EEG Comparison Between Normal and Developmental Disorder in Perception and Imitation of Facial Expressions with the NeuCube | SpringerLink
Skip to main content

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

  • Conference paper
  • First Online:
Neural Information Processing (ICONIP 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10637))

Included in the following conference series:

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.

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

Access this chapter

Subscribe and save

Springer+ Basic
¥17,985 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
JPY 3498
Price includes VAT (Japan)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
JPY 11439
Price includes VAT (Japan)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
JPY 14299
Price includes VAT (Japan)
  • 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

References

  1. Gallese, V., Fadiga, L., Fogassi, L., Rizzolatti, G.: Action recognition in thepremotor cortex. Brain 119, 593–609 (1996)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. 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)

    Article  Google Scholar 

  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. 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)

    Article  Google Scholar 

  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

    Chapter  Google Scholar 

  10. Song, S., Miller, K.D., Abbott, L.F.: Competitive Hebbian learning throughspike- timing-dependent synaptic plasticity. Nat. Neurosci. 3, 919–926 (2000)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. 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. 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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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

    Chapter  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Yuma Omori .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Omori, Y., Kawano, H., Seo, A., Doborjeh, Z.G., Kasabov, N., Doborjeh, M.G. (2017). EEG Comparison Between Normal and Developmental Disorder in Perception and Imitation of Facial Expressions with the NeuCube. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10637. Springer, Cham. https://doi.org/10.1007/978-3-319-70093-9_63

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-70093-9_63

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-70092-2

  • Online ISBN: 978-3-319-70093-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics