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Reseach Article

Orthogonal and Biorthogonal Wavelet Analysis of Visual Evoked Potentials

by Ahmed Fadhil Hassoney, Abd Khamim Ismail, Hentabli Hamza
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 60 - Number 4
Year of Publication: 2012
Authors: Ahmed Fadhil Hassoney, Abd Khamim Ismail, Hentabli Hamza
10.5120/9684-4119

Ahmed Fadhil Hassoney, Abd Khamim Ismail, Hentabli Hamza . Orthogonal and Biorthogonal Wavelet Analysis of Visual Evoked Potentials. International Journal of Computer Applications. 60, 4 ( December 2012), 50-52. DOI=10.5120/9684-4119

@article{ 10.5120/9684-4119,
author = { Ahmed Fadhil Hassoney, Abd Khamim Ismail, Hentabli Hamza },
title = { Orthogonal and Biorthogonal Wavelet Analysis of Visual Evoked Potentials },
journal = { International Journal of Computer Applications },
issue_date = { December 2012 },
volume = { 60 },
number = { 4 },
month = { December },
year = { 2012 },
issn = { 0975-8887 },
pages = { 50-52 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume60/number4/9684-4119/ },
doi = { 10.5120/9684-4119 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:05:47.245114+05:30
%A Ahmed Fadhil Hassoney
%A Abd Khamim Ismail
%A Hentabli Hamza
%T Orthogonal and Biorthogonal Wavelet Analysis of Visual Evoked Potentials
%J International Journal of Computer Applications
%@ 0975-8887
%V 60
%N 4
%P 50-52
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In the present work the performance of orthogonal and Biorthogonal wavelet filters were analyzed for visual evoked potentials (VEP) on a variety of noisy signals. The signals were analyzed at different signal to noise ratio (SNR). This research proposed a method for the selection of the best analysis. The proposed method used longest common subsequence (LCS) and basic local alignment search tool (BLAST) to measure the analysis performance objectively and visual quality subjectively of the signal analysis. It was found that orthogonal wavelets outperform the biorthogonal ones in both the criteria especially at high noisy signal.

References
  1. Aoyagi, M. and Harada, J. 1988. Application of fast Fourier transform to auditory evoked brainstem response. Informatics for Health and Social Care, 13(3), 211-220.
  2. Kalayci, T. and Özdamar, Ö. 1995. Wavelet processing for automated neural network detection of EEG spikes. IEEE Engineering in Medicine and Biology Magazine, 14(2), 160–166.
  3. Qiao, X. and Yan, M. 2011. P300 Feature Extraction for Visual Evoked EEG Based on Wavelet Transform. AICI Springer-Verlag Berlin Heidelberg, 7003: 561–567.
  4. Weiderpass, H. A. , Yamamoto, J. F. , Salomao, S. R. , Berezovsky, A. , Pereira, J. M. , Sacai, P. Y. , Oliveira, J. P. , Costa, M. A. and Burattini, M. N. 2008. Steady-state sweep visual evoked potential processing denoised by wavelet transform. International Society for Optical Engineering, 6917: 69171A.
  5. Quiroga, R. Q. 2000. Obtaining single stimulus evoked potentials with wavelet denoising. Physica D: Nonlinear Phenomena, 145(3-4), 278-292.
  6. Quiroga, R. Q. , Sakowitz, O. W. , Basar, E. and Schürmann, M. 2001. Wavelet Transform in the analysis of the frequency composition of evoked potentials. Brain Research Protocols, 8(1), 16–24.
  7. Thie, J. , Sriram, P. , Klistorner, A. and Graham, S. L. 2012. Gaussian wavelet transform and classifier to reliably estimate latency of multifocal visual evoked potentials (mfVEP). Vision Research 52(1), 79–87.
  8. Akbari, M. and Azmi, R. 2011. Automatic Classification of Visual Evoked Potentials Based on Wavelet Analysis and Support Vector Machine. 6th International Advanced Technologies Symposium (IATS'11) Elaz??, Turkey 227-230.
  9. Samar, V. J. , Bopardikar, A. , Rao, R. and Swartz, K. 1999. Wavelet Analysis of Neuroelectric Waveforms: A Conceptual Tutorial. Brain and Language, 66(1), 7–60.
  10. Gupta, C. N. , Khan, Y. U. , Palaniappan, R. and Sepulveda, F. 2009. Wavelet Framework for Improved Target Detection in Oddball Paradigms Using P300 and Gamma Band Analysis. Biomedical Soft Computing and Human Sciences, 14(2): 61-67.
  11. Yong, P. A. , Hurley, N. J. and Silvestre, G. C. M. 2005. Single-trial EEG classification for brain-computer interface using wavelet decomposition. In European Signal Processing Conference, EUSIPCO.
  12. Kumari, S. and Vijay, R. 2011. Analysis of orthogonal and biorthogonal wavelet filters for image compression. International journal of computer applications, 21(5), 17-19.
  13. Odom, J. V. , Bach, M. , Brigell, M. E. , Holder, J. E. , McCulloch, D. L. , Tormene, A. P. and Vaegan. ISCEV standard for clinical visual evoked potentials (2009 update). Doc Ophthalmol, 120(1), 111–119.
Index Terms

Computer Science
Information Sciences

Keywords

Wavelet transforms longest common subsequence Basic Local Alignment Search Tool