Improve Memory for Alzheimer Patient by Employing Mind Wave on Virtual Reality with Deep Learning

  • Marwan Kadhim Mohammed Al-shammariEmail author
  • Gao Tian Han
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 773)


Alzheimer disease is associated with many risks, including the destruction of family morale and the loss of experience of many scientists in different areas. However, little research depending on computer science has been conducted to explore this disease. The purpose of this study is trying to find the possibility of using computer techniques to improve the therapeutic methods of Alzheimer disease. This paper elaborates the approach of using EEG signals on virtual reality environment and introducing them as a patient’s therapeutic program to improve temporary memory. The patient’s memory is rearranging based on a suitable brain signal through the theory of artificial neural network and deep learning technique so that the memory is able to be gradually improved.


EEG VR Alzheimer Artificial neural network Deep learning 



Northeastern university, China. Support this project with Neurosky and Emotiv headsets, to read EEG signal.


  1. 1.
    Baldonado, M., Chang, C.-C.K., Gravano, L., Paepcke, A.: The Stanford digital library metadata architecture. Int. J. Digit. Libr. 1, 108–121 (1997)CrossRefGoogle Scholar
  2. 2.
    Bruce, K.B., Cardelli, L., Pierce, B.C.: Comparing object encodings. In: Abadi, M., Ito, T. (eds.) Theoretical Aspects of Computer Software. Lecture Notes in Computer Science, vol. 1281, pp. 415–438. Springer, New York (1997)Google Scholar
  3. 3.
    van Leeuwen, J. (ed.): Computer Science Today. Recent Trends and Developments. Lecture Notes in Computer Science, vol. 1000. Springer, New York (1995)Google Scholar
  4. 4.
    Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs, 3rd edn. Springer-Verlag, New York (1996)zbMATHGoogle Scholar
  5. 5.
    Mackenzie, I.R., Munoz, D.G.: Nonsteroidal anti-inflammatory drug use and Alzheimer-type pathology in aging. Neurology 50(4), 986–990 (1998)CrossRefGoogle Scholar
  6. 6.
    Bar-On, P., Millard, C.B., Harel, M., Dvir, H., Enz, A., Sussman, J.L., Silman, I.: Kinetic and structural studies on the interaction of cholinesterases with the anti-Alzheimer drug rivastigmine. Biochemistry 41(11), 3555–3564 (2002)CrossRefGoogle Scholar
  7. 7.
    McKhann, G.M., Knopman, D.S., Chertkow, H., Hyman, B.T., Jack, C.R., Kawas, C.H., Klunk, W.E., Koroshetz, W.J., Manly, J.J., Mayeux, R., Mohs, R.C.: The diagnosis of dementia due to Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimer’s Dement. 7(3), 263–269 (2011)CrossRefGoogle Scholar
  8. 8.
    Cho, B.H., Lee, J.M., Ku, J.H., Jang, D.P., Kim, J.S., Kim, I.Y., Lee, J.H., Kim, S.I.: Attention enhancement system using virtual reality and EEG biofeedback. In: 2002 Proceedings of IEEE Virtual Reality, pp. 156–163. IEEE (2002)Google Scholar
  9. 9.
    Wiederhold, B.K., Jang, D.P., Kim, S.I., Wiederhold, M.D.: Physiological monitoring as an objective tool in virtual reality therapy. CyberPsychology Behav. 5(1), 77–82 (2002)CrossRefGoogle Scholar
  10. 10.
    Perhakaran, G., Yusof, A.M., Rusli, M.E., Yusoff, M.Z.M., Mahalil, I., Zainuddin, A.R.R.: A study of meditation effectiveness for virtual reality based stress therapy using EEG measurement and questionnaire approaches. In: Innovation in Medicine and Healthcare 2015, pp. 365–373. Springer, Cham (2016)Google Scholar
  11. 11.
    de Oliveira, J.M., Fernandes, R.C.G., Pinto, C.S., Pinheiro, P.R., Ribeiro, S., de Albuquerque, V.H.C.: Novel virtual environment for alternative treatment of children with cerebral palsy. Comput. Intell. Neurosci. (2016)Google Scholar
  12. 12.
    Teplan, M.: Fundamentals of EEG measurement. Measur. Sci. Rev. 2(2), 1–11 (2002)Google Scholar
  13. 13.
    Burdea Grigore, C., Coiffet, P.: Virtual Reality Technology. Wiley-Interscience, London (1994)Google Scholar
  14. 14.
    LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521(7553), 436–444 (2015)CrossRefGoogle Scholar
  15. 15.
    Bengio, Y.: Learning deep architectures for AI. Found. Trends® Mach. Learn. 2(1), 1–127 (2009)CrossRefGoogle Scholar
  16. 16.
    Schalkoff, R.J.: Artificial Neural Networks, vol. 1. McGraw-Hill, New York (1997)zbMATHGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  • Marwan Kadhim Mohammed Al-shammari
    • 1
    Email author
  • Gao Tian Han
    • 1
  1. 1.Northeastern UniversityShenyangChina

Personalised recommendations