Selection of the Most Relevant Physiological Features for Classifying Emotion

C. Godin, F. Prost-Boucle, A. Campagne, S. Charbonnier, S. Bonnet, A. Vidal

2015

Abstract

With the development of wearable physiological sensors, emotion estimation becomes a hot topic in the literature. Databases of physiological signals recorded during emotional stimulation are acquired and machine learning algorithms are used. Yet, which are the most relevant signals to detect emotions is still a question to be answered. In order to better understand the contribution of each signal, and thus sensor, to the emotion estimation problem, several feature selection algorithms were implemented on two databases freely available to the research community (DEAP and MANHOB-HCI). Both databases manipulate emotions by showing participants short videos (video clips or part of movies respectively). Features extracted from Galvanic Skin response were found to be relevant for arousal estimation in both databases. Other relevant features were eye closing rate for arousal, variance of zygomatic EMG for valence (those features being only available for DEAP). The hearth rate variability power in three frequency bands also appeared to be very relevant, but only for MANHOB-HCI database where heat rate was measured using ECG (whereas DEAP used PPG). This suggests that PPG is not accurate enough to estimate HRV precisely. Finally we showed on DEAP database that emotion classifiers need just a few well selected features to obtain similar performances to literature classifiers using more features.

References

  1. André, E., Rehm, M., Minker, W., Bühler, D., 2004. Endowing spoken language dialogue systems with emotional intelligence, in: proceedings affective dialogue systems 2004. Springer, pp. 178-187.
  2. Chanel, G., Ansari-Asl, K., Pun, T., 2007. Valence-arousal evaluation using physiological signals in an emotion recall paradigm, in: Systems, Man and Cybernetics, 2007. ISIC. IEEE International Conference on. pp. 2662-2667.
  3. Cover, T. M., Thomas, J. A., 2012. Elements of information theory. John Wiley & Sons.
  4. Ekman, P., 2005. Basic Emotions. Psychol. Rev. - PSYCHOL REV 99, 45 - 60.
  5. Ekman, P., Levenson, R. W., Friesen, W. V., 1983. Autonomic nervous system activity distinguishes among emotions. Science 221, 1208-1210.
  6. Ertin, E., Stohs, N., Kumar, S., Raij, A., al' Absi, M., Shah, S., 2011. AutoSense: Unobtrusively Wearable Sensor Suite for Inferring the Onset, Causality, and Consequences of Stress in the Field, in: Proceedings of the 9th ACM Conference on Embedded Networked Sensor Systems, SenSys 7811. ACM, New York, NY, USA, pp. 274-287.
  7. Fleureau, J., Guillotel, P., Huynh-Thu, Q., 2012. Physiological-Based Affect Event Detector for Entertainment Video Applications. IEEE Trans. Affect. Comput. 3, 379-385.
  8. Gaggioli, A., Pallavicini, F., Morganti, L., Serino, S., Scaratti, C., Briguglio, M., Crifaci, G., Vetrano, N., Giulintano, A., Bernava, G., Tartarisco, G., Pioggia, G., Raspelli, S., Cipresso, P., Vigna, C., Grassi, A., Baruffi, M., Wiederhold, B., Riva, G., 2014. Experiential Virtual Scenarios With Real-Time Monitoring (Interreality) for the Management of Psychological Stress: A Block Randomized Controlled Trial. J. Med. Internet Res. 16, e167.
  9. Gini, C., 1912. Variabilite e mutabilita (Italian). Mem. Metodol. Stat.
  10. Guyon, I., Elisseeff, A., 2003. An introduction to variable and feature selection. J. Mach. Learn. Res. 3, 1157- 1182.
  11. Hall, M. A., Smith, L. A., 1999. Feature Selection for Machine Learning: Comparing a Correlation-Based Filter Approach to the Wrapper., in: FLAIRS Conference. pp. 235-239.
  12. Healey, J. A., 2000. Wearable and automotive systems for affect recognition from physiology (Thesis). Massachusetts Institute of Technology.
  13. Healey, J. A., Picard, R.W., 2005. Detecting stress during real-world driving tasks using physiological sensors. IEEE Trans. Intell. Transp. Syst. 6, 156-166.
  14. Healey, J., Picard, R.W., 2002. Eight-emotion Sentics Data, MIT Affective Computing Group.
  15. Janecek, A., Gansterer, W.N., Demel, M., Ecker, G., 2008. On the Relationship Between Feature Selection and Classification Accuracy., in: FSDM. Citeseer, pp. 90- 105.
  16. Koelstra, S., Muhl, C., Soleymani, M., Lee, J.-S., Yazdani, A., Ebrahimi, T., Pun, T., Nijholt, A., Patras, I., 2012. Deap: A database for emotion analysis; using physiological signals. Affect. Comput. IEEE Trans. On 3, 18-31.
  17. Kreibig, S. D., 2010. Autonomic nervous system activity in emotion: A review. Biol. Psychol., The biopsychology of emotion: Current theoretical and empirical perspectives 84, 394-421.
  18. Lang, P. J., Greenwald, M. K., Bradley, M. M., Hamm, A.O., 1993. Looking at pictures: Affective, facial, visceral, and behavioral reactions. Psychophysiology 30, 261-273.
  19. Liu, H., Setiono, R., 1995. Chi2: Feature selection and discretization of numeric attributes, in: 2012 IEEE 24th International Conference on Tools with Artificial Intelligence. IEEE Computer Society, pp. 388-388.
  20. Mauss, I. B., Robinson, M. D., 2009. Measures of emotion: A review. Cogn. Emot. 23, 209-237.
  21. Picard, R.W., Vyzas, E., Healey, J., 2001. Toward machine emotional intelligence: analysis of affective physiological state. IEEE Trans. Pattern Anal. Mach. Intell. 23, 1175-1191.
  22. Posner, J., Russell, J. A., Peterson, B. S., 2005. The circumplex model of affect: An integrative approach to affective neuroscience, cognitive development, and psychopathology. Dev. Psychopathol. 17, 715-734.
  23. Roy, R. N., Charbonnier, S., Bonnet, S., 2014. Eye blink characterization from frontal EEG electrodes using source separation and pattern recognition algorithms. Biomed. Signal Process. Control 14, 256-264.
  24. Saeys, Y., Inza, I., Larrañaga, P., 2007. A review of feature selection techniques in bioinformatics. bioinformatics 23, 2507-2517.
  25. Soleymani, M., Lichtenauer, J., Pun, T., Pantic, M., 2012. A Multimodal Database for Affect Recognition and Implicit Tagging. IEEE Trans. Affect. Comput. 3, 42- 55.
  26. Wei, Z.-P., Lu, B.-L., 2012. Online vigilance analysis based on electrooculography, in: The 2012 International Joint Conference on Neural Networks (IJCNN). Presented at the The 2012 International Joint Conference on Neural Networks (IJCNN), pp. 1-7.
  27. Wilhelm, F. H., Grossman, P., 2010. Emotions beyond the laboratory: Theoretical fundaments, study design, and analytic strategies for advanced ambulatory assessment. Biol. Psychol. 84, 552-569.
  28. Yannakakis, G. N., Isbister, K., Paiva, A., Karpouzis, K., 2014. Guest Editorial: Emotion in Games. IEEE Trans. Affect. Comput. 5, 1-2.
  29. Yu, L., Liu, H., 2003. Feature selection for highdimensional data: A fast correlation-based filter solution, in: ICML. pp. 856-863.
  30. Zeng, Z., Pantic, M., Roisman, G.I., Huang, T.S., 2009. A Survey of Affect Recognition Methods: Audio, Visual, and Spontaneous Expressions. IEEE Trans. Pattern Anal. Mach. Intell. 31, 39-58.
  31. Zhao, Z., Morstatter, F., Sharma, S., Alelyani, S., Anand, A., Liu, H., 2010. Advancing feature selection research. ASU Feature Sel. Repos.
Download


