Nonlinear EEG Processing and Statistical Analysis in the Study of Preference and Liking a Product

Raiesdana, Somayeh; Safari, Samaneh

doi:10.22041/ijbme.2019.103139.1442

Document Type : Full Research Paper

Authors

Somayeh Raiesdana ¹
Samaneh Safari ²

¹ Assistant Professor, Biomedical Engineering Group, Department of Electrical, Biomedical and Mechatronics Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran

² M.Sc. Biomedical Engineering Group, Department of Electrical, Biomedical and Mechatronics Engineering, Qazvin Branch, Islamic Azad University, Qazvin, Iran

https://doi.org/10.22041/ijbme.2019.103139.1442

Abstract

In this study, a neuromarketing project was conducted via EEG signal processing in which the individuals’ interest for buying a relatively luxurious decorative product (which has a relative advantage in exports based on commonly evaluated criteria and indicators in economic) was evaluated. EEG signals of 24 participants during observing and selecting gemstone images were recorded and processed in order to analyze statistical significance of brain activity variations involved in the emotional (liking) and the decision making (choosing) processes. The recorded signals during the stimulation and selection phases were pre-processed in several steps to remove the existing noises and artifacts. Then, the 19-channel EEGs were processed via multiple tools to indicate active brain regions while watching gemstones. Brain mapping and regional analysis indicated that the occipital>frontal>limbic regions were more activated than other regions. Moreover, the left hemisphere has been more active than the right hemisphere. At the next step, nonlinear entropy feature of each signal segment was extracted to be used for training a neurofuzzy system which is an automatic classifier that learns to classify the individuals’ choices. The classification has resulted in 86.25% precision and 87.4% accuracy in a three-class classification task (including two pleasant selections and one unpleasant selection). At the final step, using a questionnaire filled by participants following the recording session, a number of statistical analyses were performed over the self-conscious and unconscious by means of statistical tools including t-test, analysis of variance and regression. The results of statistical tests indicated that there are significant differences for the cognition of liking or preferring among different choices and based on the selections made by women and men. Furthermore, the lack of existence of a significant difference between conscious and unconscious choices were rejected.

Keywords

Main Subjects

Biomedical Signal Processing / Medical Signal Processing / Biosignal Processing

References

[1] N. Lee, A. J. Broderick, L. Chamberlain, “What is ‘neuromarketing’? A discussion and agenda for future research,” International Journal of Psychophysiology, vol. 63, pp.199–204, 2007.

[2] T. Schneider, S. Woolger, “Neuroscience beyond the laboratory: Neuro knowledges, technologies and Markets,” BioSocieties, vol.10, pp.389–399, 2015.

[3] RP. Bagozzi, M. Gopinath, PU. Nyer, “The role of emotions in marketing,” J Acad Market Sci, vol. 27, no. 2, pp.184-206, 1999.

[4] P. Renvoise, C. Morin, “Neuromarketing: Understanding the Buy Buttons in Your Customer's Brain”. Kindle Edition, Nashville, Tenn.: Thomas Nelson; London: New Holland, 2008.

[5] G. Vecchiato, J. Toppi, L, Astolfi, et al., “Spectral EEG frontal asymmetries correlate with the experienced pleasantness of TV commercial advertisements,” Med Biol Eng Comput, vol. 49, pp. 579–583, 2011.

[6] U. Karmarkar, “Note on Neuromarketing. Harvard Business School Background Nore Marketing Unit Case,” 512-031, Sep. 2011.

[7] B. Veronica, “Brief history of neuromarketing,” J Bert Rus, pp.119-121, 2009.

[8] M. Cohen, CE. Elger, C. Ranganath, “Reward expectation modulates feedback-related negativity and EEG spectra. Neuroimage,” vol. 35, no. 2, pp. 968-78, Apr 2007.

[9] M. M. Izadkhah, S. yahyaie, “Analysis of Conscious and Unconscious Animation Audience Behavior in Children Using Neuromarketing,” New Media Studies, vol 2, issue 5, pp. 102-135, 2016.

[10] G. Vecchiato, J. Toppi, L. Astolﬁ , F.V. Fallani, F. Cincotti , D. Mattia , F. Bez , F. Babilo, “Spectral EEG frontal asymmetries correlate with the experienced pleasantness of TV commercial advertisements,” Med Biol Eng Comput, vol. 49:, pp.579–583, 2011.

