EEG pra-diproses yang diperoleh daripada Database for Emotion Analysis Using Physiological (DEAP). Data ini dibahagikan kepada dua kumpulan mengikut subjek iaitu Data 1 dan Data 2. Keputusan menunjukkan bahawa model yang dicadangkan mengatasi model sedia ada. Valens maksimum dan ketepatan rangsangan berdasarkan Data 1 masing-masing adalah 76.39% dan 83.33%. Sementara itu, valens maksimum dan ketepatan rangsangan dalam Data 2 masing-masing adalah 72.22% dan 81.94%. Model pengecaman emosi berasaskan EEG yang dicadangkan dapat menyumbang kepada keperluan perkembangan Brain-Computer Interface (BCI) yang cerdas dan boleh memudahkan pembangunan aplikasi penjagaan kesihatan.

Kata Kunci: Pengecaman emosi, Electroencephalogram, Discrete Wavelet Packet Transform, Social Spider Optimisation, Least Square Support Vector Machine

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Abstract

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.

Keywords: Emotion recognition, Electroencephalogram, Discrete Wavelet Packet Transform, Social Spider Optimisation, Least Square Support Vector Machine

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Acknowledgement

First and foremost, I thanks the mighty God for giving me this gift and simplifies my Ph.D journey.

I would like to dedicate this achievement to my father, who has strong will, which I have always acquired it by him, and for his constant support to me. As for my mother, as no words or statement are enough to express my thanks to her, I would shorten the letters and words, I only say thank you for your everlasting gifts to me, and for all prayers.

I am fully proud that I am one of graduated students of this ancient and giant University. I would like to express my sincere gratitude to my advisors Dr. Farzana Kabir Ahmad and Prof. Madya. Dr. Yuhanis Yusof for the continuous support of my Ph.D study, patience, motivation, and immense knowledge. Their guidance helped me over all the time of the research and writing of this thesis. I could not have imagined having a better advisors and mentors for my Ph.D study.

My sincere thanks also go to Prof. Dr. Ku Ruhana who is the head of the data science research lab, for supporting me and motivating me.

Last but not the least, I would like to thank my family; my brothers and sisters for supporting me spiritually throughout writing this thesis and during my Ph.D journey in general.

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List of Tables

Table 2.1. Comparison between Discrete Space and Continuous Space. ... 18

Table 2.2. A Summary of The Emotion Recognition Techniques. ... 23

Table 2.3. Some EEG-based Emotion Recognition Studies. ... 27

Table 2.4. Some Information about the Five Effective EEG Databases. ... 34

Table 2.5. Contents of Each Subject File. ... 39

Table 2.6. A Description of Time Domain and Frequency Domain ... 43

Table 2.7. Some Common Fractal Dimension Methods. ... 47

Table 2.8. Some Commonly Used Time-Frequency Algorithms. ... 49

Table 2.9. Some Studies That Used Feature Selection Methods in EEG-Based Classification ... 64

Table 2.10. Description, Advantages, and Disadvantages of the Most Common Feature Selection Types ... 67

Table 2.11. Some Studies That Used the SSO Algorithm ... 83

Table 2.12. Some EEG-Based Studies That Used Swarm Intelligence Algorithms ... 88

Table 2.13. SVM Kernel Functions ... 95

Table 2.14. A Review of Some Commonly Used Classification Algorithms ... 97

Table 2.15. Prediction Technique Using LSSVM Optimized by Swarm-Based Algorithm. ... 106

Table 3.1. Research Strategy ... 114

Table 3.2. The labels of original DEAP dataset and predefined label. ... 119

Table 3.3. One Versus One Coding on the Taken Classes. ... 132

Table 4.1 . Extracted Frequency Bands Information ... 143

Table 4.2. Classifciation Results of Diffirents K-fold Number . ... 148

Table 4.3. Classification Results for Original DWPT and Grubbs-DWPT of Data 1 and Data2 Experiments ... 150

Table 4.4. The Confusion Matrix of Data 1 Experiment of Original DWPT ... 153

Table 4.5. The Confusion Matrix of Data 1 Experiment of Grubbs-DWPT ... 154

Table 4.6. The Confusion Matrix of Data 2 Experiment of Original DWPT ... 154

Table 4.7. The Confusion Matrix of Data 2 Experiment of NN-Grubb-DWPT ... 155

Table 4.8. T-test Statistical Results of Grubbs-DWPT vs. Original DWPT for Data 1 and Data 2 Experiments’ Results. ... 157