Paper Citation


in Harvard Style

Godin C., Prost-Boucle F., Campagne A., Charbonnier S., Bonnet S. and Vidal A. (2015). Selection of the Most Relevant Physiological Features for Classifying Emotion . In Proceedings of the 2nd International Conference on Physiological Computing Systems - Volume 1: PhyCS, ISBN 978-989-758-085-7, pages 17-25. DOI: 10.5220/0005238600170025


in Bibtex Style

@conference{phycs15,
author={C. Godin and F. Prost-Boucle and A. Campagne and S. Charbonnier and S. Bonnet and A. Vidal},
title={Selection of the Most Relevant Physiological Features for Classifying Emotion},
booktitle={Proceedings of the 2nd International Conference on Physiological Computing Systems - Volume 1: PhyCS,},
year={2015},
pages={17-25},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005238600170025},
isbn={978-989-758-085-7},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 2nd International Conference on Physiological Computing Systems - Volume 1: PhyCS,
TI - Selection of the Most Relevant Physiological Features for Classifying Emotion
SN - 978-989-758-085-7
AU - Godin C.
AU - Prost-Boucle F.
AU - Campagne A.
AU - Charbonnier S.
AU - Bonnet S.
AU - Vidal A.
PY - 2015
SP - 17
EP - 25
DO - 10.5220/0005238600170025