Abstract
Dyslexia is seen as learning disorder that causes learners having difficulties to recognize the word, be fluent in reading and to write accurately. This is characterized by a deficit in the region associated with learning pathways in the brain. Activities in this region can be investigated using electroencephalogram (EEG). In this work, Discrete Wavelet Transform (DWT) with Daubechies order of 2 (db2) based features extraction was applied to the EEG signal and the power is calculated. The differences between beta and theta band with responding to learning activities were explored. Multiclass Support Vector Machine (SVM) was used to classify the EEG signal. Performance comparison of Polynomial and Radial Basis Function (RBF) kernel recognizing EEG signal during writing word and non-word is presented in this paper. It was found that SVM with RBF kernel performance was generally higher than that of the polynomial kernel in recognizing normal, poor and capable dyslexic children. The SVM with RBF kernel produced 91% accuracy compared to the polynomial kernel.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
E. S. Norton, S. D. Beach, and J. DE Gabrieli, “Neurobiology of dyslexia,” Curr. Opin. Neurobiol., vol. 30, pp. 73–78, Feb. 2015.
U. Goswami, “Dyslexia, Developmental,” in International Encyclopedia of the Social & Behavioral Sciences, vol. 6, Elsevier, 2015, pp. 727–730.
Ministry of Education, “Instrumen Senarai Semak Disleksia,” 2011.
B. P. K. K. P. Malaysia, “Data Pendidikan Khas 2016,” 2016.
Z. Mahfuzah, “Statistik Murid Disleksia di Malaysia,” 2017. [Online]. Available: https://www.mahfuzahzainol.com/single-post/2017/12/06/Statistik-Murid-Disleksia-di-Malaysia. [Accessed: 19-Jan-2018].
Y. Sun, J. Lee, and R. Kirby, “Brain Imaging Findings in Dyslexia,” Pediatr. Neonatol., vol. 51, no. 2, pp. 89–96, Apr. 2010.
S. Mohamad, W. Mansor, L. Y. Khuan, C. W. N. F. C. W. Fadzal, N. Mohammad, and S. Amirin, “Development of computer-based assessment for brain electrophysiology technique of dyslexic children,” in 2016 IEEE Symposium on Computer Applications & Industrial Electronics (ISCAIE), 2016, pp. 79–83.
S. Qureshi and S. Vanichayobon, “Evaluate different machine learning techniques for classifying sleep stages on single-channel EEG,” in 2017 14th International Joint Conference on Computer Science and Software Engineering (JCSSE), 2017, pp. 1–6.
S. Siuly and Y. Li, “Designing a robust feature extraction method based on optimum allocation and principal component analysis for epileptic EEG signal classification,” Comput. Methods Programs Biomed., vol. 119, no. 1, pp. 29–42, 2015.
X. Li, X. Chen, Y. Yan, W. Wei, and Z. J. Wang, “Classification of EEG signals using a multiple kernel learning support vector machine,” Sensors (Basel)., vol. 14, no. 7, pp. 12784–12802, 2014.
D. C. Hammond, “What is Neurofeedback: An Update,” J. Neurother., vol. 15, no. 4, pp. 305–336, 2011.
T. Gandhi, B. K. Panigrahi, and S. Anand, “A comparative study of wavelet families for EEG signal classification,” Neurocomputing, vol. 74, no. 17, pp. 3051–3057, 2011.
X. Liu, C. Gao, and P. Li, “A comparative analysis of support vector machines and extreme learning machines,” Neural Networks, vol. 33, pp. 58–66, 2012.
Y. Ma, X. Ding, Q. She, Z. Luo, T. Potter, and Y. Zhang, “Classification of Motor Imagery EEG Signals with Support Vector Machines and Particle Swarm Optimization,” Comput. Math. Methods Med., vol. 2016, no. 5, pp. 667–677, 2016.
E. A. Zanaty, “Support Vector Machines (SVMs) versus Multilayer Perception (MLP) in data classification,” Egypt. Informatics J., vol. 13, no. 3, pp. 177–183, 2012.
C. K. I. Williams, “Learning With Kernels: Support Vector Machines, Regularization, Optimization, and Beyond,” J. Am. Stat. Assoc., vol. 98, no. 462, pp. 489–489, Jun. 2003.
Acknowledgements
This work was supported by Fundamental Research Grant Scheme (FRGS), Malaysia (600-RMI/FRGS 5/3(137/2015)). The authors would like to thank Ministry of Higher Education, Malaysia, Research Management Institute and Faculty of Electrical Engineering, Universiti Teknologi MARA, Shah Alam, for financial support, facilities and various contributions, and to Dyslexia Association Malaysia for their assistance.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Ethics declarations
The authors declare that they have no conflict of interest.
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Zainuddin, A.Z.A., Mansor, W., Lee, K.Y., Mahmoodin, Z. (2019). Comparison Between Support Vector Machine with Polynomial and RBF Kernels Performance in Recognizing EEG Signals of Dyslexic Children. In: Lhotska, L., Sukupova, L., Lacković, I., Ibbott, G. (eds) World Congress on Medical Physics and Biomedical Engineering 2018. IFMBE Proceedings, vol 68/3. Springer, Singapore. https://doi.org/10.1007/978-981-10-9023-3_17
Download citation
DOI: https://doi.org/10.1007/978-981-10-9023-3_17
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-9022-6
Online ISBN: 978-981-10-9023-3
eBook Packages: EngineeringEngineering (R0)