Skip to main content
SpringerLink
Log in

Analysis of EEG signals and its application to neuromarketing

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Marketing and promotions of various consumer products through advertisement campaign is a well known practice to increase the sales and awareness amongst the consumers. This essentially leads to increase in profit to a manufacturing unit. Re-production of products usually depends on the various facts including consumption in the market, reviewer’s comments, ratings, etc. However, knowing consumer preference for decision making and behavior prediction for effective utilization of a product using unconscious processes is called “Neuromarketing”. This field is emerging fast due to its inherent potential. Therefore, research work in this direction is highly demanded, yet not reached a satisfactory level. In this paper, we propose a predictive modeling framework to understand consumer choice towards E-commerce products in terms of “likes” and “dislikes” by analyzing EEG signals. The EEG signals of volunteers with varying age and gender were recorded while they browsed through various consumer products. The experiments were performed on the dataset comprised of various consumer products. The accuracy of choice prediction was recorded using a user-independent testing approach with the help of Hidden Markov Model (HMM) classifier. We have observed that the prediction results are promising and the framework can be used for better business model.

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
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18

Similar content being viewed by others

Notes

  1. https://sites.google.com/site/iitrcsepradeep7/

References

  1. Ambler T, Braeutigam S, Stins J, Rose S, Swithenby S (2004) Salience and choice: neural correlates of shopping decisions. Psychol Market 21(4):247–261

    Article  Google Scholar 

  2. Amin HU, Malik AS, Ahmad RF, Badruddin N, Kamel N, Hussain M, Chooi WT (2015) Feature extraction and classification for EEG signals using wavelet transform and machine learning techniques. Aust Phys Eng Sci Med 38(1):139–149

    Article  Google Scholar 

  3. Anderson CW, Sijercic Z (1996) Classification of EEG signals from four subjects during five mental tasks. In: Solving engineering problems with neural networks: proceedings of the conference on engineering applications in neural networks (EANN’96), pp 407–414

  4. Awal MA, Mostafa SS, Ahmad M (2011) Performance analysis of savitzky-golay smoothing filter using ECG signal. Int J Comput Inf Technol 1(02)

  5. Baldo D, Parikh H, Piu Y, Müller KM (2015) Brain waves predict success of new fashion products: A practical application for the footwear retailing industry. J Creat Value 1(1):61–71

    Article  Google Scholar 

  6. Berns G, Moore S E (2010) A neural predictor of cultural popularity. Available at SSRN 1742971

  7. Boksem MA, Smidts A (2015) Brain responses to movie trailers predict individual preferences for movies and their population-wide commercial success. J Mark Res 52 (4):482–492

    Article  Google Scholar 

  8. Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–297

    MATH  Google Scholar 

  9. Gandhi V, Prasad G, Coyle D, Behera L, McGinnity TM (2014) Quantum neural network-based EEG filtering for a brain–computer interface. IEEE Trans Neural Networks Learn Syst 25(2):278–288

    Article  Google Scholar 

  10. Guo G, Elgendi M (2013) A new recommender system for 3d e-commerce: an EEG based approach. J Adv Manag Sci 1(1):61–65

    Article  Google Scholar 

  11. Hargittai S (2005) Savitzky-golay least-squares polynomial filters in ECG signal processing. In: Computers in cardiology, pp 763–766

  12. Hazarika N, Chen JZ, Tsoi AC, Sergejew A (1997) Classification of EEG signals using the wavelet transform. In: 13th International conference on digital signal processing proceedings, vol 1, pp 89–92

  13. Ho TK (1995) Random decision forests. In: 3rd International conference on document analysis and recognition, pp 278–282

  14. Holzinger A, Scherer R, Seeber M, Wagner J, Müller-Putz G (2012) Computational sensemaking on examples of knowledge discovery from neuroscience data: towards enhancing stroke rehabilitation. In: International conference on information technology in bio-and medical informatics, pp 166–168

  15. Holzinger A, Stocker C, Bruschi M, Auinger A, Silva H, Gamboa H, Fred A (2012) On applying approximate entropy to ECG signals for knowledge discovery on the example of big sensor data. In: International conference on active media technology, pp 646–657

  16. Hsieh CH, Chu HP, Huang YH (2014) An hmm-based eye movement detection system using EEG brain-computer interface. In: International symposium on circuits and systems, pp 662–665

  17. Kaur B, Singh D, Roy PP A novel framework of eeg-based user identification by analyzing music-listening behavior. Multimed Tools Appl, pp 1–22

  18. Kawasaki M, Yamaguchi Y (2012) Effects of subjective preference of colors on attention-related occipital theta oscillations. Neuroimage 59(1):808–814

