Wavelet Design for Automatic Real-Time Eye Blink Detection and Recognition in EEG Signals

  • Michael Gabriel Miranda Department of Informatic Engineering Metropolitan University of Technology, Chile. Jose Pedro Alessandri 1242, ~Nu~noa, Santiago, Chile
  • Renato Alberto Salinas Department of Mechanical Engineering University of Santiago, Chile Av. Libertador Bernardo O'Higgins 3363, Santiago, Chile.
  • Ulrich Raff Department of Physics University of Santiago, Chile Av. Libertador Bernardo O'Higgins 3363, Santiago, Chile.
  • Oscar Magna Department of Informatic Engineering Metropolitan University of Technology, Chile Jose Pedro Alessandri 1242, ~Nu~noa, Santiago, Chile

Abstract

The blinking of an eye can be detected in electroencephalographic (EEG) recordings and can be understood as a useful control signal in some information processing tasks. The detection of a specific pattern associated with the blinking of an eye in real time using EEG signals of a single channel has been analyzed. This study considers both theoretical and practical principles enabling the design and implementation of a system capable of precise real-time detection of eye blinks within the EEG signal. This signal or pattern is subject to considerable scale changes and multiple incidences. In our proposed approach, a new wavelet was designed to improve the detection and localization of the eye blinking signal. The detection of multiple occurrences of the blinking perturbation in the recordings performed in real-time operation is achieved with a window giving a time-limited projection of an ongoing analysis of the sampled EEG signal.

References

[1] Aeschbach, D.; Borbely, A. (1993). All-night dynamics of the human sleep EEG, Journal of Sleep Research, 2, 70-81, 1993.
https://doi.org/10.1111/j.1365-2869.1993.tb00065.x

[2] Bayliss, J. (2001). Flexible Brain Computer Interface, PhD thesis, Comp. Science Dept., University of Rochester, 2001.

[3] Binnie, C.; Cooper, R.; Maguire, F.; Osselton, J.; Prior, P.; Tedman, B.(2003). Clinical Neurophysiology, Elsevier Academic Press, 2003.

[4] Buzsaki, G. (2006). Rhythms of the Brain; Oxford University Press, Inc., ISBN-13 978-0- 19-530106-9, 2006.
https://doi.org/10.1093/acprof:oso/9780195301069.001.0001

[5] Chambayil, B.; Singla, R.; Jha, R. (2010). EEG Eye Blink Classification Using Neural Network, Proceedings of the World Congress on Engineering, Vol I. WCE 2010, London, U.K. 2010

[6] Daubechies, I. (1992). Ten Lectures on Wavelets (CBMS-NSF Regional Conference Series in Applied Mathematics), Society for Industrial & Applied Mathematics, U.S. 1992.

[7] Dzitac, I.; Vesselenyi, T.; Tarca, R.C. (2011). Identification of ERD using Fuzzy Inference Systems for Brain-Computer Interface, International Journal of Computers Communications & Control, 6(3), 403-417, 2011.
https://doi.org/10.15837/ijccc.2011.3.2126

[8] Fisch B.J. (1999). EEG PRIMER Basic Principles of Digital and Analog EEG. 3rd Edition, Elsevier Academic Press, 1999.

[9] Gilmore R.L. (1994). American electroencephalographic society guidelines in electroencephalography, evoked potentials, and polysomnography, Journal of Clinical Neurophysiology, 11, 147, 1994.
https://doi.org/10.1097/00004691-199401000-00020

[10] Gloor P. (1969). Hans Berger on the Electroencephalogram of Man. Amsterdam, Elsevier Publishing Company, 1969.

[11] Mallat, S. (1989). A theory for multiresolution signal decomposition: the wavelet representation, IEEE Transactions on Pattern Analysis and Machine Intelligence, 11(7), 674-693, 1989.
https://doi.org/10.1109/34.192463

[12] Mallat, S. (2009). A Wavelet Tour of Signal Processing, The Sparse Way. 3rd Ed., Library of Congress Cataloging-in-Publication. Data, Elsevier Inc. Burlington, EEUU, 205-206, 2009.

[13] Neurosky Inc. (2016). BCI: Entertainment's Brain Hacking Tool for Control & Monitoring, http://neurosky.com/wp-content/uploads/2016/06/Control-vs-Monitor.pdf

[14] Misiti, M.; Misiti, Y.; Oppenheim, G.; Poggi, J.M. (2007); Wavelets and Their Applications, ISTE Ltd., 115-131, 2007.
https://doi.org/10.1002/9780470612491

[15] Polkko J. (2007). A Method for Detecting Eye Blinks from Single-Channel Biopotential Signal in the Intensive Care, Unit. Master's Thesis, 2007.

[16] Salinas R., E. Schachter and M. Miranda. (2012). Recognition and Real-Time Detection of Blinking Eyes on Electroencephalographic Signals Using Wavelet Transform, Lecture Notes in Computer Science, 7441, 682-690, 2012.
https://doi.org/10.1007/978-3-642-33275-3_84

[17] Senthil Kumar, P.; Arumuganathan, R.; Sivakumar, K.; Vimal, C. (2008). Removal of Ocular Artifacts in the EEG through Wavelet Transform without using an EOG Reference Channel, International Journal of Open Problems in Computer Science and Mathematics, 1, 188-200, 2008.

[18] Sharbroug, F.; Chatrian, G.E.; Lesser,R.P.; Luders, H.; Nuwer, M.; Picton, T.W. (1991). American Electroencephalographic Society Guidelines for Standard Electrode Position Nomenclature. Journal of Clinical Neurophysiology, 8, 200-202, 1991.
https://doi.org/10.1097/00004691-199104000-00007
Published
2019-05-31
How to Cite
MIRANDA, Michael Gabriel et al. Wavelet Design for Automatic Real-Time Eye Blink Detection and Recognition in EEG Signals. INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL, [S.l.], v. 14, n. 3, p. 375-387, may 2019. ISSN 1841-9844. Available at: <http://univagora.ro/jour/index.php/ijccc/article/view/3516>. Date accessed: 12 july 2020. doi: https://doi.org/10.15837/ijccc.2019.3.3516.

Keywords

Biological signals, electroencephalogram, brain computer interface, eye blink detection, pattern recognition, wavelet design