[11]D. Ariely, G. S. Berns, “Neuromarketing: the hope and hype of neuroimaging in business”, Nat Rev Neurosci. Vol. 11, no. 4, pp. 284–292, April 2010.

[12]T. Nomura, Y. Mitsukura, “Extraction of unconscious emotions while watching TV commercials”, IECON2015-Yokohoma, November 9-12.2015.

[13]A. Telpaz, R. Webb, D. J. Levy, “Using EEG to predict consumers’ future choices,” Journal of Marketing Research, vol. 52, no. 4, pp. 511-529, 2015.

[14]K. Hengsberg, “Neuromarketing-fundamentals and insights for advantageous advertising in a luxury watch context,” Doctoral dissertation, Msc thesis in marketing, Dublin Business School 2015.

[15]S. Raiesdana, “Automated sleep staging of OSAs based on ICA preprocessing and consolidation of temporal correlations,” Australasian Physical & Engineering Sciences in Medicine, vol. 41, pp. 161–176, March 2018.

[16]M. Costa, A. L. Goldberger, C. K. Peng, Multiscale entropy analysis of biological signals, Physical review, vol. 71, 021906-1:18, 2005.

[17]S. M. pincus, “Approximate entropy as a measure of system complexity,” Proceedings of the National Academy of Sciences of the United States of America, vol. 88, no. 6, pp.2297-2301,1991.

[18]S. Heykin, “Neural network, a comprehensive foundation”. 1thed. John Wiley & Sons, New York, 1999.

[19]R. Christensen,”Analysis of Variance, Design and Regression, Applied Statistical method,” Chapman & Hall/CRC Texts in Statistical Science, Chapman and Hall/CRC; 1 edition (June 1, 1996.

[20]https://sccn.ucsd.edu/eeglab/index.php

[21]Delorme, J. Palmer, R. Oostenveld, J. Onton, S. Makeig, “Independent EEG components are dipolar”, PLos One. doi.org/10.1371/journal.pone.0030135, Feb. 2012.

[22]CM. Held, JE. Heiss, PA. Estévez, CA. Perez, M. Garrido, C. Algarín, P. Peirano, “Extracting fuzzy rules from polysomnographic recordings for infant sleep classification”. IEEE Trans Biomed Eng, vol. 53, no. 10, pp. 1954-1962, 2006.

[23]M. Yadava, P. Kumar, R. Saini, P. Pratim Roy, D. Prosad Dogra, “Analysis of EEG signals and its application to neuromarketing Analysis of EEG signals and its application to neuromarketing”, Multimed Tools Appl, DOI 10.1007/s11042-017-4580-6.

[24]M. Yadava, P. Kumar, R. Saini, P. Pratim Roy, D. Prosad Dogra, “Analysis of EEG signals and its application to neuromarketing Analysis of EEG signals and its application to neuromarketing”, Multimed Tools Appl, DOI 10.1007/s11042-017-4580-6.

[25] B. Yılmaza, S. Korkmazb, D. B. Arslanb, E. Güngörb, M. H. Asyalı, “Like/dislike analysis using EEG: Determination ofmost discriminative channels and frequencies”, Computer methods and programs in biomedicine, vol. 1 1 3, pp. 705–713, 2014.

[26]R. Ohme, D. Reykowska, D. Wiener, et al. “Application of frontal EEG asymmetry to advertising research”, Journal of Economic Psychology vol. 31, no. 5, pp. 758-793, 2010.

[27]C. Molin, “A Neuromarketing research: investigating consumers’ self-declared and biometric emotional responses to products”, Master Thesis, Management Engineering Department, Politecnico di Milano university, 2015.

Iranian Journal of Biomedical Engineering

Nonlinear EEG Processing and Statistical Analysis in the Study of Preference and Liking a Product

References

References

Volume 13, Issue 2
August 2019
Pages 117-134

Nonlinear EEG Processing and Statistical Analysis in the Study of Preference and Liking a Product

References

References

Volume 13, Issue 2August 2019Pages 117-134

Volume 13, Issue 2
August 2019
Pages 117-134