A Novel Approach towards Recognizing Emotions from EEG Signals

Authors

  • Chris Maria Antony  Department of Computer Science & Engineering, MA College of Engineering, India
  • Ani Sunny  Department of Computer Science & Engineering, MA College of Engineering, India

Keywords:

Arousal, Deap, Dominance, DWT, EEG, SVM, Valence

Abstract

Emotion can be recognized using different methods, among which the most commonly used method is emotion recognition from facial expressions. But this cannot be used in an effective way since persons can mimic emotions. Recognition of emotions from EEG is an effective method for emotion recognition since it cannot be manipulated intentionally. The proposed emotion recognition technique uses Support Vector Machine (SVM) for classifying emotions into eight emotional categories. A new emotional model is also proposed using Valence- Arousal- Dominance values. The proposed method is helpful in identifying the emotions of normal persons as well as paralyzed patients. Recognizing emotions of paralyzed patients will help a lot in improving Their Treatment.

References

  1. O.Sourina, Y.Liu, M.K.Nguyen,"Real-time EEG based Emotion Recognition for Music Therapy", Proceedings on J Multimodal User Interfaces, Dec 2011
  2. D.R Chavan, M.S Kumbhar, R.R Chavan, , "The Human Stress Recognition of Brain, using Music Therapy", Proceedings on International Conference on Computer of Power, Energy Information and Communication, 2016.
  3. N. Zaware, T. Rajgure, A. Bhadang and D. D. Sapkal, "Emotion Based Music Player", Proceedings on International Journal of Innovative Research and Development, Vol. 3, 2014.
  4. A. B. Benke, S. S. Jadhav, S. A. Joshi, "Emotion Based Music Player Using Facial Recognition", Proceedings on International Journal of Innovative Research in Computer and Communication Engineering, vol. 5, Issue 2, February 2017.
  5. A. Al-Nafjan, A. Al-Wabil, M. Hosny , Y. Al-Ohali,"Classification of Human Emotions from Electroencephalogram Signal using Deep Neural Network", Proceedings on IJACS, Vol. 8, 2017.
  6. W. N. Dattatray, N. Ashok Kumar, "Emotion Detection Using EEG Signal Analysis", Proceedings on International Journal & Magazine of Engineering , Technology, Management and Research, vol. 3, Issue 9, 2016.
  7. M. Murugappan, R. Nagarajan and S. Yaacob, "Discrete Wavelet Transform Based Selection of Salient EEG Frequency Band for Assessing Human Emotions", Proceedings on Intelligent Signal Processing, September 2011.
  8. C. Petrantonais, "Emotion recognition from EEG using higher order", vol. 14, pp 390- 396 in 2010 IEEE.
  9. Okamura, Shuhei, "The Short Time Fourier Transform and Local Signals", Dissertations, Paper 58, 2011.
  10. Myoung Soo Park and Jin Hee Na et al, "PCA-based feature extraction using class information", IEEE International Conference on Systems, Man and Cybernetics, vol.1, Oct. 2005, pp.341 – 345.
  11. S. Koelstra, C. Muhl, M. Soleymani, J.-S. Lee, A. Yazdani, T. Ebrahimi, T. Pun, A. Nijholt, and I. Patras, "Deap: A database for emotion analysis, using physiological signals", Proceedings on IEEE Transactions on Affective Computing, vol. 3, 2012.
  12. G. G. Molina, T. Tsoneva, and A. Nijholt, "Emotional brain-computer interfaces", Proceedings on 3rd International Conference on Affective Computing and Intelligent Interaction, pp. 138–146, 2009.
  13. Amit Konar, Aruna Chakraborthy, "Toward Affective Brain Computer Interface", Emotion Recognition: A Pattern Analysis Approach, pp. 326-328. January 2015.
  14. Abdulhamit Subasi and Ismail M Gursoy, "EEG Signal Classification Using PCA, ICA, LDA and Support Vector Machines" Elsevier Transactions on Expert Systems with applications, vol. 37, pp.8659-8666, 2010.
  15. Graimann, rendan Allison, and G. Pfurtscheller, "Brain-Computer Interfaces: A Gentle Introduction", in Brain-Computer Interfaces: Revolutionizing Human Computer Interaction, Springer, pp. 1– 27, 2010.
  16. A. Roman-Gonzalez, "EEG Signal Processing for I Applications", Human Computer Systems Interaction: Backgrounds and Applications 2, Advances in Intelligent and Soft Computing, vol. 98, no. 1, pp. 51–72, 2012.
  17. F. Nijboer, S. P. Carmien, E. Leon, F. O. Morin, R. A. Koene, and U.Hoffmann, "Affective Brain-Computer Interfaces: Psychophysiological Markers of Emotion in Healthy Persons and in Persons with Amyotrophic Lateral clerosis", in Affective Computing Intelligent Interaction ACII2009, 2009.

IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

Downloads

Published

2018-02-28

Issue

Volume 3, Issue 1, January-February-2018

Section

Research Articles

License

Copyright (c) IJSRCSEIT

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Chris Maria Antony, Ani Sunny, " A Novel Approach towards Recognizing Emotions from EEG Signals , IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 3, Issue 1, pp.1418-1422, January-February-2018.