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Efficient Recognition of Attentional Bias Using EEG Data and the NeuCube Evolving Spatio-Temporal Data Machine

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Neural Information Processing (ICONIP 2016)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9950))

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Abstract

Modelling of dynamic brain activity for better understanding of human decision making processes becomes an important task in many areas of study. Inspired by importance of the attentional bias principle in human choice behaviour, we proposed a Spiking Neural Network (SNN) model for efficient recognition of attentional bias. The model is based on the evolving spatio-temporal data machine NeuCube. The proposed model is tested on a case study experimental EEG data collected from a group of subjects exemplified here on a group of moderate drinkers when they were presented by different product features (in this case different features of drinks). The results showed a very high accuracy of discriminating attentional bias to non-target objects and their features when compared with a poor performance of traditional machine learning methods. Potential applications in neuromarketing and cognitive studies are also discussed.

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References

  1. Touhami, Z.O., Benlafkih, L., Jiddane, M., Cherrah, Y., El Malki, H.O., Benomar, A.: Neuromarketing: where marketing and neuroscience meet. Afr. J. Bus. Manag. 5, 1528–1532 (2011). Academic Journals

    Google Scholar 

  2. Dooley, R.: Brainfluence: 100 ways to persuade and convince consumers with neuromarketing (2011)

    Google Scholar 

  3. Nazari, M., Doborjeh, Z.G., Oghaz, T.A., Fadardi, J.S., Yazdi, S.A.A.: 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)

    Google Scholar 

  4. Harrison, N.R., McCann, A.: The effect of colour and size on attentional bias to alcohol-related pictures. Int. J. Methodol. Exp. Psychol. 35, 39–48 (2014)

    Google Scholar 

  5. Maass, W., Natschlager, T., Markram, H.: Real-time computing without stable states: a new framework for neural computation based on perturbations. Neural Comput. 14(11), 2531–2560 (2002)

    Article  MATH  Google Scholar 

  6. Thorpe, S., Gautrais, J.: Rank order coding. In: Bower, J.M. (ed.) Computational Neuroscience, pp. 113–118. Springer, Berlin (1998)

    Chapter  Google Scholar 

  7. Masquelier, T., Guyonneau, R., Thorpe, S.J.: Competitive STDP-based spike pattern learning. Neural Comput. 21(5), 1259–1276 (2009)

    Article  MATH  Google Scholar 

  8. Furber, S.B., Galluppi, F., Temple, S., Plana, L.A.: The SpiNNaker project. Proc. IEEE 102(5), 652–665 (2014)

    Article  Google Scholar 

  9. Modha. D.S.: Introducing a brain inspired computer (2016). http://www.research.ibm.com/articles/brain-chip.shtml

  10. Kasabov, N.: NeuCube: a spiking neural network architecture for mapping, learning and understanding of spatio-temporal brain data. Neural Netw. 52, 62–76 (2014). Elsevier

    Article  Google Scholar 

  11. Kasabov, N., Capecci, E.: Spiking neural network methodology for modelling, classification and understanding of EEG spatio-temporal data measuring cognitive processes. Inf. Sci. 294, 565–575 (2015)

    Article  MathSciNet  Google Scholar 

  12. Doborjeh, M.G, Capecci, E., Kasabov, N.: Classification and segmentation of fMRI spatio-temporal brain data with a NeuCube evolving spiking neural network model. In: IEEE SSCI, Orlando, U.S.A., pp. 73–80 (2014)

    Google Scholar 

  13. Doborjeh, M.G., Wang, G.Y., Kasabov, N.K., Kydd, R., Russell, B.: A spiking neural network methodology and system for learning and comparative analysis of EEG data from healthy vs addiction treated vs addiction not treated subjects. IEEE Trans. Biomed. Eng. 63(9), 1830–1841 (2015)

