Attentional Bias Pattern Recognition in Spiking Neural Networks from Spatio-Temporal EEG Data


When facing with different marketing product features, consumers are unaware of the important role of external stimuli on their decision-making behaviour. Neuromarketing background suggested that consumers might be seduced by the attentional bias which can direct their decision. This study aims at modelling and visualisation of the brain activity patterns generated by marketing product features with respect to the spatio-temporal relationships between the continuous EEG data streams. This research utilises brain-like Spiking Neural Network (SNN) models for analysing spatio-temporal brain patterns generated by attentional bias. The model was applied to Electroencephalogram (EEG) data for investigating the effectiveness of attentional bias on consumer preference towards marketing stimuli. Our experimental results have shown that consumers were more likely to get distracted by product features that are related to their subconscious preferences. This paper proofs that consumers pay the highest attention to non-target stimuli when they were presented with attractive features. This study provided a proof of principle for the role of attentional bias on concern-related human preferences. It represents knowledge discovery in the prediction of consumer preferences in the field of neuromarketing. The SNN-based models performed superior not only in achieving a higher classification of EEG data related to different stimuli in comparison with traditional methods, but it most importantly enables a better interpretation and understanding of underpinning brain functions against marketing stimuli.

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

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


  1. 1.

    Bar-Haim Y, Lamy D, Pergamin L, Bakermans-Kranenburg MJ, Van Ijzendoorn MH. Threat-related attentional bias in anxious and nonanxious individuals: a meta-analytic study. Psychol Bull. 2007;133(1):1.

    Article  PubMed  Google Scholar 

  2. 2.

    Nazari M, Doborjeh ZG, Oghaz TA, Fadardi JS, Yazdi SA. Evaluation of consumers’ preference to the brands of beverage by means of ERP pre-comprehension component. In: Proceedings of the International Conference on Global Economy, Commerce and Service Science (GECSS), Thailand 2014 Jan 9, pp: 294–297.

  3. 3.

    Harrison NR, McCann A. The Effect of Colour and Size on Attentional Bias to Alcohol-Related Pictures. Psicol: Int J Methodol Exp Psychol. 2014;35(1):39–48.

    Google Scholar 

  4. 4.

    Abdullah A, Khan IH, Basuhail A, Hussain A. A Novel Near-Infrared Spectroscopy Based Spatiotemporal Cognition Study of the Human Brain Using Clustering. Cogn Comput. 2015;7(6):693–705.

    Article  Google Scholar 

  5. 5.

    Lee N, Broderick AJ, Chamberlain L. What is ‘neuromarketing’? A discussion and agenda for future research. Int J Psychophysiol. 2007;63(2):199–204.

    Article  PubMed  Google Scholar 

  6. 6.

    Field M, Cox WM. Attentional bias in addictive behaviours: a review of its development, causes, and consequences. Drug Alcohol Depend. 2008;97(1):1–20.

    Article  PubMed  Google Scholar 

  7. 7.

    Fadardi JS, Cox WM. Reversing the sequence: reducing alcohol consumption by overcoming alcohol attentional bias. Drug Alcohol Depend. 2009;101(3):137–45.

    Article  PubMed  Google Scholar 

  8. 8.

    Luijten M, Veltman DJ, Hester R, Smits M, Pepplinkhuizen L, Franken IH. Brain activation associated with attentional bias in smokers is modulated by a dopamine antagonist. Neuro-psychoanalysis. 2012;37(13):2772–9.

    CAS  Google Scholar 

  9. 9.

    Paugam-Moisy H, Bohte S. Computing with spiking neuron networks. In: Handbook of natural computing 2012 (pp. 335–376). Springer Berlin Heidelberg.

  10. 10.

    Masquelier T, Guyonneau R, Thorpe SJ. Competitive STDP-based spike pattern learning. Neural Comput. 2009;21(5):1259–76.

    Article  PubMed  Google Scholar 

  11. 11.

    Furber SB, Galluppi F, Temple S, Plana LA. The spinnaker project. Proc IEEE. 2014 May;102(5):652–65.

    Article  Google Scholar 

  12. 12.

    Modha DS. Introducing a Brain-inspired Computer. IBM Research, accessed at www. Research. ibm. Com/articles/brain-chip. shtml. 2014.

  13. 13.

    Philiastides MG, Heekeren HR. Spatiotemporal characteristics of perceptual decision making in the human brain. In: Dreher JC, Tremblay L, editors. Handbook of reward and decision making; 2009. p. 185–212.

    Google Scholar 

  14. 14.

    Yadava M, Kumar P, Saini R, Roy PP, Dogra DP. Analysis of EEG signals and its application to neuromarketing. Multimedia Tools Appl. 2017:1–25.

  15. 15.

    Minati L, Grisoli M, Franceschetti S, Epifani F, Granvillano A, Medford N, et al. Neural signatures of economic parameters during decision-making: a functional MRI (FMRI), electroencephalography (EEG) and autonomic monitoring study. Brain Topogr. 2012;25(1):73–96.

    Article  PubMed  Google Scholar 

  16. 16.

    Vecchiato G, Cherubino P, Maglione AG, Ezquierro MT, Marinozzi F, Bini F, et al. How to measure cerebral correlates of emotions in marketing relevant tasks. Cogn Comput. 2014;6(4):856–71.

    Article  Google Scholar 

  17. 17.

    Kasabov NK. NeuCube: A spiking neural network architecture for mapping, learning and understanding of spatio-temporal brain data. Neural Netw. 2014;52:62–76.

