Skip to main content

Advertisement

SpringerLink
Log in

Consumers’ Preference Recognition Based on Brain–Computer Interfaces: Advances, Trends, and Applications

  • Research Article-Computer Engineering and Computer Science
  • Published:
Arabian Journal for Science and Engineering Aims and scope Submit manuscript

Abstract

Brain–computer interface (BCI) technology used to monitor conscious-brain electrical activity via electroencephalogram (EEG) signals has facilitated the detection of human preferences. Recently, great progress has been made in the development of novel paradigms and methods for EEG-based preference detection including attempts to apply BCI research findings in various contexts. Advances in BCI technology have increased the scientists’ interest in possible practical applications of BCI technology involving a human–machine interaction. A major objective of our research was to provide an overview of recent advances in EEG-based preference detection applications. As a foundation for our research, we reviewed previous studies in EEG-based neuromarketing and classified them according to their practical domains, research directions, and recording modalities. Another major research goal was to investigate how the analysis of EEG signals using classification algorithms can be applied to recognize and understand consumers’ mental states and preferences patterns. To this end, we built three different classification algorithms: the random forest (RF), support vector machine (SVM), and the k-nearest neighbor (KNN). Our results demonstrate that RF is more accurate than either KNN or SVM.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  1. Aldayel, M.; Ykhlef, M.; Al-Nafjan, A.: Deep learning for EEG-based preference classification in neuromarketing. Appl. Sci. 10(4), 1 (2020). https://doi.org/10.3390/app10041525

    Article  Google Scholar 

  2. Ramsøy, T.Z.; Friis-Olivarius, M.; Jacobsen, C.; Jensen, S.B.; Skov, M.: Effects of perceptual uncertainty on arousal and preference across different visual domains. J. Neurosci. Psychol. Econ. 5(4), 212 (2012). https://doi.org/10.1037/a0030198

    Article  Google Scholar 

  3. Daugherty, T.; Hoffman, E.; Kennedy, K.: Research in reverse: Ad testing using an inductive consumer neuroscience approach. J. Bus. Res. 69(8), 3168 (2016). https://doi.org/10.1016/j.jbusres.2015.12.005

    Article  Google Scholar 

  4. Teo, J.; Chew, L.H.; Chia, J.T.; Mountstephens, J.: Classification of affective states via EEG and deep learning. Int. J. Adv. Comput. Sci. Appl. 9(5), 132 (2018)

    Google Scholar 

  5. Khushaba, R.N.; Greenacre, L.; Kodagoda, S.; Louviere, J.; Burke, S.; Dissanayake, G.: Choice modeling and the brain: a study on the Electroencephalogram (EEG) of preferences. Expert Syst. Appl. 39(16), 12378 (2012). https://doi.org/10.1016/j.eswa.2012.04.084

    Article  Google Scholar 

  6. Alvino, L.; Constantinides, E.; Franco, M.: Towards a better understanding of consumer behavior: marginal utility as a parameter in neuromarketing research. Int. J. Mark. Stud. 10(1), 90 (2018). https://doi.org/10.5539/ijms.v10n1p90

    Article  Google Scholar 

  7. Al-Nafjan, A.; Hosny, M.; Al-Ohali, Y.; Al-Wabil, A.: Review and classification of emotion recognition based on EEG brain–computer interface system research: a systematic review. Appl. Sci. 7(12), 1239 (2017). https://doi.org/10.3390/app7121239

    Article  Google Scholar 

  8. Ohme, R.; Reykowska, D.; Wiener, D.; Choromanska, A.: Application of frontal EEG asymmetry to advertising research. J. Econ. Psychol. 31((5, SI)), 785 (2010). https://doi.org/10.1016/j.joep.2010.03.008

    Article  Google Scholar 

  9. Plassmann, H.; Venkatraman, V.; Huettel, S.; Yoon, C.: Consumer neuroscience: applications, challenges, and possible solutions. J. Mark. Res. 52(4), 427 (2015)

    Article  Google Scholar 

  10. Riedl, R.; Léger, P.M.: Fundamentals of NeuroIS: Information Systems and the Brain (2016). https://doi.org/10.1007/978-3-662-45091-8

  11. Dimoka, A.; Pavlou, P.A.; Davis, F.D.: NeuroIS: the potential of cognitive neuroscience for information systems research. Inf. Syst. Res. 22(4), 687 (2011). https://doi.org/10.1287/isre.1100.0284

    Article  Google Scholar 

  12. Agarwal, S.; Dutta, T.: Neuromarketing and consumer neuroscience: current understanding and the way forward. Decision 42(4), 457 (2015). https://doi.org/10.1007/s40622-015-0113-1

    Article  Google Scholar 

  13. Murugappan, M.; Murugappan, S.; Balaganapathy; Gerard, C.: Wireless EEG signals based Neuromarketing system using Fast Fourier Transform (FFT). In: 2014 IEEE 10th International Colloquium on Signal Processing and its Applications Applications, pp. 25–30. IEEE (2014). https://doi.org/10.1109/CSPA.2014.6805714

  14. Lin, M.H.J.; Cross, S.N.N.; Jones, W.J.; Childers, T.L.: Applying EEG in consumer neuroscience. Eur. J. Mark. 52(1–2), 66 (2018). https://doi.org/10.1108/EJM-12-2016-0805

    Article  Google Scholar 

  15. Ma, Q.; Wang, K.; Wang, X.; Wang, C.; Wang, L.: The influence of negative emotion on brand extension as reflected by the change of N2: a preliminary study. Neurosci. Lett. 485(3), 237 (2010). https://doi.org/10.1016/j.neulet.2010.09.020

    Article  Google Scholar 

  16. Wang, X.; Ma, Q.; Wang, C.: N400 as an index of uncontrolled categorization processing in brand extension. Neurosci. Lett. 525(1), 76 (2012). https://doi.org/10.1016/j.neulet.2012.07.043

    Article  Google Scholar 

  17. Jones, W.J.; Childers, T.L.; Jiang, Y.: The shopping brain: math anxiety modulates brain responses to buying decisions. Biol. Psychol. 89(1), 201 (2012). https://doi.org/10.1016/j.biopsycho.2011.10.011

    Article  Google Scholar 

  18. Venkatraman, V.; Dimoka, A.; Pavlou, P.A.; Vo, K.; Hampton, W.; Bollinger, B.; Hershfield, H.; Ishihara, M.; Winer, R.S.: Predicting advertising success beyond traditional measures: new insights from neurophysiological methods and market response modeling. J. Mark. Res. 52(4), 436 (2015). https://doi.org/10.2139/ssrn.2498095

  19. Venkatraman, V.; Clithero, J.A.; Fitzsimons, G.J.; Huettel, S.A.: New scanner data for brand marketers: how neuroscience can help better understand differences in brand preferences. J. Consum. Psychol. 22((1, SI)), 143 (2012). https://doi.org/10.1016/j.jcps.2011.11.008

    Article  Google Scholar 

  20. Hakim, A.; Klorfeld, S.; Sela, T.; Friedman, D.; Shabat-Simon, M.; Levy, D.J.: Pathways to consumers minds: using machine learning and multiple EEG metrics to increase preference prediction above and beyond traditional measurements. bioRxiv (2018). https://doi.org/10.1101/317073

    Article  Google Scholar 

  21. Boksem, M.A.S.; Smidts, A.: Brain responses to movie trailers predict individual preferences for movies and their population-wide commercial success. J. Mark. Res. 52(4), 482 (2015)

    Article  Google Scholar 

  22. Hadjidimitriou, S.K.; Hadjileontiadis, L.J.: Toward an EEG-based recognition of music liking using time-frequency analysis. IEEE Trans. Biomed. Eng. 59(12), 3498 (2012). https://doi.org/10.1109/TBME.2012.2217495

    Article  Google Scholar 

  23. Ait Hammou, K.; Galib, M.H.; Melloul, J.: The contributions of neuromarketing in marketing research. J. Manag. Res. 5(4), 20 (2013). https://doi.org/10.5296/jmr.v5i4.4023

    Article  Google Scholar 

  24. Ohme, R.; Reykowska, D.; Wiener, D.; Choromanska, A.: Analysis of neurophysiological reactions to advertising stimuli by means of EEG and galvanic skin response measures. J. Neurosci. Psychol. Econ. 2(1), 21 (2009). https://doi.org/10.1037/a0015462

    Article  Google Scholar 

  25. Ramadan, R.A.; Refat, S.; Elshahed, M.A.; Ali, R.A.: Brain–computer interfaces, intelligent systems reference library, vol. 74. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-10978-7

