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Emotion Recognition Based on Various Physiological Signals-A Review


Affiliations
1 Giesecke and Devrient MS India Private Limited, India
2 Electronics and Telecommunication Engineering, MIT Academy of Engineering, India
     

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Emotion recognition is one of the biggest challenges in human-human and human-computer interaction. There are various approaches to recognize emotions like facial expression, audio signals, body poses, and gestures etc. Physiological signals play vital role in emotion recognition as they are not controllable and are of immediate response type. In this paper, we discuss the research done on emotion recognition using skin conductance, skin temperature, electrocardiogram (ECG), electromyography (EMG), and electroencephalogram (EEG) signals. Altogether, the same methodology has been adopted for emotion recognition techniques based upon various physiological signals. After survey, it is understood that none of these methods are fully efficient standalone but the efficiency can be improved by using combination of physiological signals. The study of this paper provides an insight on the current state of research and challenges faced during emotion recognition using physiological signals, so that research can be advanced for better recognition.

Keywords

Physiological Signals, Skin Conductance, EMG, ECG, EEG.
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  • Tazrin Ahmed, Monira Islam and Mohiuddin Ahmad, “Human Emotion Modeling based on Salient Global Features of EEG Signal”, Proceedings of IEEE International Conference on Advances in Electrical Engineering, pp. 246-251, 2013.

  • Ali S. Almejrad, “Human Emotions Detection using Brain Wave Signals: A Challenging”, European Journal of Scientific Research, Vol. 44, No. 4, pp. 640-659, 2010.

  • Liqin Fu, Xia Mao and Lijiang Chen, “Relative Speech Emotion Recognition based Artificial Neural Network”, Proceedings of IEEE Pacific-Asia Workshop on Computational Intelligence and Industrial Application, pp. 140-144, 2008.

  • Xiaowei Niu, Liwan Chen and Qiang Chen, “Research on Genetic Algorithm based on Emotion Recognition using Physiological Signals”, Proceedings of IEEE International Conference on Robot and Human Interactive Communication, pp. 614-618, 2011.

  • A.N. Paithane, D.S. Bormane and Sneha Dinde, “Electrocardiogram Signal Analysis using Empirical Mode Decomposition and Hilbert Spectrum”, Proceedings of IEEE International Conference on Pervasive Computing, pp. 194-197, 2015.

  • Gouizi, K. Maaoui and C. Reguig, “Negative Emotion Detection using EMG Signal”, Proceedings of IEEE International Conference on Control, Decision and Information Technologies, pp. 690-695, 2014.

  • Chen Defu, Cai Jing and Liu Guangyuan, “Towards Recognizing Two Emotion States from ECG Signals”, Proceedings of IEEE International Conference on Computational Intelligence and Natural Computing, pp. 210-213, 2009.

  • Xianhai Guo, “Research on Emotion Recognition based on Physiological Signal and AuBT”, Proceedings of IEEE International Conference on Consumer Electronics, Communications and Networks, pp. 614-617, 2011.

  • Guanghua Wu, Guangyuan Liu and Min Hao, “The Analysis of Emotion Recognition from GSR Based on PSO”, Proceedings of International Symposium on Intelligence Information Processing and Trusted Computing, pp. 360-363, 2010.

  • Foteini Agrafioti, Dimitrios Hatzinakos and Adam K. Anderson, “ECG Pattern Analysis for Emotion Detection”, IEEE Transactions on Affective Computing, Vol. 3, No. 1, pp. 102-115, 2012.

  • T. Yamakoshi et al., “Feasibility Study on Driver’s Stress Detection from Differential Skin Temperature Measurement”, Proceedings of IEEE International Conference on Engineering in Medicine and Biology Society, pp. 1076-1079, 2008.

  • Teena George, K. Shalu George and K.S. Sivanandan, “Sensing, Processing and Application of EMG signals for HAL (Hybrid Assistive Limb)”, Proceedings of IEEE International Conference on Sustainable Energy and Intelligent Systems, pp. 20-22, 2011.

