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A Hybrid Approach for Artifacts Removal from EEG Recordings

by Alaa Eldeen M. Helal, Ahmed Farag Seddik, Ayat Allah F. Hussein
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 168 - Number 4
Year of Publication: 2017
Authors: Alaa Eldeen M. Helal, Ahmed Farag Seddik, Ayat Allah F. Hussein
10.5120/ijca2017914301

Alaa Eldeen M. Helal, Ahmed Farag Seddik, Ayat Allah F. Hussein . A Hybrid Approach for Artifacts Removal from EEG Recordings. International Journal of Computer Applications. 168, 4 ( Jun 2017), 10-19. DOI=10.5120/ijca2017914301

@article{ 10.5120/ijca2017914301,
author = { Alaa Eldeen M. Helal, Ahmed Farag Seddik, Ayat Allah F. Hussein },
title = { A Hybrid Approach for Artifacts Removal from EEG Recordings },
journal = { International Journal of Computer Applications },
issue_date = { Jun 2017 },
volume = { 168 },
number = { 4 },
month = { Jun },
year = { 2017 },
issn = { 0975-8887 },
pages = { 10-19 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume168/number4/27861-2017914301/ },
doi = { 10.5120/ijca2017914301 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:15:13.029209+05:30
%A Alaa Eldeen M. Helal
%A Ahmed Farag Seddik
%A Ayat Allah F. Hussein
%T A Hybrid Approach for Artifacts Removal from EEG Recordings
%J International Journal of Computer Applications
%@ 0975-8887
%V 168
%N 4
%P 10-19
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The electroencephalogram (EEG) is a widely used traditional procedure for diagnosing, monitoring and managing neurological disorders. Many artifact types that often contaminate EEG remain a key challenge for precise diagnosis of brain dysfunctions and neurological disorders. Hence, artifact removal is intuitively required for accurate EEG analysis and treatment. This paper presents a new extensive method that can remove a wide variety of EEG artifacts based mainly on Template Matching approach including multiple signal-processing tools. The method was evaluated and validated on real EEG data, giving promising results that offer better capabilities to neurophysiologists in routine EEG examinations and diagnosis.

