Skip to main content

Harneshing the Potential of EEG in Neuromarketing with Deep Learning and Riemannian Geometry

  • Conference paper
  • First Online:
Brain Informatics (BI 2023)


Neuromarketing exploits neuroimaging techniques to study consumers’ responses to various marketing aspects, with the goal of gaining a more thorough understanding of the decision-making process. The neuroimaging technology encountered the most in neuromarketing studies is Electroencephalography (EEG), mainly due to its non-invasiveness, low cost and portability. Opposed to typical neuromarketing practices, which rely on signal-power related features, we introduce an efficient decoding scheme that is based on the principles of Riemannian Geometry and realized by means of a suitable deep learning (DL) architecture (i.e., SPDNet). We take advantage of a recently released, multi-subject, neuromarketing dataset to train SPDNet under the close-to-real-life scenario of product selection from a supermarket leaflet and compare its performance against standard tools in EEG-based neuromarketing. The sample covariance is used as an estimator of the ‘quasi-instantaneous’, brain activation pattern and derived from the multichannel signal recorded while the subject is gazing at a given product. Pattern derivation is followed by proper re-alignment to reduce covariate shift (inter-subject variability) before SPDNet casts its binary decision (i.e., “Buy”-“NoBuy”). The proposed decoder is characterized by sufficient generalizability to derive valid predictions upon unseen brain signals. Overall, our experimental results provide clear evidence about the superiority of the DL-decoder relatively to both conventional neuromarketing and alternative Riemannian Geometry-based approaches, and further demonstrate how neuromarketing can benefit from recent advances in data-centric machine learning and the availability of relevant experimental datasets.

This work was a part of project NeuroMkt, co-financed by the European Regional Development Fund of the EU and Greek National Funds through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the call RESEARCH CREATE INNOVATE (Project code T2EDK-03661).

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

Access this chapter

USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
USD 59.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 79.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others


  1. 1.

  2. 2.


  1. Ali, A., et al.: EEG signals based choice classification for neuromarketing applications. In: Kumar, P., Obaid, A.J., Cengiz, K., Khanna, A., Balas, V.E. (eds.) A Fusion of Artificial Intelligence and Internet of Things for Emerging Cyber Systems. ISRL, vol. 210, pp. 371–394. Springer, Cham (2022).

    Chapter  Google Scholar 

  2. Bengio, Y., Paiement, J.f., Vincent, P., Delalleau, O., Roux, N., Ouimet, M.: Out-of-sample extensions for LLE, isomap, MDS, eigenmaps, and spectral clustering. In: Advances in Neural Information Processing Systems, vol. 16 (2003)

    Google Scholar 

  3. Bini, D.A., Iannazzo, B.: Computing the Karcher mean of symmetric positive definite matrices. Linear Algebra Appl. 438(4), 1700–1710 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  4. Blankertz, B., Tomioka, R., Lemm, S., Kawanabe, M., Muller, K.R.: Optimizing spatial filters for robust EEG single-trial analysis. IEEE Signal Process. Mag. 25(1), 41–56 (2007)

    Article  Google Scholar 

  5. Branco, P., Torgo, L., Ribeiro, R.P.: A survey of predictive modeling on imbalanced domains. ACM Comput. Surv. (CSUR) 49(2), 1–50 (2016)

    Article  Google Scholar 

  6. Cohen, M.X.: Analyzing Neural Time Series Data: Theory and Practice. MIT press, Cambridge (2014)

    Book  Google Scholar 

  7. Congedo, M., Barachant, A., Bhatia, R.: Riemannian geometry for EEG-based brain-computer interfaces; a primer and a review. Brain-Comput. Interfaces 4(3), 155–174 (2017)

    Article  Google Scholar 

  8. Daly, I., Scherer, R., Billinger, M., Müller-Putz, G.: Force: fully online and automated artifact removal for brain-computer interfacing. IEEE Trans. Neural Syst. Rehabil. Eng. 23(5), 725–736 (2014)

    Article  Google Scholar 

  9. García-Madariaga, J., Moya, I., Recuero, N., Blasco, M.F.: Revealing unconscious consumer reactions to advertisements that include visual metaphors. a neurophysiological experiment. Front. Psychol. 11, 760 (2020)

    Article  Google Scholar 

  10. Georgiadis, K., Adamos, D.A., Nikolopoulos, S., Laskaris, N., Kompatsiaris, I.: A graph-theoretic sensor-selection scheme for covariance-based motor imagery (MI) decoding. In: 2020 28th European Signal Processing Conference (EUSIPCO), pp. 1234–1238. IEEE (2021)

    Google Scholar 

  11. Georgiadis, K., Kalaganis, F.P., Riskos, K. et al.: NeuMa - the absolute neuromarketing dataset en route to an holistic understanding of consumer behaviour. Sci. Data 10, 508 (2023).