    Book  Google Scholar 

  26. Abdulkader, S.N.; Atia, A.; Mostafa, M.S.M.: Brain computer interfacing: applications and challenges. Egypt. Inf. J. 16(2), 213 (2015). https://doi.org/10.1016/j.eij.2015.06.002

    Article  Google Scholar 

  27. Hwang, H.J.; Kim, S.; Choi, S.; Im, C.H.: EEG-based brain–computer interfaces: a thorough literature survey. Int. J. Hum. Comput. Interact. 29(12), 814 (2013). https://doi.org/10.1080/10447318.2013.780869

    Article  Google Scholar 

  28. Zander, T.O.; Kothe, C.: Towards passive brain–computer interfaces: applying brain–computer interface technology to human–machine systems in general. J. Neural Eng. (2011). https://doi.org/10.1088/1741-2560/8/2/025005

    Article  Google Scholar 

  29. Ramadan, R.A.; Vasilakos, A.V.: Brain computer interface: control signals review. Neurocomputing 223(October 2016), 1 (2016). https://doi.org/10.1016/j.neucom.2016.10.024

    Article  Google Scholar 

  30. Bercik, J.; Horska, E.; Wang, R.W.Y.; Chen, Y.C.: The impact of parameters of store illumination on food shopper response. Appetite 106, 101 (2016). https://doi.org/10.1016/j.appet.2016.04.010

    Article  Google Scholar 

  31. Monica, È.B.; Iuliana, C.; Mihai, È.: Studying the user experience in online banking services: an eye-tracking application. Stud. Bus. Econ. 14(2), 193 (2019). https://doi.org/10.2478/sbe-2019-0034

    Article  Google Scholar 

  32. Wang, J.; Han, W.: The impact of perceived quality on online buying decisions: an event-related potentials perspective. Neuroreport 25(14), 1091 (2014). https://doi.org/10.1097/WNR.0000000000000233

    Article  Google Scholar 

  33. Goto, N.; Mushtaq, F.; Shee, D.; Lim, X.L.; Mortazavi, M.; Watabe, M.; Schaefer, A.: Neural signals of selective attention are modulated by subjective preferences and buying decisions in a virtual shopping task. Biol. Psychol. 128, 11 (2017). https://doi.org/10.1016/j.biopsycho.2017.06.004

    Article  Google Scholar 

  34. Yadava, M.; Kumar, P.; Saini, R.; Roy, P.P.; Dogra, D.P.: Analysis of EEG signals and its application to neuromarketing. Multimed. Tools Appl. 76(18), 19087 (2017). https://doi.org/10.1007/s11042-017-4580-6

    Article  Google Scholar 

  35. Touchette, B.; Lee, S.E.: Measuring neural responses to apparel product attractiveness: an application of frontal asymmetry theory. Cloth. Text. Res. J. 35(1), 3 (2017). https://doi.org/10.1177/0887302X16673157

    Article  Google Scholar 

  36. Agarwal, M.; Sivakumar, R.: Cerebro: A Wearable Solution to Detect and Track User Preferences using Brainwaves, pp. 47–52 (2019). https://doi.org/10.1145/3325424.3329660

  37. Vecchiato, G.; Toppi, J.; Astolfi, L.; Fallani, F.D.V.; Cincotti, F.; Mattia, D.; Bez, F.; Babiloni, F.: Spectral EEG frontal asymmetries correlate with the experienced pleasantness of TV commercial advertisements. Med. Biol. Eng. Comput. 49((5, SI)), 579 (2011). https://doi.org/10.1007/s11517-011-0747-x

    Article  Google Scholar 

  38. Vecchiato, G.; Astolfi, L.; Fallani, F.D.V.; Cincotti, F.; Mattia, D.; Salinari, S.; Soranzo, R.; Babiloni, F.: Changes in brain activity during the observation of TV commercials by using EEG, GSR and HR measurements. Brain Topog. 23(2), 165 (2010). https://doi.org/10.1007/s10548-009-0127-0

    Article  Google Scholar 

  39. Cartocci, G.; Caratu, M.; Modica, E.; Maglione, A.G.; Rossi, D.; Cherubino, P.; Babiloni, F.: Electroencephalographic, heart rate, and galvanic skin response assessment for an advertising perception study: application to antismoking public service announcements. J. Vis. Exp. (2017). https://doi.org/10.3791/55872

