Advertisement

Odor Pleasantness Classification from Electroencephalographic Signals and Emotional States

  • M. A. Becerra
  • E. Londoño-Delgado
  • S. M. Pelaez-Becerra
  • L. Serna-Guarín
  • A. E. Castro-Ospina
  • D. Marin-Castrillón
  • D. H. Peluffo-Ordóñez
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 885)

Abstract

Odor identification refers to the capability of the olfactory sense for discerning odors. The interest in this sense has grown over multiple fields and applications such as multimedia, virtual reality, marketing, among others. Therefore, objective identification of pleasant and unpleasant odors is an open research field. Some studies have been carried out based on electroencephalographic signals (EEG). Nevertheless, these can be considered insufficient due to the levels of accuracy achieved so far. The main objective of this study was to investigate the capability of the classifiers systems for identification pleasant and unpleasant odors from EEG signals. The methodology applied was carried out in three stages. First, an odor database was collected using the signals recorded with an Emotiv Epoc+ with 14 channels of electroencephalography (EEG) and using a survey for establishing the emotion levels based on valence and arousal considering that the odor induces emotions. The registers were acquired from three subjects, each was subjected to 10 different odor stimuli two times. The second stage was the feature extraction which was carried out on 5 sub-bands \(\delta \), \(\theta \), \(\alpha \), \(\beta \), \(\gamma \) of EEG signals using discrete wavelet transform, statistical measures, and other measures such as area, energy, and entropy. Then, feature selection was applied based on Rough Set algorithms. Finally, in the third stage was applied a Support vector machine (SVM) classifier, which was tested with five different kernels. The performance of classifiers was compared using k-fold cross-validation. The best result of 99.9% was achieved using the linear kernel. The more relevant features were obtained from sub-bands \(\beta \) and \(\alpha \). Finally, relations among emotion, EEG, and odors were demonstrated.

Keywords

Electroencephalographic signal Emotion Odor pleasantness Sensorial stimuli Signal processing 