  12. Georgiadis, K., Kalaganis, F.P., Oikonomou, V.P., Nikolopoulos, S., Laskaris, N.A., Kompatsiaris, I.: Rneumark: a Riemannian EEG analysis framework for neuromarketing. Brain Inform. 9(1), 22 (2022)

    Article  Google Scholar 

  13. Georgiadis, K., Laskaris, N., Nikolopoulos, S., Kompatsiaris, I.: Exploiting the heightened phase synchrony in patients with neuromuscular disease for the establishment of efficient motor imagery bcis. J. Neuroeng. Rehabil. 15(1), 1–18 (2018)

    Article  Google Scholar 

  14. Hakim, A., Klorfeld, S., Sela, T., Friedman, D., Shabat-Simon, M., Levy, D.J.: Machines learn neuromarketing: improving preference prediction from self-reports using multiple EEG measures and machine learning. Int. J. Res. Mark. 38(3), 770–791 (2021)

    Article  Google Scholar 

  15. Huang, Z., Van Gool, L.: A Riemannian network for SPD matrix learning. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31 (2017)

    Google Scholar 

  16. Kalaganis, F.P., Georgiadis, K., Oikonomou, V.P., Laskaris, N.A., Nikolopoulos, S., Kompatsiaris, I.: Unlocking the subconscious consumer bias: a survey on the past, present, and future of hybrid EEG schemes in neuromarketing. Front. Neuroergonomics 2, 11 (2021)

    Article  Google Scholar 

  17. Kalaganis, F.P., Laskaris, N.A., Chatzilari, E., Nikolopoulos, S., Kompatsiaris, I.: A data augmentation scheme for geometric deep learning in personalized brain-computer interfaces. IEEE Access 8, 162218–162229 (2020)

    Article  Google Scholar 

  18. Kalaganis, F.P., Laskaris, N.A., Chatzilari, E., Nikolopoulos, S., Kompatsiaris, I.: A Riemannian geometry approach to reduced and discriminative covariance estimation in brain computer interfaces. IEEE Trans. Biomed. Eng. 67(1), 245–255 (2019)

    Article  Google Scholar 

  19. Kobler, R., Hirayama, J.i., Zhao, Q., Kawanabe, M.: SPD domain-specific batch normalization to crack interpretable unsupervised domain adaptation in EEG. In: Advances in Neural Information Processing Systems, vol. 35, pp. 6219–6235 (2022)

    Google Scholar 

  20. MacKenzie, S.B., Podsakoff, P.M.: Common method bias in marketing: causes, mechanisms, and procedural remedies. J. Retail. 88(4), 542–555 (2012)

    Article  Google Scholar 

  21. Mullen, T.R., et al.: Real-time neuroimaging and cognitive monitoring using wearable dry EEG. IEEE Trans. Biomed. Eng. 62(11), 2553–2567 (2015)

    Article  Google Scholar 

  22. Naser, D.S., Saha, G.: Influence of music liking on EEG based emotion recognition. Biomed. Signal Process. Control 64, 102251 (2021)

    Article  Google Scholar 

  23. Oikonomou, V.P., Georgiadis, K., Kalaganis, F., Nikolopoulos, S., Kompatsiaris, I.: A sparse representation classification scheme for the recognition of affective and cognitive brain processes in neuromarketing. Sensors 23(5), 2480 (2023)

    Article  Google Scholar 

  24. Pennec, X., Fillard, P., Ayache, N.: A Riemannian framework for tensor computing. Int. J. Comput. Vision 66, 41–66 (2006)

    Article  MATH  Google Scholar 

  25. Rawnaque, F.S., et al.: Technological advancements and opportunities in neuromarketing: a systematic review. Brain Inform. 7, 1–19 (2020)

    Google Scholar 

  26. Vecchiato, G., et al.: Neurophysiological tools to investigate consumer’s gender differences during the observation of tv commercials. Comput. Math. Methods Med. 2014 (2014)

    Google Scholar 

  27. Wang, Y., Qiu, S., Ma, X., He, H.: A prototype-based SPD matrix network for domain adaptation EEG emotion recognition. Pattern Recogn. 110, 107626 (2021)

    Article  Google Scholar 

  28. Yadava, M., Kumar, P., Saini, R., Roy, P.P., Prosad Dogra, D.: Analysis of EEG signals and its application to neuromarketing. Multimedia Tools Appl. 76, 19087–19111 (2017)

    Article  Google Scholar 

  29. Zanini, P., Congedo, M., Jutten, C., Said, S., Berthoumieu, Y.: Transfer learning: a Riemannian geometry framework with applications to brain-computer interfaces. IEEE Trans. Biomed. Eng. 65(5), 1107–1116 (2017)

    Article  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations


Corresponding author

Correspondence to Kostas Georgiadis .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Georgiadis, K., Kalaganis, F.P., Oikonomou, V.P., Nikolopoulos, S., Laskaris, N.A., Kompatsiaris, I. (2023). Harneshing the Potential of EEG in Neuromarketing with Deep Learning and Riemannian Geometry. In: Liu, F., Zhang, Y., Kuai, H., Stephen, E.P., Wang, H. (eds) Brain Informatics. BI 2023. Lecture Notes in Computer Science(), vol 13974. Springer, Cham.

Download citation

  • DOI:

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-43074-9

  • Online ISBN: 978-3-031-43075-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics