Iranian Journal of Medical Physics

Iranian Journal of Medical Physics

Feature Extraction of Visual Evoked Potentials Using Wavelet Transform and Singular Value Decomposition

Document Type : Original Paper

Authors
Ahmed Fadhil Almurshedi 1
Abd Khamim Ismail 2
Najwa Sulaiman 1
1 Department of Medical Physics, College of Science, Al-Karkh University of Science
2 Department of Physics, Faculty of Science, Universiti Teknologi Malaysia
10.22038/ijmp.2018.28583.1311
Abstract
Introduction: Brain visual evoked potential (VEP) signals are commonly known to be accompanied by high levels of background noise typically from the spontaneous background brain activity of electroencephalography (EEG) signals.
Material and Methods: A model based on dyadic filter bank, discrete wavelet transform (DWT), and singular value decomposition (SVD) was developed to analyze the raw data of visual evoked potentials and extract time-locked signals with external visual stimulation. A bio-amplifier (iERG 100P) and data acquisition system (OMB-DAQ-3000) were utilized to record EEG raw data from the human scalp. MATLAB Data Acquisition Toolbox, Wavelet Toolbox, and Simulink model were employed to analyze EEG signals and extract VEP responses.
Results: Results were verified in Simulink environment for the real recorded EEG data. The proposed model allowed precise decomposition and classification of VEP signals through the combined operation of DWT and SVD. DWT was successfully used for the decomposition of VEP signals to different frequencies followed by SVD for feature extraction and classification.
Conclusion: The visual and quantitative results indicated that the impact of the proposed technique of combining DWT and SVD was promising. Combining the two techniques led to a two-fold increase in the impact of peak signal to noise ratio of all the tested signals compared to using each technique individually.
Keywords
classification
Feature Extraction
Singular Value Decomposition (SVD)
Visual Evoked Potentials Wavelet Transform
Subjects
  1. Başar E. EEG-brain dynamics: relation between EEG and brain evoked potentials. Elsevier-North-Holland Biomedical Press. 1980.
  2. Galloway N. Human brain electrophysiology: Evoked potentials and evoked magnetic fields in science and medicine. The British Journal of Ophthalmology. 1990;74(4):255.
  3. Almurshedi AFH, Ismail AK. Measure Projection Analysis of VEP localization neuron generator. 2015 International Conference on BioSignal Analysis, Processing and Systems (ICBAPS), 2015 International Conference on 2015 May 26 (pp. 108-111). IEEE.
  4. Quiroga RQ, Basar E, Schürmann M, Urmann MS. Phase-Locking Of Event-Related Alpha Oscillations. 2000.
  5. Quian Quiroga R. Obtaining single stimulus evoked potentials with wavelet denoising. Physica D: Nonlinear Phenomena. 2000;145(3):278-92.
  6. Almurshedi A, Ismail AK. Puzzle task ERP response: time-frequency and source localization analysis. Translational Neuroscience. 2015;6(1):187-97. Doi:10.1515/tnsci-2015-0020.
  7. Grossmann A, Morlet J. Decomposition of Hardy functions into square integrable wavelets of constant shape. SIAM journal on mathematical analysis. 1984;15(4):723-36.
  8. Bertrand O, Bohorquez J, Pernier J. Time-frequency digital filtering based on an invertible wavelet transform: an application to evoked potentials. IEEE Transactions on Biomedical Engineering. . 1994;41(1):77-88.
  9. Demiralp T, Ademoglu A, Schürmann M, Basar-Eroglu C, Basar E. Detection of P300 waves in single trials by the wavelet transform (WT). Brain and language. 1999;66(1):108-28.
  10. Schiff SJ, Aldroubi A, Unser M, Sato S. Fast wavelet transformation of EEG. Electroencephalography and Clinical Neurophysiology. 1994;91(6):442-55.
  11. Almurshedi A, Ismail AK. Signal Refinement: Principal Component Analysis and Wavelet Transform of Visual Evoked Response. Research Journal of Applied Sciences, Engineering and Technology. 2015;9(2):106-12
