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

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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: 02 November 2024
  • Accept Date: 06 April 2022

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