  • Yu-Hao Wu, Shu-Jing Lin and Don-Lin Yang, “A Mobile Emotion Recognition System based on Speech Signals and Facial Images”, Proceedings of International Conference on Computer Science and Engineering, pp. 212-217, 2013.

  • Zhengji Long, Guangyuan Liu and C. Xuewu Dai, “Extracting Emotional Features from ECG by using Wavelet Transform”, Proceedings of IEEE International Conference on Biomedical Engineering and Computer Science, pp. 1-4, 2010.

  • Xiao-Wei Wang, Dan Nie and Bao-Liang Lu, “EEG-based Emotion Recognition using Frequency Domain Features and Support Vector Machines”, Proceedings of International Conference on Neural Information Processing, pp. 734-743, 2011.

  • Cristian A. Torres-Valencia, Hernan F. Garca-Arias, Mauricio A. Alvarez Lopez and Alvaro A. Orozco-Gutierrez, “Comparative Analysis of Physiological Signals and Electroencephalogram (EEG) for Multimodal Emotion Recognition using Generative Models”, Proceedings of International Conference on Image, Signal Processing and Artificial Vision, pp. 1-5, 2014.

  • C. Maaoui and A. Pruski, “A Comparative Study of SVM Kernel Applied to Emotion Recognition from Physiological Signals”, Proceedings of IEEE International Multi-Conference on Systems, Signals and Device, pp. 1-6, 2008.

  • R. Jenke, “Feature Extraction and Selection for Emotion Recognition from EEG”, IEEE Transactions on Affective Computing, Vol. 5, No. 3, pp. 327-339, 2014.

  • C.A.I. Jing, L.I.U. Guangyuan and H.A.O. Min, “The Research on Emotion Recognition from ECG Signal”, Proceedings of IEEE International Conference on Information Technology and Computer Science, pp. 497-500, 2009.

  • Z. Khalili and M.H. Moradi, “Emotion Detection using Brain and Peripheral Signals”, Proceedings of IEEE International Conference on Biomedical Engineering, pp. 1-4, 2008.

  • Ivan Manuel and Benito Nunez, “EEG Artifact Detection”, Master Thesis, Department of Cybernetics, Czech Technical University of Prague, 2010.

  • Sander Koelstra, Christian Muhl, Mohammad Soleymani, Jong-Seok Lee, Ashkan Yazdani, Touradj Ebrahimi, Thierry Pun, Anton Nijholt and Ioannis Patras, “DEAP: A Database for Emotion Analysis using Physiological Signals”, IEEE Transactions on Affective Computing, Vol. 3, No. 2, pp. 18-31, 2012.

  • Panagiotis C. Petrantonakis and Leontios J. Hadjileontiadis, “Emotion Recognition from EEG using Higher Order Crossings”, IEEE Transactions on Information Technology in Biomedicine, Vol. 14, No. 2, pp. 1-8, 2010.

  • Revathipriya Muthusamy, “Emotion Recognition from Physiological Signals using Bio-Sensors”, Technical Report, Department of Computer Science, University of Fribourg, 2012

  • Surya Cheemalapati, Michael Gubanov, Michael Del Vale and Anna Pyayt, “A Real-Time Classification Algorithm for Emotion Detection using Portable EEG”, Proceedings of IEEE International Conference on Information Reuse and Integration, pp. 720-723, 2013.

  • Y. Gu, S.L. Tan, K.J. Wong, M.H. R. Ho and L. Qu, “Using GA-based Feature Selection for Emotion Recognition from Physiological Signals”, Proceedings of IEEE International Symposium on Intelligent Signal Processing and Communications Systems, pages 1-4, 2008.

  • Aayush Bhardwaj, Ankit Gupta, Pallav Jain, Asha Rani and Jyoti Yadav, “Classification of Human Emotions from EEG signals using SVM and LDA Classifiers”, Proceedings of IEEE International Conference on Signal Processing and Integrated Networks, pp. 180-185, 2015.

  • Khadidja Gouizi, Fethi Bereksi Reguig and Choubeila Maaoui, “Analysis Physiological Signals for Emotion Recognition”, Proceedings of IEEE International Workshop on Systems, Signal Processing and their Applications, pp.147-150, 2011.