References
  1. Mintaze Kerem Günel “Management of Epilepsy Research, Results and Treatment” InTech, Janeza Trdine 9, 51000 Rijeka, Croatia, 2011 DOI: 10.5772/1139, available at www.intechopen.com
  2. E. Niedermeyer and F. H. Lopes da Silva, Electroencephalography: basic principles, clinical applications, and related fields, 5th ed. Philadelphia: Lippincott Williams & Wilkins, 2010.
  3. LanlanYu, “EEG De-Noising Based on Wavelet Transformation” Bioinformatics and Biomedical Engineering, ICBBE 2009. 3rd International Conference, vol., no., pp.1-4, 11-13 June 2009.
  4. Wallstrom, Garrick L., et al. "Automatic correction of ocular artifacts in the EEG: a comparison of regression-based and component-based methods." International journal of psychophysiology 53.2 (2004): 105-119.
  5. Correa, A. Garcés, et al. "Artifact removal from EEG signals using adaptive filters in cascade." Journal of Physics: Conference Series. Vol. 90. No. 1, IOP Publishing, 2007
  6. Jung, T-P., et al. "Removing electroencephalographic artifacts: comparison between ICA and PCA." Neural Networks for Signal Processing VIII, 1998. Proceedings of the 1998 IEEE Signal Processing Society Workshop. IEEE, 1998
  7. Jung, Tzyy-Ping, et al. "Extended ICA removes artifacts from electroencephalographic recordings." Advances in neural information processing systems (1998): 894-900.
  8. Zikov, Tatjana, et al. "A wavelet based de-noising technique for ocular artifact correction of the electroencephalogram" Engineering in Medicine and Biology, 24th Annual Conference and the Annual Fall Meeting of the Biomedical Engineering Society EMBS/BMES Conference, Proceedings of the Second Joint. Vol. 1, IEEE, 2002
  9. M. Fatourechi, A. Bashashati, R. K. Ward, and G. E. Birch, “EMG and EOG artifacts in brain computer interface systems: a survey,” Clinical Neurophysiology, vol. 118, no. 3, pp. 480–494, 2007
  10. Julie Onton, Marissa Westerfield, Jeanne Townsend, Scott Makeig. “Review Imaging human EEG dynamics using independent component analysis”, Neuroscience and Biobehavioral Reviews 30 (2006) 808–822,Elsevier, oi:10.1016/j.neubiorev.2006.06.007
  11. Yuan Zou, John Hart and Roozbeh Jafari “Automatic EEG artifact removal based on ICA and hierarchical clustering”, IEEE international conference on Acoustics, Speech and Signal Processing (ICASSP) 2012, Kyoto, Japan
  12. M. Li, Y. Cui, and J. Yang, “Automatic removal of ocular artifact from EEG with DWT and ICA Method,” Applied Mathematics and Information Sciences, vol. 7, no. 2, pp. 809–816, 2013
  13. H. P. Huang, Y. H. Liu, C. P.Wang, andT. H. Huang, “Automatic artifact removal in EEG using independent component analysis and one-class classification strategy,” Journal of Neuroscience and Neuroengineering, vol. 2, no. 2, pp. 73–78, 2013
  14. I. Daly, M. Billinger, R. Scherer, and G. M¨uller-Putz, “On the automated removal of artifacts related to head movement from the EEG,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 21, no. 3, pp. 427–434, 2013
  15. Hyvärinen A. "Fast and robust fixed-point algorithms for independent component analysis", IEEE Trans Neural Netw 1999;10:626–34
  16. Frank RM, Frishkoff GA. “Automated protocol for evaluation of electromagnetic component separation (APECS): application of a framework for evaluating statistical methods of blink extraction from multichannel EEG”, Clin Neurophysiol 2007; 118:80–97
  17. G. Inuso, F. La Foresta, N. Mammone, and F.C. Morabito “Wavelet- ICA methodology for efficient artifact removal from Electroencephalographic recordings” In Proc International Joint Conference on Neural Networks, 2011, pp. 1524-1529
  18. Janett Walters-Williams and Yan Li, “A New Approach to Denoising EEG Signals – Merger of Translation Invariant Wavelet and ICA”. In International Journal of Biometric and Bioinformatics (IJBB), Volume 5, Issue 2, May 2011, pp 130 – 148
  19. R. Q. Quiroga, “Quantitative analysis of EEG signals: time-frequency methods and chaos theory,” Institute of Physiology-Medical University Lubeck and Institute of Signal Processing-Medical University Lubeck, 1998
  20. Lorena Orosco, Agustina Garcés Correa, Eric Laciar, (2013). “Review:A Survey of Performance and Techniques for Automatic Epilepsy Detection”. Journal of Medical and Biological Engineering, 33(6): 526-537 doi: 10.5405/jmbe.1463
  21. V.V.K.D.V. Prasad, P. Siddaiah, and B. Prabhaksrs Rao, “A New Wavelet Based Method for Denoising of Biological Signals”, International Journal of Computer Science and Network Security 8(1), 2008, 238-244
  22. R. Romo-Vazquez, R., Ranta, V. Louis-Dorr, and D. Maquin, “Ocular Artifacts Removal in Scalp EEG: Combining ICA and Wavelet Denoising”, In the Proceedings of Physics in Signal and Image Processing (PSISP 07), 2007
  23. P. Senthil Kumar, R. Arumuganathan, K. Sivakumar, and C. Vimal “A Wavelet based Statistical Method for De-noising of Ocular Artifacts in EEG Signals” International Journal of Computer Science and Network Security (IJCSNS), 8(9), pp. 87-92, 2013
  24. Y. Song and J. Zhang, “Automatic recognition of epileptic EEG patterns via Extreme Learning Machine and multiresolution feature extraction,” Expert Syst. Appl., vol. 40, no. 14, pp. 5477–5489, Oct. 2013
  25. B. Ferguson, D. Abbott, “Denoising Techniques for Terahertz Response of Biological Samples”, Microelectronics Journal 32, 943-953, 2001
  26. Ling Guo, D. Rivero, J. Dorado, Juan R.Rabunal, A. Pazos “Automatic epileptic seizure detection in EEG based on line length feature and artificial neural network” Journal of Neuroscience Methods, volume 191, issue 1, 101–109 (2010)
  27. Alain Manzo-Martínez, José A. and Camarena-Ibarrola, “A New and Efficient Alignment Technique by Cosine Distance” International Journal of Combinatorial Optimization Problems and Informatics, Vol. 4, No. 1, 2013, pp. 12-24.
  28. Mingai Li, Yan Cui, Jinfu Yang,”Automatic Removal of Ocular Artifact from EEG with DWT and ICA Method” Applied Mathematics & Information Sciences. (2013), Appl. Math. Inf. Sci. 7, No. 2, 809-816
  29. T. Zikov, S. Bibian, G. A. Durnont, M. Huzmezan, C. R. Ries, “A wavelet based de-noising technique for ocular artifact correction of the electroencephalogram” in Proceedings of the Second Joint EMBS/BMES Conference, Houston, TX, USA, 23-26 (2009).
  30. T. P. Jung, C. Humphries, T. W. Lee “Removing Electroencephalographic Artifacts: Comparison between ICA and PCA”, in Neural Networks for Signal Processing Proceedings of the 2007 IEEE Signal Processing Society Workshop, Cambridge, England, 63-72 (2007)
  31. XIE Song-yun, ZHANG Zhen-zhong, ZHANG Wei-ping, and ZHAO Hai-tao, “Method and application of removing noise from EEG signals based on ICA method” Chinese Journal of Medical Imaging Technology, 23, 1562-1565 (2011)
  32. H. N. Suresh and V. Balasubramanyam “Wavelet Transforms and Neural Network Approach for Epileptical EEG”, pp. 12–17, 2012
  33. R. Yadav, R. Agarwal and M. N. S. Swamy "Detection of epileptic seizures in stereo-EEG using frequency-weighted energy", Circuits and Systems, 50th Midwest Symposium, MWSCAS 2007, pp. 77-80
  34. B. S. Saini, Dilbag Singh, Moin Uddin, and Vinod Kumar “Improved power spectrum estimation for RR-interval time series” World Acad Sci Eng Technol, (2008), 46(10):44–48
  35. P. Jahankhani, V. Kodogiannis, and K. Revett, “EEG Signal Classification Using Wavelet Feature Extraction and Neural Networks” IEEE John Vincent Atanasoff 2006 Int. Symp. Mod. Comput., pp. 120–124, Oct. 2006
Index Terms

Computer Science
Information Sciences

Keywords

Electroencephalogram (EEG) artifacts removal independent component analysis wavelet cosine similarity measure

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