    Article  PubMed  Google Scholar 

  18. 18.

    Doborjeh ZG, Doborjeh MG, Kasabov N. Efficient recognition of attentional bias using EEG data and the NeuCube evolving spatio-temporal data machine. In: International Conference on Neural Information Processing. Springer International Publishing; 2016. pp. 645–53.

  19. 19.

    Kawano H, Seo A, Doborjeh ZG, Kasabov N, Doborjeh MG. Analysis of similarity and differences in brain activities between perception and production of facial expressions using EEG DATA and the NeuCube spiking neural network architecture. In: International Conference on Neural Information Processing. Springer International Publishing; 2016. pp. 221–7.

  20. 20.

    Capecci E, Doborjeh ZG, Mammone N, La Foresta F, Morabito FC, Kasabov N. Longitudinal study of Alzheimer’s disease degeneration through EEG data analysis with a NeuCube spiking neural network model. In: Neural Networks (IJCNN), 2016 International Joint Conference on 2016 Jul 24. IEEE. pp. 1360–6.

  21. 21.

    Doborjeh MG, Wang GY, Kasabov NK, Kydd R, Russell B. A spiking neural network methodology and system for learning and comparative analysis of EEG data from healthy versus addiction treated versus addiction not treated subjects. IEEE Trans Biomed Eng. 2016;63(9):1830–41.

    Article  PubMed  Google Scholar 

  22. 22.

    Kasabov N, Scott NM, Tu E, Marks S, Sengupta N, Capecci E, et al. Evolving spatio-temporal data machines based on the NeuCube neuromorphic framework: design methodology and selected applications. Neural Netw. 2016;78:1–4.

    Article  PubMed  Google Scholar 

  23. 23.

    Jongsma ML, van Rijn CM, Gerrits NJ, Eichele T, Steenbergen B, Maes JH, et al. The learning-oddball paradigm: Data of 24 separate individuals illustrate its potential usefulness as a new clinical tool. Clin Neurophysiol. 2013;124(3):514–21.

    Article  PubMed  Google Scholar 

  24. 24.

    García-Larrea L, Lukaszewicz AC, Mauguiére F. Revisiting the oddball paradigm. Non-target vs neutral stimuli and the evaluation of ERP attentional effects. Neuro psychology. 1992;30(8):723–41.

    Google Scholar 

  25. 25.

    Kasabov N, Zhou L, Doborjeh MG, Doborjeh ZG, Yang J. New algorithms for encoding, learning and classification of fMRI data in a spiking neural network architecture: a case on modelling and understanding of dynamic cognitive processes. IEEE Trans Cogn Dev Syst. 2016.

  26. 26.

    Tu E, Kasabov N, Yang J. Mapping temporal variables into the NeuCube for improved pattern recognition, predictive modelling, and understanding of stream data. IEEE Trans Neural Netw Learn Syst. 2016;15(99):1–13.

    Google Scholar 

  27. 27.

    Lancaster JL, Woldorff MG, Parsons LM, Liotti M, Freitas CS, Rainey L, et al. Automated Talairach atlas labels for functional brain mapping. Hum Brain Mapp. 2000;10(3):120–31.

    CAS  Article  PubMed  Google Scholar 

  28. 28.

    Koessler L, Maillard L, Benhadid A, Vignal JP, Felblinger J, Vespignani H, et al. Automated cortical projection of EEG sensors: anatomical correlation via the international 10–10 system. NeuroImage. 2009;46(1):64–72.

    CAS  Article  PubMed  Google Scholar 

  29. 29.

    Song S, Miller KD, Abbott LF. Competitive Hebbian learning through spike-timing-dependent synaptic plasticity. Nat Neurosci. 2000;3(9):919–26.

    CAS  Article  PubMed  Google Scholar 

  30. 30.

    Kasabov N, Dhoble K, Nuntalid N, Indiveri G. Dynamic evolving spiking neural networks for on-line spatio-and spectro-temporal pattern recognition. Neural Netw. 2013;41:188–201.

    Article  PubMed  Google Scholar 

  31. 31.

    Schliebs S, Kasabov N. Evolving spiking neural network—a survey. Evol Syst. 2013;4(2):87–98.

    Article  Google Scholar 

  32. 32.

    Delorme A, Makeig S. EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J Neurosci Methods. 2004;134(1):9–21.

    Article  PubMed  Google Scholar 

Download references


This study is supported by Knowledge Engineering and Discovery Research Institute (KEDR), Auckland University of Technology (AUT), New Zealand.

Author information



Corresponding authors

Correspondence to Zohreh Gholami Doborjeh or Maryam G. Doborjeh.

Ethics declarations

The contents of this study are the authors’ original work and the manuscript has not been published or submitted for publication elsewhere. Also we declare there was no disclosure of any financial support, conflict of interest to be made.

Ethical Approval

The EEG data was collected from human participants. Prior to EEG data collection, ethical approval was granted by Ethics Committee of Auckland University of Technology (AUTEC) New Zealand, and informed consent was signed by every single subjects.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Gholami Doborjeh, Z., Doborjeh, M.G. & Kasabov, N. Attentional Bias Pattern Recognition in Spiking Neural Networks from Spatio-Temporal EEG Data. Cogn Comput 10, 35–48 (2018).

Download citation


  • Neuromarketing
  • Attentional bias
  • Consumer preferences
  • Decision making
  • NeuCube
  • Spiking neural networks
  • Spatio-temporal brain data