    Article  Google Scholar 

  40. Telpaz, A.; Webb, R.; Levy, D.J.: Using EEG to Predict Consumers’ Future Choices. J. Mark. Res. 52(4), 511 (2015). https://doi.org/10.1509/jmr.13.0564

    Article  Google Scholar 

  41. Khushaba, R.N.; Wise, C.; Kodagoda, S.; Louviere, J.; Kahn, B.E.; Townsend, C.: Consumer neuroscience: assessing the brain response to marketing stimuli using electroencephalogram (EEG) and eye tracking. Expert Syst. Appl. 40(9), 3803 (2013). https://doi.org/10.1016/j.eswa.2012.12.095

    Article  Google Scholar 

  42. Chew, L.H.; Teo, J.; Mountstephens, J.: Aesthetic preference recognition of 3D shapes using EEG. Cognit. Neurodyn. 10(2), 165 (2016). https://doi.org/10.1007/s11571-015-9363-z

    Article  Google Scholar 

  43. Modica, E.; Cartocci, G.; Rossi, D.; Martinez Levy, A.C.; Cherubino, P.; Maglione, A.G.; Di Flumeri, G.; Mancini, M.; Montanari, M.; Perrotta, D.; Di Feo, P.; Vozzi, A.; Ronca, V.; Aricò, P.; Babiloni, F.: Neurophysiological responses to different product experiences. Comput. Intell. Neurosci. (2018). https://doi.org/10.1155/2018/9616301

    Article  Google Scholar 

  44. Michael, I.; Ramsoy, T.; Stephens, M.; Kotsi, F.: A study of unconscious emotional and cognitive responses to tourism images using a neuroscience method. J. Islam. Mark. 10(2), 543 (2019). https://doi.org/10.1108/JIMA-09-2017-0098

    Article  Google Scholar 

  45. Cherubino, P.: Application of Neuro-marketing techniques to the wine tasting experience. In: 11th Annual Conference of the EuroMed Academy of Business, Malta, pp. 290–298 (2018)

  46. Ramsøy, T.Z.; Skov, M.; Christensen, M.K.; Stahlhut, C.: Frontal brain asymmetry and willingness to pay. Front. Neurosci. (2018). https://doi.org/10.3389/fnins.2018.00138

    Article  Google Scholar 

  47. Ma, Q.; Wang, X.; Shu, L.; Dai, S.: P300 and categorization in brand extension. Neurosci. Lett. 431(1), 57 (2008). https://doi.org/10.1016/j.neulet.2007.11.022

    Article  Google Scholar 

  48. Yang, T.; Lee, S.; Seomoon, E.; Kim, S.P.: Characteristics of human brain activity during the evaluation of service-to-service brand extension. Front. Hum. Neurosci. (2018). https://doi.org/10.3389/fnhum.2018.00044

    Article  Google Scholar 

  49. Bosshard, S.S.; Bourke, J.D.; Kunaharan, S.; Koller, M.; Walla, P.: Established liked versus disliked brands: brain activity, implicit associations and explicit responses. Cogent Psychol. (2016). https://doi.org/10.1080/23311908.2016.1176691

    Article  Google Scholar 

  50. Lee, N.; Chamberlain, L.; Brandes, L.: Welcome to the jungle! The neuromarketing literature through the eyes of a newcomer. Eur. J. Mark. 52(1–2), 4 (2018). https://doi.org/10.1108/EJM-02-2017-0122

    Article  Google Scholar 

  51. Morin, C.: Neuromarketing: the new science of consumer behavior. Society 48(2), 131 (2011). https://doi.org/10.1007/s12115-010-9408-1

    Article  Google Scholar 

  52. Vecchiato, G.; Maglione, A.G.; Cherubino, P.; Wasikowska, B.; Wawrzyniak, A.; Latuszynska, A.; Latuszynska, M.; Nermend, K.; Graziani, I.; Leucci, M.R.; Trettel, A.; Babiloni, F.: Neurophysiological tools to investigate consumer’s gender differences during the observation of TV commercials. Comput. Math. Methods Med. (2014). https://doi.org/10.1155/2014/912981