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Table 5.1. Parameter/Factors Setup for ISSO-FS ... 163

Table 5.2. Feature Selection Results Experiments for EEG Dataset of Data 1 and Data 2 at Population is 10. ... 170

Table 5.7. T-test Results for Data 1 and Data 2 Experiments. ... 175

Table 5.8. The Selected Features Vectoes of Data 1 Valence. ... 177

Table 5.9. The Selected Features Vectoes of Data 1 Arousal . ... 178

Table 6.1. Parameter/Factor Setup for ESSO-LSSVM ... 190

Table 6.2. The Classification Experiments Results by Different Classification Schemes in the Range Between 0.001 and 100 for EEG Dataset of Data 1 and Data 2 at Population is 10. ... 197

Table 6.5. The Classification Experiments Results by Different Classification Schemes in the Range Between 0.001 and 100 for EEG Dataset of Data 1 and Data 2 at Population is 40 ... 203

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Table 6.7. The Classification Experiments Results by Different Classification

Schemes in the Range Between 0.001 and 1000 for EEG Dataset of Data 1 and Data 2 at Population is 10. ... 207 Table 6.8. The Classification Experiments Results by Different Classification

Schemes in the Range Between 0.001 and 1000 for EEG Dataset of Data 1 and Data 2 at Population is 50. ... 215 Table 6.12. T-test Results for Data 1 and Data2 Experiments of Range (0.001-100).

... 218 Table 6.13. T-test Results for Data 1 and Data2 Experiments of Range (0.001-1000).

... 219

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List of Figures

Figure 2.1. Emotions placed on arousal–valence space coordinates

(Jirayucharoensak et al., 2014)... 18

Figure 2.2. Hemisphere (Rao et al., 2012) . ... 23

Figure 2.3. Emotion recognition approach using EEG signals (Jatupaiboon et al., 2013) . ... 25

Figure 2.4. Brainwaves with basic emotion labels, including delta, theta, alpha, beta, and gamma (brainwavemaster, 2014). ... 26

Figure 2.5. Self-assessment manikins (SAM) (Morris, 1995) . ... 36

Figure 2.6. Experiment protocol (Rached & Perkusich, 2013). ... 37

Figure 2.7. The 10-10 international system (Sepulveda, 2011). ... 37

Figure 2.8. Arousal-valence of four classes (Koelstra et al., 2012)... 38

Figure 2.9. The 40 channels of each subject ... 40

Figure 2.10. DWT tree ... 51

Figure 2.11. Decomposition of five-level EEG signals using DWPT ... 53

Figure 2.12. Two levels of DWPT decomposition (Wali et al., 2013). ... 55

Figure 2.13. The Pseudo-code of the original DWPT ... 57

Figure 2.14. Emotion recognition approach using EEG signals ... 63

Figure 2.15. Configuration of each special relation: (a) Vibci, (b) Vibbi, and (c) Vibfi ... 78

Figure 2.16. Representation of the data flow of the SSO algorithm... 80

Figure 2.17. The pseudo-code of the SSO algorithm (Cuevas et al., 2013). ... 82

Figure 2.18. ANN layers system ... 94

Figure 2.19. SVM architecture ... 96

Figure 3.1. Research Steps... 115

Figure 3.2. The EEG subject’s files Used in this Research . ... 117

Figure 3.3. The Stored EEG Data of Subject ‘s07’. ... 118

Figure 3.4. Classes of emotional states ... 120

Figure 3.5. Description of the Data. ... 120

Figure 3.6. Sample of the data belonging to subject number 7. ... 122

Figure 3.7. Replacement of the outlier signal point at index 312 based on Grubbs- DWPT ... 124

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Figure 3.8. The Extracted Features Space ... 125

Figure 3.9. An Example of the extracted features from Data1. ... 126

Figure 3.10. Objective Function. ……..………. 132

Figure 3.11. The approach of the proposed computational model. ... 137

Figure 4.1. Research steps (feature extraction phase). ... 139

Figure 4.2. The flow of feature extraction using NN-Grubbs-DWPT. ... 140

Figure 4.3. The Loading EEG data of ‘subject 7’... 141

Figure 4.4. An example of predefined label. ... 141

Figure 4.5. Five-level decomposition tree of DWPT algorithm ... 146

Figure 4.6. Comparison in terms of min, max, mean, and SD values for Data 1. ... 151

