Functional Connectivity Evaluation for Infant EEG Signals Based on Artificial Neural Network

  • Mhd Saeed SharifEmail author
  • Usman Naeem
  • Syed Islam
  • Amin Karami
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 869)


The employment of the brain signals electroencephalography (EEG) could supply a deep intuitive understanding for infants behaviour and their alertness level within the living environment. The study of human brain through a computer-based approach has increased significantly as it aims at the understanding of infants’ mind and measures their attention towards the surrounding activities. The artificial neural network achieved a significant level of success in different fields, such as pattern classification, decision making, prediction, and adaptive control by learning from a set of data and construct weight matrices to represent the learning patterns. This research study proposes an artificial neural network based approach to predict the rightward asymmetry or leftward asymmetry which reflects higher frontal functional connectivity in the frontal right and frontal left, respectively within infant’s brain. In the traditional methods, the value of asymmetry of the frontal (FA) functional connectivity is used to determine the rightward or the leftward asymmetry while the proposed approach is trying to predict that without going through all the levels of the calculation complexity. The achieved work will supply a deep understanding into the deployment of the functional connectivity to provide information on the interactions between different brain regions.


Electroencephalography Neural network EEG signals Infant attention Behaviour Signal features 


  1. 1.
    Ward, J.: The Students Guide to Cognitive Neuroscience. Psychology Press (2010)Google Scholar
  2. 2.
    Diagram of the Brain and its Functions. ©2000–2009 (2010)Google Scholar
  3. 3.
    Huang, R., Jung, T., Makeig, S.: Tonic Changes in EEG Power Spectra During Simulated Driving, pp. 394–403. Springer, Berlin (2009)CrossRefGoogle Scholar
  4. 4.
    Jung, T., Makeig, S., Stensmo, M., Sejnowski, T.J.: Estimating alertness from the EEG power spectrum. IEEE Transactions on Biomedical Engineering 44(1), 60–69 (1997)CrossRefGoogle Scholar
  5. 5.
    Subasi, A., Ccelebi, E.: Classification of EEG signals using neural network and logistic regression. Comput. Methods Progr. Biomed. 78(2), 87–99 (2005)CrossRefGoogle Scholar
  6. 6.
    Killane, I., Browett, G., Reilly, R.B.: Measurement of attention during movement: acquisition of ambulatory EEG and cognitive performance from healthy young adults. In: 35th Annual International Conference of the IEEE in Engineering in Medicine and Biology Society (EMBC), pp. 6397–6400 (2013)Google Scholar
  7. 7.
    Mizoguchi, F., Nishiyama, H., Iwasaki, H.: A new approach to detecting distracted car drivers using eye movement data. In: IEEE 13th International Conference on Cognitive Informatics and Cognitive Computing, pp. 266–272 (2014)Google Scholar
  8. 8.
    Soe, N.N., Wen, D.J., Poh, J.S., Li, Y.: Broekman, B.F.P., Chen, H., Chong, Y.S., Kwek, K., Saw, S.M., Gluckman, P.D., Meaney, M.J., Rifkin-Graboi, A., Qiu, A.: Pre- and post-natal maternal depressive symptoms in relation with infant frontal function. Connect. Behav. PLoS ONE 11(4), e0152991 (2016).
  9. 9.
    Aydore, S., Pantazis, D., Leahy, R.M.: A note on the phase locking value and its properties. NeuroImage 74, 231–244 (2013). Scholar
  10. 10.
    Hansen, E.C., Battaglia, D., Spiegler, A., Deco, G., Jirsa, V.K.: Functional connectivity dynamics: modeling the switching behaviour of the resting state. NeuroImage 105, 525–535 (2015).
  11. 11.
    iMotions Biometric Research Platform: EEG pocket guide. (2016)
  12. 12.
    Swartz Center for Computational Neuroscience. (2017)
  13. 13.
  14. 14.
    Wen, D.J., Soe, N.N., Sim, L.W., Sanmugam, S., Kwek, K., Chong, Y.S., Gluckman, P.D., Meaney, M.J., Rifkin-Graboi, A., Qiu, A.: Infant frontal EEG asymmetry in relation with postnatal maternal depression and parenting behavior. Transl. Psychiatry 7, e1057 (2017). Scholar
  15. 15.
    Quraan, M.A., Protzner, A.B., Daskalakis, Z.J., Giacobbe, P., Tang, C.W., Kennedy, S.H., Lozano, A.M., McAndrews, M.P.: EEG power asymmetry and functional connectivity as a marker of treatment effectiveness in DBS surgery for depression. Neuropsychopharmacology 39, 1270–1281 (2014)CrossRefGoogle Scholar
  16. 16.
    Thomas, J., Princy, R.T.: Human heart disease prediction system using data mining techniques. In: International Conference on Circuit, Power and Computing Technologies (ICCPCT), Nagercoil, pp. 1–5 (2016)Google Scholar
  17. 17.
    Chaitrali, M., Dangare, S., Apte, S.S.: A data mining approach for prediction of heart disease using risk factors. Int. J. Comput. Eng. Technol. (IJCET) 3(3), 30–40 (2012)Google Scholar
  18. 18.
    Amin, S.U., Agarwal, K., Beg, R.: Genetic neural network based data mining in prediction of heart disease using risk factors. In: IEEE Conference on Information & Communication Technologies, JeJu Island, pp. 1227–1231 (2013)Google Scholar
  19. 19.
    Kermani, B.G., Schiffman, S.S., Nagle, H.T.: Performance of the Levenberg-Marquardt neural network training method in electronic nose applications. Sens. Actuators B 110(1), 13–22 (2005)CrossRefGoogle Scholar
  20. 20.
    Bhaya, A., Kaszkurewicz, E.: Steepest descent with momentum for quadratic functions is a version of the conjugate gradient method. Neural Networks 17(1), 65–71 (2004)CrossRefGoogle Scholar
  21. 21.
    Sharif, M.S., Alsibai, M.H.: Medical data analysis based on nao robot: an automated approach towards robotic real-time interaction with human body. In: 7th IEEE International Conference on Control System, Computing and Engineering (ICCSCE) (2017)Google Scholar
  22. 22.
    Iranmanesh, S.: A differential adaptive learning rate method for back-propagation neural networks. In: Proceedings of the 10th WSEAS International Conference on Neural Networks (2009)Google Scholar
  23. 23.
    Sharif, M.S., Amira, A.: An intelligent system for PET tumour detection and quantification. In: Proceedings of the IEEE International Conference on Image Processing (ICIP), Nov 2009Google Scholar
  24. 24.
    Yu, X., Efe, M.O., Kaynak, O.: A backpropagation learning framework for feedforward neural networks. In: Proceedings of the IEEE International Symposium on Circuits and Systems (ISCAS ’01), vol. 3, pp. 700–702, May 2001Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Mhd Saeed Sharif
    • 1
    Email author
  • Usman Naeem
    • 1
  • Syed Islam
    • 1
  • Amin Karami
    • 1
  1. 1.School of Architecture, Computing and Engineering, College of Arts, Technology and InnovationUniversity WayLondonUK

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