The feature extraction and estimation of a steady-state visual evoked potential by the Karhunen-Loeve expansion

Authors

DOI:

https://doi.org/10.15587/1729-4061.2017.91861

Keywords:

steady-state visual evoked potential, linear random process, Karhunen-Loeve expansion

Abstract

The study justifies using the Karhunen-Loeve expansion (KLE) for the feature extraction of a steady-state visual evoked potential (SSVEP), with the research results contributing to this subject area. The obtained parameters that sufficiently describe the study process allow constructing the information technology of visual analyzer diagnostics. It will help ophthalmologists reveal the characteristics of a disease and determine the right diagnosis.

To obtain the research results, the Karhunen-Loeve expansion of the SSVEP was implemented at different stimulation frequencies, which helped determine the optimal number of the informative features: 6 Hz – 18, 8 Hz – 15, and 10 Hz – 12. The expansion results of two one-channel and one two-channel SSVEPs were compared to establish the fact of the channels’ correlation effects on the number of the informative parameters. It has been proved that, taking into account the interference between the registration channels, it is possible to use fewer informative parameters for diagnostics. The obtained results will be used at the information technology of ophthalmologic diagnostics; the stage of their evaluation and the number of informative parameters are critically important because it all affects the accuracy and reliability of a diagnosis.

Author Biographies

Maria Stadnyk, Ternopil Ivan Puluj National Technical University Ruska str., 56, Ternopil, Ukraine, 46001

Assistant

Department of Computer Science 

Mykhailo Fryz, Ternopil Ivan Puluj National Technical University Ruska str., 56, Ternopil, Ukraine, 46001

PhD, Associate Professor

Department of Computer Science 

Leonid Scherbak, National Aviation University Kosmonavta Komarova ave., 1, Kyiv, Ukraine, 03058

Doctor of Technical Science, Professor

Department of Information and Measuring System 

References

  1. Vialatte, F.-B., Maurice, M., Dauwels, J., Cichocki, A. (2010). Steady-state visually evoked potentials: Focus on essential paradigms and future perspectives. Progress in Neurobiology, 90 (4), 418–438. doi: 10.1016/j.pneurobio.2009.11.005
  2. Baker, D. H., Simard, M., Saint-Amour, D., Hess, R. F. (2015). Steady-State Contrast Response Functions Provide a Sensitive and Objective Index of Amblyopic Deficits. Investigative Ophthalmology & Visual Science, 56 (2), 1208–1216. doi: 10.1167/iovs.14-15611
  3. Ellis, K. A., Silberstein, R. B., Nathan, P. J. (2006). Exploring the temporal dynamics of the spatial working memory n-back task using steady state visual evoked potentials (SSVEP). NeuroImage, 31 (4), 1741–1751. doi: 10.1016/j.neuroimage.2006.02.014
  4. Rady, A., Elkholy, O., Abou-El-Wafa, H., Ramadan, I. (2011). Visual Evoked Potential (VEP) in schizophrenia and psychotic depression. World Journal of Life Sciences and Medical Research, 1 (2), 11–14.
  5. Vangelis, P. O., Liaros, G., Georgiadis, K., Chatzilari, E., Adam, K., Nikolopoulos, S., Kompatsiaris, I. (2016). Comparative evaluation of state-of-the-art algorithms for SSVEP-based BCIs. Technical Report. Available at: https://arxiv.org/pdf/1602.00904v2.pdf
  6. Silberstein, R. B., Nield, G. E. (2012). Measuring Emotion in Advertising Research: Prefrontal Brain Activity. IEEE Pulse, 3 (3), 24–27. doi: 10.1109/mpul.2012.2189172
  7. Kamel, N., Hani, A. F. (2011). Single-trial subspace-based approach for VEP extraction. IEEE Transaction Biomedical Engineering, 58 (5), 1383–1393. doi: 10.1109/tbme.2010.2101073
  8. Lopez, M. A., Pelayo, F., Madrid, E., Prieto, A. (2009). Statistical Characterization of Steady-State Visual Evoked Potentials and Their Use in Brain–Computer Interfaces. Neural Processing Letters, 29 (3), 179–187. doi: 10.1007/s11063-009-9102-8
  9. Diez, P. F., Correa, A. G., Leber, E. L. (2013). SSVEP Detection Using Adaptive Filters. IFMBE Proceedings, 1154–1157. doi: 10.1007/978-3-642-21198-0_293
  10. Talwar, T. S., Matharu, S. S. (2016). Classification of SSVEP based brain signals using discrete wavelet transform. International journal for research in applied science & engineering technology, 4, 421–426.
  11. Odom, J. V., M. Bach, M. Brigell et. al. (2016). ISCEV standard for clinical visual evoked potentials: (2016 update). Documenta Ophthalmologica, 133 (1), 1–9. doi: 10.1007/s10633-016-9553-y
  12. Fryz, M., Stadnyk, M. (2014). Linijnyj periodychnyj vypadkovyj proces jak matematychna model' ustalenogo zorovogo vyklykanogo potencialu. Modeljuvannja ta informacijni tehnologii', 72, 36–43.
  13. Fryz, M., Stadnyk, M. (2014). Matematychna model' dvokanal'nogo ustalenogo zorovogo vyklykanogo potencialu v zadachah rozrobky informacijnyh tehnologij oftal'modiagnostyky. Visnyk nacional'nogo Hmel'nyc'kogo universytetu, 1, 126–132.
  14. Gardner, W. A., Napolitano, A., Paura, L. (2006). Cyclostationarity: Half a century of research. Signal Processing, 86 (4), 639–697. doi: 10.1016/j.sigpro.2005.06.016
  15. Wang, L. (2008). Karhunen-Loeve Expansions and their Applications. The London School of Economics and Political Science, 292.
  16. Dony, R. S.; Rao, K. R., Yip, P. C. (Eds.) (2001). The Karhunen-Love Transform. The Transform and Data Compression Handbook. Boca Raton: CRC Press LLC. doi: 10.1201/9781420037388.ch1
  17. Gardner, W., Franks, L. (1975). Characterization of cyclostationary random signal processes. IEEE Transactions on Information Theory, 21 (1), 4–14. doi: 10.1109/tit.1975.1055338
  18. Marchenko, B. G., Pryjmak, M. V. (1999). Pobudova modeli ta analiz stohastychno periodychnyh navantazhen' energosystem. Praci instytutu elektrodynamiky Nacional'noi' akademii' nauk Ukrai'ny, 1, 129–153.
  19. Jackson, D. A. (1993). Stopping Rules in Principal Components Analysis: A Comparison of Heuristical and Statistical Approaches. Ecology, 74 (8), 2204–2214. doi: 10.2307/1939574