  12. Gurley K, Kijewski T, Kareem A. First-and higher-order correlation detection using wavelet transforms. Journal of engineering mechanics. 2003;129(2):188-201.
  13. Blankertz B, Dornhege G, Krauledat M, Muller K, Kunzmann V, Losch F. The Berlin brain-computer interface: EEG-based communication without subject training. IEEE Trans Neural Syst Rehabil Eng. 2006;14(2):147-52. Doi:10.1109/TNSRE.2006.875557.
  14. Zhan XD, Kang T, Choi HR. An approach for pattern recognition of hand activities based on EEG and fuzzy neural network. Journal of mechanical science and technology. 2005;19(1):87-96.
  15. Zhang X, Diao W, Cheng Z. Wavelet transform and singular value decomposition of EEG signal for pattern recognition of complicated hand activities. Digital Human Modeling. Springer; 2007. 294-303.
  16. Liu Y, Sourina O, Nguyen MK. Real-time EEG-based human emotion recognition and visualization. Cyberworlds (CW), 2010 International Conference on; 2010: IEEE.
  17. Liu Y, Sourina O, Nguyen MK. Real-time EEG-based emotion recognition and its applications. Transactions on computational science XII. Springer; 2011. 256-77.
  18. Mackay AM, Bradnam MS, Hamilton R, Elliot AT, Dutton GN. Real-time rapid acuity assessment using VEPs: development and validation of the step VEP technique. Investigative ophthalmology & visual science. 2008;49(1):438-41. Doi:10.1167/iovs.06-0944.
  19. Al-maqtari MT, Taha Z, Moghavvemi M. Steady state-VEP based BCI for control gripping of a robotic hand. InTechnical Postgraduates (TECHPOS), 2009 International Conference for 2009 Dec 14 (pp. 1-3). IEEE.
  20. Guger C, Allison BZ, Grosswindhager B, Pruckl R, Hintermuller C, Kapeller C, et al. How Many People Could Use an SSVEP BCI? Frontiers in neuroscience. 2012;6:169. Doi:10.3389/fnins.2012.00169.
  21. Samar VJ, Swartz KP, Raghuveer MR. Multiresolution analysis of event-related potentials by wavelet decomposition. Brain and cognition. 1995;27(3):398-438. Doi:10.1006/brcg.1995.1028.
  22. Nakos G, Joyner D. Linear algebra with applications. Brooks/Cole Publishing Company; 1998.
  23. Hassanpour H, Mesbah M, Boashash B. Time-frequency feature extraction of newborn EEG seizure using SVD-based techniques. EURASIP Journal on Applied Signal Processing. 2004;2004:2544-54.
  24. Odom JV, Bach M, Barber C, Brigell M, Marmor MF, Tormene AP, et al. Visual evoked potentials standard. Documenta ophthalmologica. 2004;108(2):115-23.
  25. Odom JV, Bach M, Brigell M, Holder GE, McCulloch DL, Tormene AP. ISCEV standard for clinical visual evoked potentials. Documenta ophthalmologica. 2010;120(1):111-9.
  26. Kurita-Tashima S, Tobimatsu S, Nakayama-Hiromatsu M, Kato M. The neurophysiologic significance of frontal negativity in pattern-reversal visual-evoked potentials. Investigative ophthalmology & visual science. 1992;33(8):2423-8.
  27. Foxe JJ, Simpson GV. Flow of activation from V1 to frontal cortex in humans. Experimental Brain Research. 2002;142(1):139-50.
  28. Ocak H. Automatic detection of epileptic seizures in EEG using discrete wavelet transform and approximate entropy. Expert Systems with Applications. 2009;36:2027-36. DOI:10.1016/j.eswa.2007.12.065.
  29. Jahankhani P, Kodogiannis V, Revett K. EEG Signal Classification Using Wavelet Feature Extraction and Neural Networks. Modern Computing, 2006. JVA '06. IEEE John Vincent Atanasoff 2006 International Symposium. 2006; 3Oct: 120-4
  30. Odom JV, Bach M, Brigell M, Holder GE, McCulloch DL, Mizota A, et al. ISCEV standard for clinical visual evoked potentials. Documenta Ophthalmologica. 2016;133(1):1-9. Doi:10.1007/s10633-016-9553-y.

 

 

Iranian Journal of Medical Physics
Volume 15, Issue 4 - Serial Number 4
Autumn 2018
Pages 206-214

  • Receive Date 01 January 2018
  • Revise Date 08 April 2018
  • Accept Date 18 April 2018