Figure 4.7. Comparison of precision, recall, F-score, and AUC values for Data 1. 151 Figure 4.8. Comparison of min, max, mean, and SD values for Data 2. ... 152

Figure 4.9. Comparison of precision, recall, F-score, and AUC values for Data 2. 152 Figure 4.10. Theta bands (4–6.5 Hz) of the original DWPT and NN-Grubbs-DWPT ... 156

Figure 5.1. Research steps (feature selection phase). ... 161

Figure 5.2. The results obtained by the different feature selection methods . ... 169

Figure 5.3. Selected features’ numbers in different optimizers. ... 177

Figure 5.4. Average distances of the population of Data1 experiment. ... 181

Figure 5.5. Average distances of the population of Data2 experiment. ... 182

Figure 5.6. Surface plots of the population distances over 20 run and 100 iterations. ... 186

Figure 6.1. Research steps (classification phase) ... 188

Figure 6.2. A simplifised form of the classification model. ... 189

Figure 6.3. ESSO-LSSVM classification scheme (parameters tuning). ... 195

Figure 6.4. The distances of the populations along with its classification accuracy, Data1. ... 223

Figure 6.5. The distances of the populations along with its classification accuracy, Data2. ... 224

Figure 6.6. The boxplots of the population distances of the ESSO, ISSO, SSO, ABC, and PSO algorithms, Data1. ... 225

Figure 6.7. The boxplots of the population distances of the ESSO, ISSO, SSO, ABC, and PSO algorithms, Data 2. ... 226

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Figure 6.8. Surface plots of the population distances over 20 run and 100 iterations, Data1. ... 229 Figure 6.9. Surface plots of the population distances over 20 run and 100 iterations, Data2. ... 230

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CHAPTER ONE INTRODUCTION

1.1 Background

Emotion states have great influences and play essential roles in human life, such as learning, decision-making, and communication. Human emotion recognition is commonly referred to as a pattern classification problem. Accurate classification results can only be accomplished through the determination of an ideal/optimal feature space with implementation of a classifier that closely models the actual classification problem based on the selected feature space (Al-Nafjan, Hosny, Al-Wabil, & Al- Ohali, 2017; Zhang, Jiang, & Dong, 2017; Ghosh Dastidar, 2007; Kim, Kim, Oh, &

Kim, 2013; Kumar et al., 2016; Valenza, Citi, Lanatá, Scilingo, & Barbieri, 2014).

Emotion can be assessed by analysing physiological signals (Al-Nafjan et al., 2017;

Granero et al., 2016; Kim et al., 2013; Taffese, 2017). Human emotion can be systematically described through mapping onto a corresponding two-dimensional valence–arousal emotion space in which valence is represented as a horizontal axis indicating positivity of emotion and arousal is represented as a vertical axis indicating emotional activation level (Russell, 1979; Thammasan, Moriyama, Fukui, & Numao, 2017). Physiological signals are divided into a couple of categories, for instance: (i) the peripheral nervous system (e.g., galvanic skin response (GSR), electromyography (EMG), and heart rate (HR)) and (ii) the central nervous system (i.e., electroencephalogram [EEG]) (Chanel, Kronegg, Grandjean, & Pun, 2006; Scherer, 2001; Girardi, Lanubile, & Novielli, 2017; Granero et al., 2016; Kim et al., 2013;

Luneski et al., 2010; Taffese, 2017). The interaction of neurons in the human brain creates rhythmic signals. These signals can be separated into different bands based on

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BASED EMOTION RECOGNITION

AN ENHANCED COMPUTATIONAL MODEL BASED ON SOCIAL SPIDER OPTIMISATION ALGORITHM FOR EEG-

BASED EMOTION RECOGNITION

ABDULLAH YOUSEF AWWAD AL-QAMMAZ

DOCTOR OF PHILOSOPHY UNIVERSITY UTARA MALAYSIA

2019

Permission to Use

Abstrak

Abstract

Acknowledgement

Table of Contents

List of Tables

List of Figures

CHAPTER ONE INTRODUCTION

REFERENCES