Downloads

Published

2017-02-13

How to Cite

Stadnyk, M., Fryz, M., & Scherbak, L. (2017). The feature extraction and estimation of a steady-state visual evoked potential by the Karhunen-Loeve expansion. Eastern-European Journal of Enterprise Technologies, 1(4 (85), 56–62. https://doi.org/10.15587/1729-4061.2017.91861

Issue

Vol. 1 No. 4 (85) (2017): Mathematics and Cybernetics - applied aspects

Section

Mathematics and Cybernetics - applied aspects

License

Copyright (c) 2017 Maria Stadnyk, Mykhailo Fryz, Leonid Scherbak

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

The consolidation and conditions for the transfer of copyright (identification of authorship) is carried out in the License Agreement. In particular, the authors reserve the right to the authorship of their manuscript and transfer the first publication of this work to the journal under the terms of the Creative Commons CC BY license. At the same time, they have the right to conclude on their own additional agreements concerning the non-exclusive distribution of the work in the form in which it was published by this journal, but provided that the link to the first publication of the article in this journal is preserved.

A license agreement is a document in which the author warrants that he/she owns all copyright for the work (manuscript, article, etc.).
The authors, signing the License Agreement with TECHNOLOGY CENTER PC, have all rights to the further use of their work, provided that they link to our edition in which the work was published.
According to the terms of the License Agreement, the Publisher TECHNOLOGY CENTER PC does not take away your copyrights and receives permission from the authors to use and dissemination of the publication through the world's scientific resources (own electronic resources, scientometric databases, repositories, libraries, etc.).
In the absence of a signed License Agreement or in the absence of this agreement of identifiers allowing to identify the identity of the author, the editors have no right to work with the manuscript.
It is important to remember that there is another type of agreement between authors and publishers – when copyright is transferred from the authors to the publisher. In this case, the authors lose ownership of their work and may not use it in any way.