A Review of EEG Emotion Recognition

  IJCTT-book-cover
 
International Journal of Computer Trends and Technology (IJCTT)          
 
© 2019 by IJCTT Journal
Volume-67 Issue-6
Year of Publication : 2019
Authors : Mohamed Ahmed Abdullah, Lars Rune Christensen
DOI :  10.14445/22312803/IJCTT-V67I6P106

MLA

MLA Style:Mohamed Ahmed Abdullah, Lars Rune Christensen"A Review of EEG Emotion Recognition" International Journal of Computer Trends and Technology 67.6 (2019): 41-47.

APA Style Mohamed Ahmed Abdullah, Lars Rune Christensen. A Review of EEG Emotion RecognitionInternational Journal of Computer Trends and Technology, 67(6),41-47.

Abstract
Emotion recognition is an important aspect of HMI (Human Machine Interface) Field, EEG (Electroencephalography) allows a simple and effective elicitation of those emotions, increasing the accuracy of the EEG signals is the focus of many researchers from across the globe, some are intending to improve the signals by focusing on the signal processing techniques, some are focusing on statistics or machine learning techniques. In this paper, we will discuss the most common techniques, especially the studies that are yielding to the best result, but we also are going to highlight the novel ways of classifying the emotions even if the results weren’t the best. Also reviewing the common steps of making the emotion elicitation experiment setup, we will discuss the different techniques of collecting the signals, then extracting the features then selecting the features, as well as discussing some standing problems in the field and future growth areas.

