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Arabian Journal for Science and Engineering

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28.05.2021 | Research Article-Computer Engineering and Computer Science

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

verfasst von: Mashael Aldayel, Mourad Ykhlef, Abeer Al-Nafjan

Erschienen in: Arabian Journal for Science and Engineering | Ausgabe 9/2021

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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.

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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/app10041525CrossRef

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/a0030198CrossRef

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.2015.12.005CrossRef

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)

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.2012.04.084CrossRef

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.v10n1p90CrossRef

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/app7121239CrossRef

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.2010.03.008CrossRef

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

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.1100.0284CrossRef

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/s40622-015-0113-1CrossRef

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-12-2016-0805CrossRef

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.2010.09.020CrossRef

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.2012.07.043CrossRef

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.2011.10.011CrossRef

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.2011.11.008CrossRef

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/10.1101/317073CrossRef

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)CrossRef

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.2012.2217495CrossRef

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.v5i4.4023CrossRef

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/a0015462CrossRef

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/10.1007/978-3-319-10978-7CrossRef

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.2015.06.002CrossRef

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/10.1080/10447318.2013.780869CrossRef

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/10.1088/1741-2560/8/2/025005CrossRef

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.2016.10.024CrossRef

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.2016.04.010CrossRef

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-2019-0034CrossRef

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.0000000000000233CrossRef

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.2017.06.004CrossRef

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/s11042-017-4580-6CrossRef

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/10.1177/0887302X16673157CrossRef

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-xCrossRef

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/s10548-009-0127-0CrossRef

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/10.3791/55872CrossRef

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.13.0564CrossRef

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.2012.12.095CrossRef

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-zCrossRef

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/10.1155/2018/9616301CrossRef

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-09-2017-0098CrossRef

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.2018.00138CrossRef

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.2007.11.022CrossRef

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.2018.00044CrossRef

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/10.1080/23311908.2016.1176691CrossRef

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-02-2017-0122CrossRef

51.

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

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/10.1155/2014/912981CrossRef

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/s10551-016-3059-0CrossRef

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)CrossRef

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.2018.10.052CrossRef

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.2020.3027429CrossRef

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/dev0000362CrossRef

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.2017.080955CrossRef

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.13.0113.0194CrossRef

Titel: Consumers’ Preference Recognition Based on Brain–Computer Interfaces: Advances, Trends, and Applications
verfasst von: Mashael Aldayel
Mourad Ykhlef
Abeer Al-Nafjan
Publikationsdatum: 28.05.2021
Verlag: Springer Berlin Heidelberg
Erschienen in: Arabian Journal for Science and Engineering / Ausgabe 9/2021
Print ISSN: 2193-567X
Elektronische ISSN: 2191-4281
DOI: https://doi.org/10.1007/s13369-021-05695-4

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