EEG Comparison Between Normal and Developmental Disorder in Perception and Imitation of Facial Expressions with the NeuCube

  • Yuma Omori
  • Hideaki Kawano
  • Akinori Seo
  • Zohreh Gholami Doborjeh
  • Nikola Kasabov
  • Maryam Gholami Doborjeh
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10637)

Abstract

This paper is a feasibility study of using the NeuCube spiking neural network (SNN) architecture for modeling EEG brain data related to perceiving versus mimicking facial expressions. We collected EEG patterns during perception and imitation of facial expressions for each emotion. Comparing the collected data in perceiving and mimicking facial expressions, EEG patterns were very similar. This fact suggests that it seems that there are mirror neurons on facial expression in the human brain. Recently, some studies have been reported that the mirror neuron system does not work well in the case of subjects with brain disorders. In this study, we calculated differences between EEG patterns when we perceived facial expressions and mimicking facial expressions for healthy people and developmental disorders.

Keywords

EEG data SNN Mirror neuron system Developmental disorders 

References

  1. 1.
    Gallese, V., Fadiga, L., Fogassi, L., Rizzolatti, G.: Action recognition in thepremotor cortex. Brain 119, 593–609 (1996)CrossRefGoogle Scholar
  2. 2.
    Lacoboni, M., Woods, R.P., Brass, M., Bekkering, H., Mazziotta, J.C., Rizzolatti, G.: Cortical mechanisms of human imitation. Science 186, 2526–2528 (1999)CrossRefGoogle Scholar
  3. 3.
    Binkofski, F., Buccino, G., Seitz, R.J., Rizzolatti, G., Freund, H.-J.: Afronto-parietal circuit for object manipulation in man: evidence from an fMRIstudy. Eur. J. Neurosci. 11, 3276–3286 (1999)CrossRefGoogle Scholar
  4. 4.
    Kasabov, N.: NeuCube: a spiking neural network architecture for mapping, learning and understanding of spatio-temporal brain data. Neural Netw. 52, 62–76 (2014)CrossRefGoogle Scholar
  5. 5.
    Tu, E., Kasabov, N., Yang, J.: Mapping temporal variables into the NeuCube for improved pattern recognition, predictive modelling and understanding of stream data. In: IEEE Transactions on Neural Networks and Learning Systems, pp. 1–13. IEEE Press, New York (2016)Google Scholar
  6. 6.
    Kasabov, N., Scott, E., Tu, E., Marks, S., Sengupta, N., Capecci, E.: Evolvingspatio- temporal data machines based on the NeuCube neuromorphic framework: design methodology and selected applications. Neural Netw. 78, 1–14 (2016)CrossRefGoogle Scholar
  7. 7.
    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: IIEEE International Symposium on Circuits and Systems, pp. 73–80. IEEE Press, Melbourne (2014)Google Scholar
  8. 8.
    Doberjeh, M.G., Wang, G., Kasabov, N., Kydd, R., Russell, B.R.: A NeucubeSpiking neural network model for the study of dynamic brain activities during a GO/NO GO task: a case study on using EEG data of healthy vs addiction vs treated subjects. IEEE Trans. Biomed. Eng. 63, 1830–1841 (2016)CrossRefGoogle Scholar
  9. 9.
    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: Arik, S., Huang, T., Lai, W.K., Liu, Q. (eds.) ICONIP 2015. LNCS, vol. 9492, pp. 191–198. Springer, Cham (2015). doi: 10.1007/978-3-319-26561-2_23 CrossRefGoogle Scholar
  10. 10.
    Song, S., Miller, K.D., Abbott, L.F.: Competitive Hebbian learning throughspike- timing-dependent synaptic plasticity. Nat. Neurosci. 3, 919–926 (2000)CrossRefGoogle Scholar
  11. 11.
    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)CrossRefGoogle Scholar
  12. 12.
    Matsumoto, D., Ekman, P.: Japanese and Caucasian facial expressions of emotion (IACFEE) [Slides]. Intercultural and Emotion Research Laboratory, Department of Psychology, San Francisco State University, San Francisco (1988)Google Scholar
  13. 13.
    Talairach, J., 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
  14. 14.
    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, 64–72 (2009)CrossRefGoogle Scholar
  15. 15.
    Alfano, K.M., Cimino, C.R.: Alteration of expected hemispheric asymmetries: valence and arousal effects in neuropsychological models of emotion. Brain Cogn. 66, 213–220 (2008)CrossRefGoogle Scholar
  16. 16.
    Kawano, H., Seo, A., Doborjeh, Z.G., Kasabov, N., Doborjeh, M.G.: 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: Hirose, A., Ozawa, S., Doya, K., Ikeda, K., Lee, M., Liu, D. (eds.) ICONIP 2016. LNCS, vol. 9950, pp. 221–227. Springer, Cham (2016). doi: 10.1007/978-3-319-46681-1_27 CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Yuma Omori
    • 1
  • Hideaki Kawano
    • 1
  • Akinori Seo
    • 1
  • Zohreh Gholami Doborjeh
    • 2
  • Nikola Kasabov
    • 2
  • Maryam Gholami Doborjeh
    • 2
  1. 1.Kyushu Insititute of TechonologyKitakyushuJapan
  2. 2.Knowledge Engineering and Discovery Research InstituteAuckland University of TechnologyAucklandNew Zealand

Personalised recommendations