FEATURE EXTRACTION OF ELECTROENCEPHALOGRAM SIGNAL GENERATED FROM WRITING IN DYSLEXIC CHILDREN USING DAUBECHIES WAVELET TRANSFORM

Authors

  • Zulkifli Mahmoodin Computational Physiologic Detection RIG, Pharmaceutical & Life Sciences Communities of Research, Faculty of Electrical Engineering, Universiti Teknologi MARA, 40450 Shah Alam, Selangor, Malaysia
  • Wahidah Mansor Computational Physiologic Detection RIG, Pharmaceutical & Life Sciences Communities of Research, Faculty of Electrical Engineering, Universiti Teknologi MARA, 40450 Shah Alam, Selangor, Malaysia
  • Lee Yoot Khuan Computational Physiologic Detection RIG, Pharmaceutical & Life Sciences Communities of Research, Faculty of Electrical Engineering, Universiti Teknologi MARA, 40450 Shah Alam, Selangor, Malaysia
  • Noor Bariah Mohamad Computational Physiologic Detection RIG, Pharmaceutical & Life Sciences Communities of Research, Faculty of Electrical Engineering, Universiti Teknologi MARA, 40450 Shah Alam, Selangor, Malaysia
  • Sariah Amirin Computational Physiologic Detection RIG, Pharmaceutical & Life Sciences Communities of Research, Faculty of Electrical Engineering, Universiti Teknologi MARA, 40450 Shah Alam, Selangor, Malaysia

DOI:

https://doi.org/10.11113/jt.v78.9071

Keywords:

Electroencephalogram, dyslexia, discrete wavelet transform, writing

Abstract

Dyslexia which causes learning deficiencies in reading and writing is due to a neurological disorder where the brain processes information differently. This paper describes the feature extraction of (EEG) signal using Daubechies wavelet transform. The EEG signals were recorded from capable and poor dyslexic children during writing activities of non-words. Brain learning pathway theories for reading and writing were used to localize electrode placement to 8 positions, namely C3, C4, P3, P4, T7, T8, FC5 and FC6. Daubechies provide the wavelet function shape that represent the type of features in an EEG signal well, detecting variations in frequencies that corresponds to activation of areas in relation to activities. Results showed that capable dyslexic subjects exhibit higher beta band power feature of the frontal (FC6) and parietal (P4) right hemisphere if compared to poor dyslexics, where the normal left hemisphere processing center was utilized. This indicates that the brain of dyslexic is compensating its deficiencies of the left brain with activation of areas to the right.  

References

Walker, J. E. Norman, C. A. 2006. The Neurophysiology Of Dyslexia: A Selective Review With Implications For Neurofeedback Remediation And Results Of Treatment In Twelve Consecutive Patients. Journal of Neurotheraphy. 10: 45-55.

Lyon, G. R. Shaywitz, S. E. Shaywitz, B. A. 2003. A Definition Of Dyslexia. Annals of Dyslexia. 53(1): 1-14.

Joseph, J. E., Noble, K., Eden, G. F. 2001. The Neurobiological Basis For Reading. Journal of Learning Disabilities. 34(6): 566-579.

Sprenger, M. 2013. Wiring The Brain For Reading: Brain-Based Strategies For Teaching Literacy. John Wiley & Sons.

Shaywitz, S. Morris, R. Shaywitz, B. 2008. The Education Of Dyslexic Children From Childhood To Young Adulthood. Annual Review of Psychology. 59: 451-475.

Heim, S. Keil, A. 2004. Large-Scale Neural Correlates Of Developmental Dyslexia. European Child and Adolescent Psychiatry. 13: 125-140.

PT Kushch, A. Gross-Glenn, K. Jallad, B. Lubs, H. Rabin, M. Feldman, E. Duara, R. 1993. Temporal Lobe Surface Area Measurements On MRI In Normal And Dyslexic Readers. Neuropsychology. 31: 811-821.

Che Wan Fadzal, C.W.N.F. Mansor, W. Khuan, L.Y. 2011. An Analysis of EEG Signal Generated From Grasping and Writing. International Conference on Computer Applications and Industrial Electronics (ICCAIE). 535-537.

Sanei, S. Chambers, J. A. 2007. EEG Signal Processing. John Wiley & Sons Ltd.

Pfurtscheller, G. Stancak Jr, A. Edlinger, G. 1997. On The Existence Of Different Types Of Central Beta Rhythms Below 30Hz. Electroencephalography and Clinical Neurophysiology. 102: 316-325.

Gupta, A. Agrawal, R. K. Kaur, B. 2014. Performance Enhancement Of Mental Task Classification Using EEG Signal: A Study Of Multivariate Feature Selection Methods. Soft Computing.

Murugappan, M. Ramachandran, N. Sazali, Y. 2010. Classification of human emotion from EEG using discrete wavelet transform. Journal Biomedical Science Engineering. 3(4): 390–396.

Hsu, W. Y. Sun, Y. N. 2009. EEG-based Motor Imagery Analysis Using Weighted Wavelet Transform Features. Journal Neuroscience Methods. 176(2): 310-318.

Ocak, H. 2009. Automatic detection of epileptic seizures in EEG Using Discrete Wavelet Transform And Approximate Entropy. Expert System Application. 36(2): 2027-2036.

Sherwood, J. Derakhshani, R. 2009. On Classifiability Of Wavelet Features For EEG-Based Brain-Computer Interfaces. International Joint Conference on Neural Networks.

Daubechies, I. 1990. The Wavelet Transform. Time–Frequency Localization And Signal Analysis. IEEE Trans Information Theory. 36(5): 961-1005.

Shaywitz, B. A. Shaywitz S. E. Pugh, K. R. Menel, W. E. Fulbright, R. K. Skudlarski, P. et al. 2002. Disruption Of Posterior Brain Systems For Reading In Children With Developmental Dyslexia. Biological Psychiatry. 52: 101-110.

Shaywitz, B. A. Lyon, G. R. Shaywitz, S. E. 2006. The Role Of Functional Magnetic Resonance Imaging In Understanding Reading And Dyslexia. Dev Neuropsychology.

Hoeft, F. McCandliss, B. D. Black, J. M. Gantman, A. Zakerani, N. Hulme, C. Lyytinen, H. Whitfield-Gabrieli, S. Glover, G. H. Reiss, A. L. Gabrieli, J. D. E. 2010. Neural Systems Predicting Long-Term Outcome In Dyslexia. Proceedings of the National Academy of Sciences of the United States of America. 108(1): 361-366.

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Published

2016-06-13

Issue

Section

Science and Engineering

How to Cite

FEATURE EXTRACTION OF ELECTROENCEPHALOGRAM SIGNAL GENERATED FROM WRITING IN DYSLEXIC CHILDREN USING DAUBECHIES WAVELET TRANSFORM. (2016). Jurnal Teknologi, 78(6-8). https://doi.org/10.11113/jt.v78.9071