References

  1. 1.
    Aydemir, O.: Olfactory recognition based on eeg gamma-band activity. Neural Comput. 29(6), 1667–1680 (2017)CrossRefGoogle Scholar
  2. 2.
    Barrett, L.F.: How Emotions Are Made: The Secret Life of the Brain. Mariner Books, Boston (2017)Google Scholar
  3. 3.
    Beck, A.T., Steer, R.A., Brown, G.K.: BDI-II, Beck Depression Inventory : Manual. 2nd edn (1996)Google Scholar
  4. 4.
    Burges, C.J.: A tutorial on support vector machines for pattern recognition. Data Min. Knowl. Disc. 2(2), 121–167 (1998).  https://doi.org/10.1023/A:1009715923555CrossRefGoogle Scholar
  5. 5.
    Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995).  https://doi.org/10.1023/A:1022627411411CrossRefzbMATHGoogle Scholar
  6. 6.
    Giraldo, E., Acosta, C.D., Castellanos-Domínguez, G.: Estimación dinámica neuronal a partir de señales electroencefalográficas sobre un modelo realista de la cabeza. Tecno Lógicas, no. 25 (2010)Google Scholar
  7. 7.
    Julian, L.J.: Measures of anxiety: state-trait anxiety inventory (STAI), beck anxiety inventory (BAI), and hospital anxiety and depression scale-anxiety (HADS-A). Arthritis Care Res. 63(Suppl 11), S467–S472 (2011).  https://doi.org/10.1002/acr.20561CrossRefGoogle Scholar
  8. 8.
    Khalid, M.B., Rao, N.I., Rizwan-i Haque, I., Munir, S., Tahir, F.: Towards a brain computer interface using wavelet transform with averaged and time segmented adapted wavelets. In: 2009 2nd International Conference on Computer, Control and Communication, pp. 1–4. IEEE, February 2009.  https://doi.org/10.1109/IC4.2009.4909189
  9. 9.
    Koelstra, S., et al.: DEAP: a database for emotion analysis; using physiological. Signals (2012).  https://doi.org/10.1109/T-AFFC.2011.15
  10. 10.
    Kroupi, E., Sopic, D., Ebrahimi, T.: Non-linear EEG features for odor pleasantness recognition. In: 2014 Sixth International Workshop on Quality of Multimedia Experience (QoMEX), pp. 147–152. IEEE (2014)Google Scholar
  11. 11.
    Min, B.C., et al.: Analysis of mutual information content for EEG responses to odor stimulation for subjects classified by occupation. Chem. Senses 28(9), 741–749 (2003)CrossRefGoogle Scholar
  12. 12.
    Mori, K., Manabe, H.: Unique characteristics of the olfactory system. In: Mori, K. (ed.) The Olfactory System, pp. 1–18. Springer, Tokyo (2014).  https://doi.org/10.1007/978-4-431-54376-3_1CrossRefGoogle Scholar
  13. 13.
    Murray, N., Ademoye, O.A., Ghinea, G., Qiao, Y., Muntean, G.M., Lee, B.: Olfactory-enhanced multimedia video clips datasets. In: 2017 Ninth International Conference on Quality of Multimedia Experience (QoMEX), pp. 1–5. IEEE (2017)Google Scholar
  14. 14.
    Nakamura, T., Tomita, Y., Ito, S.i., Mitsukura, Y.: A method of obtaining sense of touch by using EEG. In: 2010 IEEE International Conference on RO-MAN, pp. 276–281. IEEE (2010)Google Scholar
  15. 15.
    Namazi, H., Akrami, A., Nazeri, S., Kulish, V.V.: Analysis of the influence of complexity and entropy of odorant on fractal dynamics and entropy of EEG signal. In: BioMed Research International 2016 (2016)Google Scholar
  16. 16.
    Orrego, D., Becerra, M., Delgado-Trejos, E.: Dimensionality reduction based on fuzzy rough sets oriented to ischemia detection. In: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS (2012).  https://doi.org/10.1109/EMBC.2012.6347186
  17. 17.
    Ortega-Adarme, M., Moreno-Revelo, M., Peluffo-Ordoñez, D.H., Marín Castrillon, D., Castro-Ospina, A.E., Becerra, M.A.: Analysis of motor imaginary BCI within multi-environment scenarios using a mixture of classifiers. In: Solano, A., Ordoñez, H. (eds.) CCC 2017. CCIS, vol. 735, pp. 511–523. Springer, Cham (2017).  https://doi.org/10.1007/978-3-319-66562-7_37CrossRefGoogle Scholar
  18. 18.
    Puchala, E., Krysmann, M.: An algorithm for detecting the instant of olfactory stimulus perception, using the EEG signal and the Hilbert-Huang transform. In: Kurzynski, M., Wozniak, M., Burduk, R. (eds.) CORES 2017. AISC, vol. 578, pp. 499–505. Springer, Cham (2018).  https://doi.org/10.1007/978-3-319-59162-9_52CrossRefGoogle Scholar
  19. 19.
    Russell, M.J.: Alpha blocking and digital filtering improve olfactory evoked potentials. In: 1991 Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, vol. 13, pp. 535–536. IEEE (1991)Google Scholar
  20. 20.
    Saha, A., Konar, A., Bhattacharya, B.S., Nagar, A.K.: EEG classification to determine the degree of pleasure levels in touch-perception of human subjects. In: 2015 International Joint Conference on Neural Networks (IJCNN), pp. 1–8. IEEE (2015)Google Scholar
  21. 21.
    Saha, A., Konar, A., Rakshit, P., Ralescu, A.L., Nagar, A.K.: Olfaction recognition by EEG analysis using differential evolution induced Hopfield neural net. In: The 2013 International Joint Conference on Neural Networks (IJCNN), pp. 1–8. IEEE, August 2013.  https://doi.org/10.1109/IJCNN.2013.6706874, http://ieeexplore.ieee.org/document/6706874/
  22. 22.
    Schriever, V.A., Han, P., Weise, S., Hösel, F., Pellegrino, R., Hummel, T.: Time frequency analysis of olfactory induced EEG-power change. PloS One 12(10), e0185596 (2017)CrossRefGoogle Scholar
  23. 23.
    Siegel, E.H., et al.: Emotion fingerprints or emotion populations? A meta-analytic investigation of autonomic features of emotion categories. Psychol. Bull. 144(4), 343–393 (2018).  https://doi.org/10.1037/bul0000128CrossRefGoogle Scholar
  24. 24.
    Yazdani, A., Kroupi, E., Vesin, J.M., Ebrahimi, T.: Electroencephalogram alterations during perception of pleasant and unpleasant odors. In: 2012 Fourth International Workshop on Quality of Multimedia Experience (QoMEX), pp. 272–277. IEEE (2012)Google Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • M. A. Becerra
    • 1
  • E. Londoño-Delgado
    • 2
  • S. M. Pelaez-Becerra
    • 2
  • L. Serna-Guarín
    • 3
  • A. E. Castro-Ospina
    • 3
  • D. Marin-Castrillón
    • 3
  • D. H. Peluffo-Ordóñez
    • 4
  1. 1.Institución Universitaria Pascual BravoMedellínColombia
  2. 2.Institución Universitaria Salazar y HerreraMedellínColombia
  3. 3.Instituto Tecnológico MetropolitanoMedellínColombia
  4. 4.SDAS Research Group, Yachay TechUrcuquíEcuador

Personalised recommendations