A review on: recognition of human emotions based on the ananlysis of EEG Physiological Signal

Author Affiliations

  • 1Department of ETC, Bhilai Institute of Technology, Durg, CG, India
  • 2Department of ETC, Bhilai Institute of Technology, Durg, CG, India

Res. J. Engineering Sci., Volume 6, Issue (7), Pages 33-38, July,26 (2017)

Abstract

Emotions are the state of mind and behavioural approach individuals use to influence their own emotional expertise It is the inclusive term to individual, attentive practice that is described mainly by psycho physiological expression, mental states, biological reaction. Emotions are combining and express accordingly with mood, activity, temperature, nature and personality. In reasonable decision making and determined action emotions play the important role. Emotions give the ability to deal with unplanned occasion in our background which helps to increase our chance of survival. Physiological signal composes vital signals in the human body. In particular to, to identify human emotions several physiologic signals have been used widely these signals are collected from Electroencephalogram, Electrocardiogram, Electromyogram, Respiratory system, Electrodermal activities, Muscular system and Brain activity. The purpose of this study is to recognize the mental emotional state of a human body by using EEG signal, which recognize the human emotions. This study would provide a deep view on current state of the research and require on emotion recognition based on the analysis of EEG physiological signals.

References

  1. Ekman E. and Davidson R.J. (1994)., The nature of emotions, fundamental questions., Oxford: Oxford University Press, USA., 1-512, ISBN-10: 0195089448.
  2. Scherer K.R. (2004)., Which emotions can be induced by music? What are the underlying mechanisms? And how can we measure them?., J New Music Res., 33(3), 239-251.
  3. Arafat A. and Hasan K. (2009)., Automatic Detection of ECG wave Boundaries using Empirical Mode Decomposition., Proc. IEEE Int’l Conf. Acoustics, Speech and Signal Processing, 461-464.
  4. Frijda N. (1986)., The emotions. Cambridge University Press Cambridge., UK Google Scholar, 1-544, ISBN: 0521301556.
  5. Drummond P.D. and Quah S-H. (2001)., The effect of expressing anger on cardiovascular reactivity and facial blood flow in Chinese and Caucasians., Psychophysiology, 38(2), 190-196.
  6. Alaoui-Ismaili O., Robin O., Rada H., Dittmar André and Vernet-Maury Evelyne (1997)., Basic emotions evoked by odorants: comparison between autonomic responses and self-evaluation., Physiology and Behaviour, 62(4), 713-720.
  7. Ax A.F. (1953)., The physiological differentiation between fear and anger in humans., Psychosomatic Medicine, 15(5), 433-442.
  8. Jang Eun-Hye, Park Byoung-Jun, Park Mi-Sook, Kim Sang-Hyeob and Sohn Jin-Hun (2015)., Analysis of physiological signals for recognition of boredom, pain, and surprise emotions., Journal of Physiological Anthropology, 34, 25.
  9. Maaoui Choubeila And Pruski Alain (2010)., Emotion Recognition through Physiological Signals for Human Machine Communication., INTECH Open Access Publisher, France, 317-332. ISBN: 9533070625.
  10. Eun-Hye Jang and Byoung-Jun Park (2013)., Classification of Human Emotions from Physiological signals using Machine Learning Algorithms., The Sixth International Conference on Advances in Computer-Human Interactions. Nice, France. 24th February – 1st March., 395-400.
  11. Sanei Saeid and Chambers J.A. (2007)., EEG Signal Processing., John Wiley & Sons, 1-312, ISBN: 978-0-470-02581-9
  12. Baby shalini T. and Vanitha L. (2013)., Emotion Detection in Human Beings Using ECG Signals., International Journal of Engineering Trends and Technology, 4(5), 1337-1342.
  13. Bradley M.M. and Lang P.J. (2008)., International Affective Digitized Sounds (IADS)., Stimuli, Instruction Manual and Affective Ratings. The Center for Research in Psychophysiology, University of Florida, Gainesville, FL, USA, 40, 784-790.
  14. Bradley M.M. and Lang P.J. (1994)., Measuring emotion: the self-assessment manikin and the semantic differential., Journal of Behavioral Therapy and Experimental Psychiatry, 25(1), 49-59.
  15. Mallat S.A. (1989)., A Theory for Multi resolution Signal Decomposition The wavelet Representation., IEEE Transactions on Pattern Analysis And Machine Intelligence, 11(7), 674-693.
  16. Chanel Guillaume, Kierkels Joep J.M., Soleymani Mohammad and Pun Thierry (2009)., Short term Emotion assessment in a recall paradigm., International Journal of Human- Computer Studies, 67(8), 607-627.
  17. Okamura Shuhei (2011)., The Short Time Fourier Transform and Local Signals., Dissertations. A Study, Doctorate Thesis. Departent of statistics Carnegie Mellon University Pittsburgh, Pennsylvanian. 1-58.
  18. Wasserman P.D. (1993)., Advanced methods in neural computing., New York: Van Nostrand Reinhold., 1-250. ISBN:0442004613.
  19. Li C., Diao Y., Ma H. and Li Y. (2008)., A Statistical PCA Method for Face Recognition., Intelligent Information Technology Application, 3, 376-380.
  20. Wang Z. and Li X. (2010)., Face Recognition Based on Improved PCA Reconstruction., Intelligent Control and Automation (WCICA), 8th World Congress on, Jinan, China, 7-9 July, 6272-6276.
  21. Duda R.O., Hart P.E. and Stork D.G. (2010)., Pattern classification., 2nd edition. New York, NY: Wiley-Interscience, 1-635, ISBN: 0471056693.
  22. Zhao Zhiqiang and Zhang Huiquan (2015)., The methodology of ECG feature extraction based on empirical mode decomposition., Biomedical Research Center, Chongqing University of Posts and Telecommunications, Chongqing, China. Journal of Chemical and Pharmaceutical Research, 7(3), 321-324.
  23. Lang P.J., Bradley M.M. and Cuthbert B.N. (2005)., International Affective Picture System (IAPS): Affective Ratings of Pictures and Instruction Manual., Technical report A-8. The Center for Research in Psychophysiology, University of Florida, Gainesville, FL, USA.
  24. Chanel Guillaume, Kronegg Julien, Grandjean Didier and Pun Thierry (2006)., Emotion assessment: Arousal evaluation using eegs and peripheral physiological signals., International Workshop, MRCS, Istanbul, Turkey. 11-13 September, 530-537.
  25. Petrantonakis P.C. and Hadjileontiadis L.J. (2009)., Emotion Recognition from Brain Signals Using Hybrid Adaptive Filtering and Higher Order Crossings Analysis., IEEE Transactions on affective computing, 1(2), 81-97.
  26. Kumari Pinki and Vaish Abhishek (2015)., Brainwave based user identification system: A pilot study in robotics environment., Robotics and Autonomous Systems, 65, 15-23.