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A Review of Electroencephalogram-Based Analysis and Classification Frameworks for Dyslexia

  • Harshani Perera
  • Mohd Fairuz Shiratuddin
  • Kok Wai Wong
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9950)

Abstract

Dyslexia is a hidden learning disability that causes difficulties in reading and writing despite average intelligence. Electroencephalogram (EEG) is one of the upcoming methods being researched for identifying unique brain activation patterns in dyslexics. This paper examines pros and cons of existing EEG-based analysis and classification frameworks for dyslexia and recommends optimizations through the findings to assist future research.

Keywords

Dyslexia Electroencephalogram Feature extraction Artifact removal Artifact subspace reconstruction Support vector machine Classification 

References

  1. 1.
    Fletcher, J.M., Lyon, G.R., Fuchs, L.S., Barnes, M.A.: Learning Disabilities: From Identification to Intervention. Guilford Press, New York (2006)Google Scholar
  2. 2.
    Ekhsan, H.M., Ahmad, S.Z., Halim, S.A., Hamid, J.N., Mansor, N.H.: The implementation of interactive multimedia in early screening of dyslexia. In: 2012 International Conference on Innovation Management and Technology Research (ICIMTR), 21–22 May 2012, pp. 566–569 (2012). doi: 10.1109/ICIMTR.2012.6236459
  3. 3.
    Mohamad, S., Mansor, W., Lee, K.Y.: Review of neurological techniques of diagnosing dyslexia in children. In: 2013 IEEE 3rd International Conference on System Engineering and Technology (ICSET), 19–20 August 2013, pp. 389–393 (2013). doi: 10.1109/ICSEngT.2013.6650206
  4. 4.
    Nunez, P.L., Srinivasan, R.: Electric Fields of the Brain: The Neurophysics of EEG. Oxford University Press, Oxford (2006)CrossRefGoogle Scholar
  5. 5.
    Arns, M., Peters, S., Breteler, R., Verhoeven, L.: Different brain activation patterns in dyslexic children: evidence from EEG power and coherence patterns for the double-deficit theory of dyslexia. J. Integr. Neurosci. 6(1), 175–190 (2007). doi: 10.1142/S0219635207001404 CrossRefGoogle Scholar
  6. 6.
    Andreadis, I.I., Giannakakis, G.A., Papageorgiou, C., Nikita, K.S.: Detecting complexity abnormalities in dyslexia measuring approximate entropy of electroencephalographic signals. In: Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2009, 3–6 September 2009, pp. 6292–6295 (2009). doi: 10.1109/IEMBS.2009.5332798
  7. 7.
    Giannakakis, G.A., Tsiaparas, N.N., Xenikou, M.F.S., Papageorgiou, C., Nikita, K.S: Wavelet entropy differentiations of event related potentials in dyslexia. In: 8th IEEE International Conference on BioInformatics and BioEngineering, BIBE 2008, 8–10 October 2008, pp. 1–6 (2008). doi: 10.1109/BIBE.2008.4696836
  8. 8.
    Che Wan Fadzal, C.W.N.F., Mansor, W., Lee, K.Y., Mohamad, S., Amirin, S.: Frequency analysis of EEG signal generated from dyslexic children. In: 2012 IEEE Symposium on Computer Applications and Industrial Electronics (ISCAIE), 3–4 December 2012, pp. 202–204 (2012). doi: 10.1109/ISCAIE.2012.6482096
  9. 9.
    Che Wan Fadzal, C.W.N.F., Mansor, W., Lee, K.Y., Mohamad, S., Mohamad, N., Amirin, S.: Comparison between characteristics of EEG signal generated from dyslexic and normal children. In: 2012 IEEE EMBS Conference on Biomedical Engineering and Sciences (IECBES), 17–19 December 2012, pp. 943–946 (2012). doi: 10.1109/IECBES.2012.6498210
  10. 10.
    Andrew Ng, C.R., Leong, W.Y.: An EEG-based approach for left-handedness detection. Biomed. Sig. Process. Control 10, 92–101 (2014). doi: 10.1016/j.bspc.2014.01.005 CrossRefGoogle Scholar
  11. 11.
    Frid, A., Breznitz, Z.: An SVM based algorithm for analysis and discrimination of dyslexic readers from regular readers using ERPs. In: 2012 IEEE 27th Convention of Electrical & Electronics Engineers in Israel (IEEEI), 14–17 November 2012, pp. 1–4 (2012). doi: 10.1109/EEEI.2012.6377068
  12. 12.
    Karim, I., Abdul, W., Kamaruddin, N.: Classification of dyslexic and normal children during resting condition using KDE and MLP. In: 2013 5th International Conference on Information and Communication Technology for the Muslim World (ICT4 M), 26–27 March 2013, pp. 1–5 (2013). doi: 10.1109/ICT4M.2013.6518886
  13. 13.
    Fuad. N., Mansor, W., Lee, K.Y.: Wavelet packet analysis of EEG signals from children during writing. In: 2013 IEEE Symposium on Computers & Informatics (ISCI), 7–9 April 2013, pp, 228–230 (2013). doi: 10.1109/ISCI.2013.6612408
  14. 14.
    Israel, G.D.: Determining sample size. University of Florida Cooperative Extension Service, Institute of Food and Agriculture Sciences, EDIS (1992)Google Scholar
  15. 15.
    Provins, K.A., Cunliffe, P.: The relationship between E.E.G. activity and handedness. Cortex 8(2), 136–146 (1972). doi: 10.1016/S0010-9452(72)80014-5 CrossRefGoogle Scholar
  16. 16.
    Ziegler, J.C., Castel, C., Pech-Georgel, C., George, F., Alario, F.X., Perry, C.: Developmental dyslexia and the dual route model of reading: simulating individual differences and subtypes. Cognition 107(1), 151–178 (2008). doi: 10.1016/j.cognition.2007.09.004 CrossRefGoogle Scholar
  17. 17.
    Mullen, T., Kothe, C., Chi, Y.M., Ojeda, A., Kerth, T., Makeig, S., Cauwenberghs, G., Jung, T.-P.: Real-time modeling and 3D visualization of source dynamics and connectivity using wearable EEG. In: 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), United States, pp. 2184–2187. IEEE (2013). doi: 10.1109/EMBC.2013.6609968
  18. 18.
    Eslahi, S.V., Dabanloo, N.J.: Fuzzy support vector machine analysis in EEG classification (2013)Google Scholar
  19. 19.
    Lotte, F., Congedo, M., Lécuyer, A., Lamarche, F., Arnaldi, B.: A review of classification algorithms for EEG-based brain–computer interfaces. J. Neural Eng. 4, R1 (2007)CrossRefGoogle Scholar
  20. 20.
    Garrett, D., Peterson, D.A., Anderson, C.W., Thaut, M.H.: Comparison of linear, nonlinear, and feature selection methods for EEG signal classification. IEEE Trans. Neural Syst. Rehabil. Eng. 11(2), 141–144 (2003). doi: 10.1109/TNSRE.2003.814441 CrossRefGoogle Scholar
  21. 21.
    Liu, S., Song, Q., Hu, W., Cao, A.: Diseases classification using support vector machine (SVM). In: Proceedings of the 9th International Conference on Neural Information Processing, ICONIP 2002, 18–22 November 2002, vol. 762, pp. 760–763 (2002). doi: 10.1109/ICONIP.2002.1198160

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Harshani Perera
    • 1
  • Mohd Fairuz Shiratuddin
    • 1
  • Kok Wai Wong
    • 1
  1. 1.School of Engineering and Information TechnologyMurdoch UniversityMurdochAustralia

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