    Article  Google Scholar 

  14. Doborjeh, M.G., Kasabov, N.: Dynamic 3D clustering of spatio-temporal brain data in the NeuCube spiking neural network architecture on a case study of fMRI data. In: Neural Information Processing, Istanbul (2015)

    Google Scholar 

  15. Kasabov, N., Scott, N., Tu, E., Marks, S., Sengupta, N., Capecci, E.: Evolving spatio-temporal data machines based on the NeuCube neuromorphic framework: design methodology and selected applications. Neural Netw. 78, 1–14 (2016)

    Article  Google Scholar 

  16. van Schaik, A., Liu, S.C.: AER EAR: a matched silicon cochlea pair with address event representation interface. In: IEEE International Symposium on Circuits and Systems, vol. 5, pp. 4213–4216 (2005)

    Google Scholar 

  17. Talairach, T., Tournoux, P.: Co-planar Stereotaxic Atlas of the Human Brain. 3-Dimensional Proportional System: An Approach to Cerebral Imaging. Thieme Medical Publishers, New York (1988)

    Google Scholar 

  18. Song, S., Miller, K.D., Abbott, L.F.: Competitive Hebbian learning through spike-timing-dependent synaptic plasticity. Nature Neurosci. 3(9), 919–926 (2000)

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  21. Schliebs, S., Kasabov, N.: Evolving spiking neural networks: a survey, evolving systems. Evolving Syst. 4(2), 87–98 (2013)

    Article  Google Scholar 

  22. Field, M., Cox, W.M.: Attentional bias in addictive behaviors: a review of its development, causes, and consequences. Drug Alcohol Depend. 1, 1–20 (2008). Elsevier

    Article  Google Scholar 

  23. Koessler, L., Maillard, L., Benhadid, A., Vignal, J.P., Felblinger, J., Vespignani, H., Braun, M.: Automated cortical projection of EEG sensors: anatomical correlation via the international 10–10 system. Neuroimage 46(1), 64–72 (2009)

    Article  Google Scholar 

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Acknowledgments

The research is supported by the Knowledge Engineering and Discovery Research Institute of the Auckland University of Technology (www.kedri.aut.ac.nz). Z. Gholami was supported by AUT summer research scholarship. A NeuCube software version is available free from: http://www.kedri.aut.ac.nz/neucube/.

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Authors and Affiliations

  1. Knowledge Engineering and Discovery Research Institute, Auckland University of Technology, Auckland, 1010, New Zealand

    Zohreh Gholami Doborjeh, Maryam Gholami Doborjeh & Nikola Kasabov

Authors
  1. Zohreh Gholami Doborjeh
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  2. Maryam Gholami Doborjeh
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  3. Nikola Kasabov
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Corresponding author

Correspondence to Zohreh Gholami Doborjeh .

Editor information

Editors and Affiliations

  1. The University of Tokyo , Tokyo, Japan

    Akira Hirose

  2. Kobe University , Kobe, Japan

    Seiichi Ozawa

  3. Okinawa Institute of Science and Technology Graduate University, Onna, Japan

    Kenji Doya

  4. Nara Institute of Science and Technology , Ikoma, Japan

    Kazushi Ikeda

  5. Kyungpook National University , Daegu, Korea (Republic of)

    Minho Lee

  6. Chinese Academy of Sciences , Beijing, China

    Derong Liu

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Gholami Doborjeh, Z., Gholami Doborjeh, M., Kasabov, N. (2016). Efficient Recognition of Attentional Bias Using EEG Data and the NeuCube Evolving Spatio-Temporal Data Machine. In: Hirose, A., Ozawa, S., Doya, K., Ikeda, K., Lee, M., Liu, D. (eds) Neural Information Processing. ICONIP 2016. Lecture Notes in Computer Science(), vol 9950. Springer, Cham. https://doi.org/10.1007/978-3-319-46681-1_76

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  • DOI: https://doi.org/10.1007/978-3-319-46681-1_76

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-46680-4

  • Online ISBN: 978-3-319-46681-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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