    Article  Google Scholar 

  53. Stanton, S.J.; Sinnott-Armstrong, W.; Huettel, S.A.: Neuromarketing: ethical implications of its use and potential misuse. J. Bus. Ethics 144(4), 799 (2017). https://doi.org/10.1007/s10551-016-3059-0

    Article  Google Scholar 

  54. Krampe, C.; Gier, N.R.; Kenning, P.: The application of mobile fNIRS in marketing research detecting the first-choice-brand effect. Front. Hum. Neurosci. 12, 433 (2018)

    Article  Google Scholar 

  55. Meyerding, S.G.; Mehlhose, C.M.: Can neuromarketing add value to the traditional marketing research? An exemplary experiment with functional near-infrared spectroscopy (fNIRS). J. Bus. Res. 107, 172 (2020). https://doi.org/10.1016/j.jbusres.2018.10.052

    Article  Google Scholar 

  56. Aldayel, M.S.; Ykhlef, M.; Al-Nafjan, A.N.: Electroencephalogram-based preference prediction using deep transfer learning. IEEE Access 8, 176818 (2020). https://doi.org/10.1109/ACCESS.2020.3027429

    Article  Google Scholar 

  57. Barros, R.Q.; Tavares, A.S.; Albuquerque, W.; da Silva, J.C.; de Lemos, I.A.; de Albuquerque Cardoso, R.L.S.; Soares, M.M.; Cairrao, M.R.: Analysis of product use by means of eye tracking and EEG: a study of neuroergonomics. In: Marcus, A. (eds.) Design, User Experience, and Usability: Novel User Experiences. DUXU 2016. Lecture Notes in Computer Science, vol. 9747, pp. 539–548. Springer, Cham. https://doi.org/10.1007/978-3-319-40355-7_51

  58. Cohrdes, C.; Wrzus, C.; Frisch, S.; Riediger, M.: Tune yourself in: Valence and arousal preferences in music-listening choices from adolescence to old age. Dev. Psychol. 53(9), 1777 (2017). https://doi.org/10.1037/dev0000362

    Article  Google Scholar 

  59. Al-Nafjan, A.; Hosny, M.; Al-Wabil, A.; Al-Ohali, Y.: Classification of human emotions from electroencephalogram (EEG) signal using deep neural network. Int. J. Adv. Comput. Sci. Appl. 8(9), 419 (2017). https://doi.org/10.14569/ijacsa.2017.080955

    Article  Google Scholar 

  60. Aldayel, M.S.: K-Nearest Neighbor classification for glass identification problem. In: 2012 International Conference on Computer Systems and Industrial Informatics, ICCSII 2012 (2012). https://doi.org/10.1109/ICCSII.2012.6454522

  61. Vega-Escobar, L.; Castro-Ospina, A.; Duque-Munoz, L.: DWT-based feature extraction for motor imagery classification. In: 6th Latin-American Conference on Networked and Electronic Media (2016). https://doi.org/10.1049/ic.2015.0309

  62. Moon, J.; Kim, Y.; Lee, H.; Bae, C.; Yoon, W.C.: Extraction of user preference for video stimuli using EEG-based user responses. ETRI J. 35(6), 1105 (2013). https://doi.org/10.4218/etrij.13.0113.0194

    Article  Google Scholar 

Download references

Acknowledgements

This research project was supported by a grant from the “Research Center of the Female Scientific and Medical Colleges,” Deanship of Scientific Research, King Saud University.

Author information

Authors and Affiliations

  1. Information System Department College of Computer and Information Sciences, King Saud University, Riyadh, 11543, Saudi Arabia

    Mashael Aldayel & Mourad Ykhlef

  2. Information Technology Department College of Computer and Information Sciences, King Saud University, Riyadh, 11543, Saudi Arabia

    Mashael Aldayel

  3. Computer Science Department College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University, Riyadh, 11432, Saudi Arabia

    Abeer Al-Nafjan

Authors
  1. Mashael Aldayel
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mourad Ykhlef
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Abeer Al-Nafjan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Abeer Al-Nafjan.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Aldayel, M., Ykhlef, M. & Al-Nafjan, A. Consumers’ Preference Recognition Based on Brain–Computer Interfaces: Advances, Trends, and Applications. Arab J Sci Eng 46, 8983–8997 (2021). https://doi.org/10.1007/s13369-021-05695-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13369-021-05695-4

Keywords

Search

Navigation