  • T. Baby Shalini and L. Vanitha, “Emotion Detection in Human Beings using ECG Signals”, International Journal of Engineering Trends and Technology, Vol. 4, No. 3, pp. 12-18, 2013.

  • Zhengjie Zhou, Huiping Jiang and Xiaoyuan Song, “EEG-based Emotion Recognition using Wavelet Features”, Proceedings of IEEE International Conference on Software Engineering and Service Science, pp. 585-588, 2014.

  • Kazuhiko Takahashi, “Remarks on SVM-based Emotion Recognition from Multi-Modal Bio-Potential Signals”, Proceedings of IEEE International Workshop on Robot and Human Interactive Communication, pp. 95-100, 2004.

  • Reza Khosrowabadi, Chai Quek, Kai Keng Ang and Abdul Wahab, “ERNN: A Biologically Inspired Feed Forward Neural Network to Discriminate Emotion from EEG Signal”, IEEE Transactions on Neural Networks and Learning Systems, Vol. 25, No. 3, pp. 223-233, 2014.

  • Chendi Wang and Feng Wang, “An Emotional Analysis Method based on Heart Rate Variability”, Proceedings of IEEE International Conference on Biomedical and Health Informatics, pp. 1-7, 2012.

  • Chang-wei Ma and Guang-yuan Liu, “Feature Extraction, Feature Selection and Classification from Electrocardiography to Emotions”, Proceedings of IEEE International Conference on Computational Intelligence and Natural Computing, Vol. 1, pp. 190-193, 2009.

  • Guangying Yang and Shanxiao Yang, “Study of Emotion Recognition based on Surface Electromyography and Improved Least Squares Support Vector Machine”, Journal of Computers, Vol. 6, No. 8, pp. 112-129, 2011.

  • Ruo-Nan Duan, Jia-Yi Zhu and Bao-Liang Lu, “Differential Entropy Feature for EEG-based Emotion Classification”, Proceedings of IEEE International Conference on Neural Engineering, pp. 81-84, 2013.

  • Xizhi Zhu, “Emotion Recognition of EMG based on BP Neural Network”, Proceedings of 2nd International Symposium on Networking and Network Security, pp. 1-4, 2010.

  • Somchanok Tivatansakul and Michiko Ohkura, “Emotion Recognition using ECG Signals with Local Pattern Description Methods”, International Journal of Affective Engineering, Vol. 15, No. 2, pp. 51-61, 2016.

  • Sabaa Ahmed Yahya Al-Galal, Imad Fakhri Alshaikhli and Abdul Wahab Abdul Rahman, “Automatic Emotion Recognition based on EEG and ECG Signals While Listening to Quranic Recitation Compared with Listening to Music”, Proceedings of 6th International Conference on Information and Communication Technology for The Muslim World, pp. 1-4, 2016.

  • Mehmet Ali Sarıkaya and Gokhanince, “Emotion Recognition from EEG Signals through One Electrode Device”, Proceedings of IEEE 25th Signal Processing and Communications Applications Conference, pp. 1-6, 2017.


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  • Emotion Recognition Based on Various Physiological Signals-A Review

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Authors

Supriya Londhe
Giesecke and Devrient MS India Private Limited, India
Rushikesh Borse
Electronics and Telecommunication Engineering, MIT Academy of Engineering, India

Abstract


Emotion recognition is one of the biggest challenges in human-human and human-computer interaction. There are various approaches to recognize emotions like facial expression, audio signals, body poses, and gestures etc. Physiological signals play vital role in emotion recognition as they are not controllable and are of immediate response type. In this paper, we discuss the research done on emotion recognition using skin conductance, skin temperature, electrocardiogram (ECG), electromyography (EMG), and electroencephalogram (EEG) signals. Altogether, the same methodology has been adopted for emotion recognition techniques based upon various physiological signals. After survey, it is understood that none of these methods are fully efficient standalone but the efficiency can be improved by using combination of physiological signals. The study of this paper provides an insight on the current state of research and challenges faced during emotion recognition using physiological signals, so that research can be advanced for better recognition.

Keywords


Physiological Signals, Skin Conductance, EMG, ECG, EEG.

References