Nonlinear Analysis of Electroencephalogram in Writing-Disabled Children for a Better Understanding of Brain Functions

Document Type : Original Article (s)

Authors

1 Department of Biomedical Engineering, School of Engineering, University of Isfahan, Isfahan, Iran

2 Assistant Professor, Department of Biomedical Engineering, School of Engineering, University of Isfahan, Isfahan, Iran

3 Assistant Professor, Department of Biomedical Engineering, Islamic Azad University, Mashhad Branch, Mashhad, Iran

4 Assistant Professor, Department of Psychiatry, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran

Abstract

Background: Electroencephalogram (EEG) shows the electrical activity of the brain and is one of the most important diagnostic tools for neurological diseases and disabilities. Dysgraphia is one of the most common learning disabilities occurs regardless of the ability to read and is not due to intellectual impairments. Nonlinear methods are used in recent studies to access the electroencephalogram in children with dysgraphia.Methods: In this study, nonlinear analysis of electroencephalogram in writing-disabled children for a better understanding of brain functions was done. The Renyi entropy estimation and Welch power spectrum estimation methods were used.Findings: Writing-disabled children's brains were more complex at the time of writing than the rest condition as a result of more erratic behavior and thus, more asynchronous activation of neurons in the central brain zone. There was a higher proportion of Theta/Beta and Theta/Alpha in writing mood showed more brain insufficiency in writing compared to the rest condition.Conclusion: Neurofeedback, as a new approach in the treatment of learning disabilities, is proposed to modify the electrical activity of the brain in writing-disabled children.

Keywords

References

  1. Meyer-Lindenberg A. The evolution of complexity in human brain development: an EEG study. Electroencephalogr Clin Neurophysiol 1996; 99(5): 405-11.
  2. Sayyah Sayyari N. Learning disabilities. 2nd ed. Tehran, Iran: Modabber Publication; 2007. [In Persian].
  3. Heydari AR, Hafezi F, Tahankar Dezfuli M. Comparison of therapeutic effects of Fernald's multi-sensory and Kparts perceptual-motion in decreasing of writing disabilities of children. New Funding in Psychology 2009; 4(12): 65-78. [In Persian].
  4. Kirk S, Chalfant J. Academic and developmental learning disabilities. Denver, CO: Love Pub Co; 1984.
  5. Saavedra-Gastelum V, Rivera AL, Fernandez-Harmony T, Castano E, Castano VM. Signals from living biomaterials: analysis of human brain signals through wavelets. Mat Res Innov 2010; 14(3): 247-51.
  6. Ismail KA, Mansor W, Khuan LY, Che Wan Fadzal CWNF. Spectral analysis of EEG signals generated from imagined writing. Proceedings of the IEEE 8th International Colloquium on Signal Processing and its Applications (CSPA); 2012 Mar 23-25; Melaka, Malaysia; p. 510-13.
  7. Thatcher RW, North DN, Biver C. Electroencephalographic (EEG) discriminant analyses of children with learning disabilities: correlations to school achievement and neuropsychological performance. J Neurother 2004; 8: 119-23.
  8. Klimesch W, Doppelmayr M, Wimmer H, Gruber W, Rohm D, Schwaiger J, et al. Alpha and beta band power changes in normal and dyslexic children. Clin Neurophysiol 2001; 112(7): 1186-95.
  9. Butlers P. Method of the EEG operant conditioning for the children with learning disabilities. Neurosci Lett 2011; 500: 37-8.
  10. Geetha G, Geethalakshmi SN. Scrutinizing different techniques for artifact removal from EEG signals. Int J Eng Sci Tech 2011; 3(2): 1167-72.
  11. Tsui CSL, Pei J, Gan JQ, Huosheng H, Kui Y. EMG-based hands-free wheelchair control with EOG attention shift detection. Proceedings of IEEE International Conference on Robotics and Biomimetics; 2007 De 15-18; Sanya, china; p. 1266-71.
  12. Barreto AB, Scargle SD, Adjouadi M. A practical EMG-based human-computer interface for users with motor disabilities. J Rehabil Res Dev 2000; 37(1): 53-63.
  13. Mohammad Rezazadeh I, Firoozabadi SM, Hou H, Hashemi Golpayegani MR. Quantative evaluation of the efficiency of facial bio-potential signals based on forehead three-channel electrode placement for facial gesture recognition applicable in a human-machine interface. Iran J Med Phys 2010; 7(2): 79-65. [In Persian].
  14. Fernandez T, Herrera W, Harmony T, Diaz-Comas L, Santiago E, Sanchez L, et al. EEG and behavioral changes following neurofeedback treatment in learning disabled children. Clin Electroencephalogr 2003; 34(3): 145-52.
  15. Fernandez T, Harmony T, Fernandez-Bouzas A, Silva J, Herrera W, Santiago-Rodriguez E, et al. Sources of EEG activity in learning disabled children. Clin Electroencephalogr 2002; 33(4): 160-4.
  16. Monastra JV. Unlocking the potential of patients with ADHD: a model for clinical practice. Washington, DC: American Psychological Association; 2008. p. 156.
  17. Gao J, Hu J, Tung W. Entropy measures for biological signal analyses. Nonlinear Dyn 2012; 68(3): 431–44.
  18. Vandeput S, Verheyden B, Aubert AE, Van HS. Nonlinear heart rate dynamics: circadian profile and influence of age and gender. Med Eng Phys 2012; 34(1): 108-17.
  19. Easwaramoorthy D, Uthayakumar R. Estimating the complexity of biomedical signals by multifractal analysis. Proceedings of the 2010 IEEE Students' Technology Symposium (TechSym); 2010 Apr 3-4; Kharagpur, India; p. 6-11.
  20. Easwaramoorthy D, Uthayakumar R. Improved generalized fractal dimensions in the discrimination between Healthy and Epileptic EEG Signals. J Comput Sci 2011; 2(1): 31-8.
  21. Breteler MHM, Arns M, Peters S, Giepmans I, Verhoeven L. Improvements in spelling after QEEG-based neurofeedback in dyslexia: A randomized controlled treatment study. Appl Psychophysiology Biofeedback 2010; 35(1): 5-11.
  22. Walker JE. Case report: Dyslexia remediated with QEEG-guided neurofeedback. NeuroConnections 2010; 3: 28.
  23. Thornton KE, Carmody DP. Electroencephalogram biofeedback for reading disability and traumatic brain injury. Child & Adolescent Psychiatric Clinics of North America 2005; 14(1): 137-62.
  24. Walker JE, Norman CA. The neurophysiology of dyslexia: A selective review with implications for neurofeedback remediation and results of treatment in twelve consecutive cases. J Neurotherapy 2006; 10(1): 45-55.
Journal of Isfahan Medical School
Volume 32, Issue 283 - Serial Number 283
March and April 2014
Pages 558-568

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History

  • Receive Date: 03 February 2013
  • Revise Date: 26 May 2024
  • Accept Date: 06 April 2022

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