Reference
[1] Esmeralda C. Djamal, Poppi Lodaya, "EEG based emotion monitoring using wavelet and learning vector quantization," 4th International Conference on Electrical Engineering, Computer Science and Informatics (EECSI), 2017.
[2] P. Ackermann, C. Kohlschein, J. Á. Bitsch, K. Wehrle and S. Jeschke, "EEG-based automatic emotion recognition: Feature extraction, selection and classification methods," 18th International Conference on e-Health Networking, Applications and Services (Healthcom), 2016.
[3] A. Samara, M. L. R. Menezes and L. Galway, "Feature Extraction for Emotion Recognition and Modelling Using Neurophysiological Data,," 15th International Conference on Ubiquitous Computing and Communications and 2016 International Symposium on Cyberspace and Security (IUCC-CSS), 2016.
[4] A. Patil, C. Deshmukh and A. R. Panat, "Feature extraction of EEG for emotion recognition using Hjorth features and higher order crossings," Conference on Advances in Signal Processing (CASP), 2016 .
[5] S. W. Byun, S. P. Lee and H. S. Han, "Feature Selection and Comparison for the Emotion Recognition According to Music Listening," International Conference on Robotics and Automation Sciences (ICRAS), 2017.
[6] K. Yano and T. Suyama, , "Fixed low-rank EEG spatial filter estimation for emotion recognition induced by movies," International Workshop on Pattern Recognition in Neuroimaging (PRNI), 2016.
[7] P. C. Petrantonakis and L. J. Hadjileontiadis, "Adaptive Emotional Information Retrieval From EEG Signals in the Time-Frequency Domain," IEEE Transactions on Signal Processing, 2012.
[8] Y. Gao, H. J. Lee and R. M. Mehmood, "Deep learninig of EEG signals for emotion recognition," IEEE International Conference on Multimedia & Expo Workshops (ICMEW), 2015.
[9] Y. H. Liu, W. T. Cheng, Y. T. Hsiao, C. T. Wu and M. D. Jeng, "EEG-based emotion recognition based on kernel Fisher`s discriminant analysis and spectral powers," IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2014.
[10] D. Huang, C. Guan, Kai Keng Ang, Haihong Zhang and Yaozhang Pan, "Asymmetric Spatial Pattern for EEG-based emotion detection," International Joint Conference on Neural Networks (IJCNN), 2012.
[11] Ou Lin, Guang-Yuan Liu, Jie-Min Yang and Yang-Ze Du, "Neurophysiological markers of identifying regret by 64 channels EEG signal," 12th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP), 2015.
[12] D. Handayani, H. Yaacob, A. Wahab and I. F. T. Alshaikli, "Statistical Approach for a Complex Emotion Recognition Based on EEG Features," 4th International Conference on Advanced Computer Science Applications and Technologies (ACSAT), 2015.
[13] B. H. Kim and S. Jo , "Deep Physiological Affect Network for the Recognition of Human Emotions," IEEE Transactions on Affective Computing,, 2018 .
[14] S. G. Mangalagowri and P. C. P. Raj, "EEG feature extraction and classification using feed forward backpropogation algorithm for emotion detection," International Conference on Electrical, Electronics, Communication, Computer and Optimization Techniques (ICEECCOT), 2016 .
[15] I. Wichakam and P. Vateekul,, "An evaluation of feature extraction in EEG-based emotion prediction with support vector machines," 11th International Joint Conference on Computer Science and Software Engineering (JCSSE), 2014.
[16] JOSEPH A. MIKELS, BARBARA L. FREDRICKSON, GREGORY R. LARKIN, CASEY M. LINDBERG, SAM J. MAGLIO, PATRICIA A. REUTER-LORENZ, "Emotional category data on images from the International Affective Picture System," Behavior Research Methods , 2005.
[17] Elise S. Dan-Glauser, Klaus R. Scherer, "The Geneva affective picture database (GAPED): a new 730-picture database focusing on valence and normative significance," Behavior Research Methods, 2011.
[18] Artur Marchewka, ?ukasz ?urawski, Katarzyna Jednoróg, Anna Grabowska, "The Nencki Affective Picture System (NAPS): Introduction to a novel, standardized, wide-range, high-quality, realistic picture database," Behavior Research Methods, 2014.
[19] Benedek Kurdi, Shayn Lozano, Mahzarin R. Banaji , "Introducing the Open Affective Standardized Image Set (OASIS)," Behavior research methods, 2017.
[20] R. Jenke, A. Peer and M. Buss, "Feature Extraction and Selection for Emotion Recognition from EEG," IEEE Transactions on Affective Computing, 2014.
[21] J. Kaur and A. Kaur, "A review on analysis of EEG signals," International Conference on Advances in Computer Engineering and Applications, 2015.
[22] Margaret M.Bradley, Peter J.Lang, "Measuring emotion: The self-assessment manikin and the semantic differential," Journal of Behavior Therapy and Experimental Psychiatry, 2002.
[23] M. Rajya Lakshmi, Dr. T. V. Prasad, Dr. V. Chandra Prakash, "Survey on EEG Signal Processing Methods," International Journal of Advanced Research in Computer Science and Software Engineering (IJARCSSE), 2014.
[24] Sunil Kalagi, José Machado, Vitor Carvalho, Filomena Soares, Demétrio Matos, "Brain computer interface systems using non-invasive electroencephalogram signal : A literature review," International conference on engineering technology and innovation (ICE/ITMC), 2017.
[25] S. Vaid, P. Singh and C. Kaur, "EEG Signal Analysis for BCI Interface: A Review," Fifth International Conference on Advanced Computing & Communication Technologies, 2015.
[26] S. H. Kim and N. A. N. Thi, "Feature extraction of emotional states for EEG-based rage control," 39th International Conference on Telecommunications and Signal Processing (TSP), 2016.
[27] A. F. Rabbi, K. Ivanca, A. V. Putnam, A. Musa, C. B. Thaden and R. Fazel-Rezai, "Human performance evaluation based on EEG signal analysis: A prospective review," Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2009.
[28] A. Saidatul, M. P. Paulraj, S. Yaacob and N. F. Mohamad Nasir, "Automated System for Stress Evaluation Based on EEG Signal: A Prospective Review," IEEE 7th International Colloquium on Signal Processing and its Applications, 2011.
[29] X. Zhuang, V. Rozgi? and M. Crystal, "Compact unsupervised EEG response representation for emotion recognition," International Conference on Biomedical and Health Informatics (BHI), 2014.
[30] M. A. B. S. Akhanda, S. M. F. Islam and M. M. Rahman, "Detection of Cognitive State for Brain-Computer Interfaces," International Conference on Electrical Information and Communication Technology (EICT), 2014.
[31] Y. P. Lin and T. P. Jung, "Exploring day-to-day variability in EEG-based emotion classification," IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2014.

Keywords
EEG,Emotion Recognition, Emotion Detection,HMI,BCI