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Cognitive Computation

, Volume 10, Issue 1, pp 35–48 | Cite as

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

  • Zohreh Gholami Doborjeh
  • Maryam G. Doborjeh
  • Nikola Kasabov
Article

Abstract

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.

Keywords

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

Notes

Acknowledgements

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

Compliance with Ethical Standards

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.

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Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.Knowledge Engineering and Discovery Research Institute (KEDRI) and School of Engineering, Computer and Mathematical Sciences (SCMS)Auckland University of TechnologyAucklandNew Zealand

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