    Article  Google Scholar 

  19. Khushaba RN, Greenacre L, Kodagoda S, Louviere J, Burke S, Dissanayake G (2012) Choice modeling and the brain: a study on the electroencephalogram (EEG) of preferences. Expert Syst Appl 39(16):12,378–12,388

    Article  Google Scholar 

  20. Khushaba R N, Wise C, Kodagoda S, Louviere J, Kahn BE, Townsend C (2013) Consumer neuroscience: assessing the brain response to marketing stimuli using electroencephalogram (EEG) and eye tracking. Expert Syst Appl 40(9):3803–3812

    Article  Google Scholar 

  21. Kroupi E, Hanhart P, Lee JS, Rerabek M, Ebrahimi T (2014) Predicting subjective sensation of reality during multimedia consumption based on EEG and peripheral physiological signals. In: International conference on multimedia and expo, pp 1–6

  22. Kumar P, Gauba H, Roy PP, Dogra DP (2016) Coupled hmm-based multi-sensor data fusion for sign language recognition. Pattern Recogn Lett

  23. Kumar P, Saini R, Roy PP, Dogra DP (2016) 3d text segmentation and recognition using leap motion. Multimed Tools Appl:1–20

  24. Morin C (2011) Neuromarketing: the new science of consumer behavior. Society 48(2):131–135

    Article  Google Scholar 

  25. Murugappan M, Murugappan S, Gerard C, et al (2014) Wireless EEG signals based neuromarketing system using fast fourier transform (FFT). In: 10th International colloquium on signal processing & its applications, pp 25–30

  26. Rabiner L, Juang B (1986) An introduction to hidden markov models. IEEE ASSP Mag 3(1):4–16

    Article  Google Scholar 

  27. Rahman FA, Othman M (2015) Real time eye blink artifacts removal in electroencephalogram using savitzky-golay referenced adaptive filtering. In: International conference for innovation in biomedical engineering and life sciences, pp 68–71

  28. Rodriguez-Galiano VF, Ghimire B, Rogan J, Chica-Olmo M, Rigol-Sanchez JP (2012) An assessment of the effectiveness of a random forest classifier for land-cover classification. ISPRS J Photogramm Remote Sens 67:93–104

    Article  Google Scholar 

  29. Smith A, Bernheim BD, Camerer CF, Rangel A (2014) Neural activity reveals preferences without choices. Amer Econ J Microecon 6(2):1–36

    Article  Google Scholar 

  30. Soleymani M, Chanel G, Kierkels JJ, Pun T (2008) Affective ranking of movie scenes using physiological signals and content analysis. In: 2nd workshop on multimedia semantics, pp 32–39

  31. Solhjoo S, Nasrabadi AM, Golpayegani MRH (2005) Classification of chaotic signals using HMM classifiers: EEG-based mental task classification. In; 13th European signal processing conference, pp 1–4

  32. Srinivasan V, Eswaran C, Sriraam N (2007) Approximate entropy-based epileptic EEG detection using artificial neural networks. IEEE Trans Inf Technol Biomed 11(3):288–295

    Article  Google Scholar 

  33. Stanton SJ, Sinnott-Armstrong W, Huettel SA (2016) Neuromarketing: Ethical implications of its use and potential misuse. J Bus Ethics:1–13

  34. Stickel C, Fink J, Holzinger A (2007) Enhancing universal access–EEG based learnability assessment. In: International conference on universal access in human-computer interaction, pp 813–822

  35. Telpaz A, Webb R, Levy DJ (2015) Using EEG to predict consumers’ future choices. J Mark Res 52(4):511–529

    Article  Google Scholar 

  36. Vecchiato G, Kong W, Giulio Maglione A, Wei D (2012) Understanding the impact of TV commercials. IEEE Pulse 3(3):42

    Article  Google Scholar 

  37. Wei S, Tang J, Chai Y, Zhao W (2016) Classifying the epilepsy EEG signal by hybrid model of CSHMM on the basis of clinical features of interictal epileptiform discharges. In: Chinese intelligent systems conference, pp 377–385

Download references

Acknowledgments

The authors would like to thank the anonymous reviewers for their constructive comments and suggestions to improve the quality of the paper.

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Indian Institute of Technology, Roorkee, India

    Mahendra Yadava, Pradeep Kumar, Rajkumar Saini & Partha Pratim Roy

  2. School of Electrical Sciences, Indian Institute of Technology, Bhubaneswar, India

    Debi Prosad Dogra

Authors
  1. Mahendra Yadava
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Pradeep Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Rajkumar Saini
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Partha Pratim Roy
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Debi Prosad Dogra
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Pradeep Kumar.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Yadava, M., Kumar, P., Saini, R. et al. Analysis of EEG signals and its application to neuromarketing. Multimed Tools Appl 76, 19087–19111 (2017). https://doi.org/10.1007/s11042-017-4580-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-017-4580-6

Keywords

Search

Navigation