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Cluster Computing

Erschienen in:

30.12.2017

Evaluating algorithms of removing EOG artifacts with experimental data in brain computer interface

verfasst von: Wen Qi

Erschienen in: Cluster Computing | Sonderheft 4/2019

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Abstract

In this paper, the authors investigate and compare two types of algorithms for removing EOG artifacts in EEG measurements. First, the sources that introduce EOG artifacts are explained and the authors briefly review the existing algorithms for EOG artifacts removal in EEG signals. Two selected algorithms—recursive least square and blind source separation are discussed in details. These two algorithms were chosen either for their good performance or for their low computational charge to be used in ambulatory monitoring devices. In the third part, the experimental protocol to collect EOG and EEG measurements are described. In the fourth part, the authors summarize the results after applying two algorithms to the collected real EEG data. From these results, conclusions are drawn on the performances of these selected algorithms. Several possible improvements or challenges are proposed in the discussion section.

Vorheriger Artikel Research on time series data mining algorithm based on Bayesian node incremental decision tree

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Atwood, H.L., MacKay, W.A.:. Essentials of neurophysiology, B.C. Decker, Hamilton, Canada, 1989, pp. 200–205 (1993)

Tyner, F.S., Knott, J.R.: Fundamentals of EEG Technology: Basic Concepts and Methods, vol. 1. Raven Press, New York (1989)

Niedermeyer, E., Lopes da Silva, F.H.: Electroencephalog-Raphy: Basic Principles, Clinical Applications and Related Fields, 3rd edn, pp. 351–354. Lippincott, Williams & Wilkins, Philadelphia (1993)

Anderer, P., Roberts, S., Schlogl, A., Gruber, G., Klosch, G., Herrmann, W., et al.: Artifact processing in computerized analysis of sleep EEG a review. Neuropsychobiology 40, 150–157 (1999)CrossRef

McFarland, D.J., McCane, L.M., David, S.V., Wolpaw, J.R.: Spatial filter selection for EEG-based communication. Electroencephalogr. Clin. Neurophysiol. 103, 386–394 (1997)CrossRef

Goncharova II, D.J., McFarland, T.M., Vaughan, J.R.: Wolpaw, EMG contamination of EEG: spectral and topographical characteristics. Clin. Neurophysiol. 114, 1580–1593 (2003)CrossRef

McFarland, D.J., Sarnacki, W.A., Vaughan, T.M., Wolpaw, J.R.: Braincomputer interface (BCI) operation: signal and noise during early training sessions. Clin. Neurophysiol. 116, 56–62 (2005)CrossRef

Gratton, G., Coles, M.G.H., Donchin, E.: A new method for the online removal of ocular artifact. Electroencephalogr.-Phys. Clin. Neurophysiol. 55, 468–484 (1983)CrossRef

Kenemans, J.L., Molenaar, P.C.M., Verbaten, M.N., Slangen, J.L.: Removal of the ocular artifact from the EEG: a comparison of time and frequency domain methods with simulated and real data. Psychophysiology 28, 115–121 (1991)CrossRef

10.

Whitton, J.L., Lue, F., Moldofsky, H.: A spectral method for removing eye movement artifacts from the EEG. Electroencephalogr. Clin. Neurophysiol. 44, 735–741 (1978)CrossRef

11.

Woestengurg, J.C., Verbaten, M.N., Slangen, J.L.: The removal of the eye movement artifact from the EEG by regression analysis in the frequency domain. Biol. Physiol. 16, 127–147 (1983)

12.

Lagerlund, T.D., Sharbrough, F.W., Busacker, N.E.: Spatial filtering of multichannel electroencephalographic recordings through principal component analysis by singular value decomposition. Clin. Neurophysiol. 14(1), 73–82 (1997)CrossRef

13.

Elbert, T., Lutzenberger, W., Rockstroh, B., Birbaumer, N.: Removal of ocular artifacts from the EEG-a biophysical approach to the EOG. Electroencephalogr. Clin. Neurophysiol. 60, 455–463 (1985)CrossRef

14.

Shoker, L., Sanei, S., Latif, M.A.: Artifact removal from electroencephalograms using a hybrid BSS-SVM algorithm. IEEE Signal Process Lett. 12(10), 721–724 (2005)CrossRef

15.

Urretarazu, E., Iriarte, J., Alegre, M., Valencia, M., Vireri, C., Artieda, J.: Independent component analysis removing artifacts in ictal recordings. Epilepsia 45, 1071–1078 (2004)CrossRef

16.

Pizzagalli, D.A.: Electroencephalography and high-density electrophysiological source localization. In: Cacioppo, J.T., Tassinary, G.G. (eds.) Bernston, Handbook of Psychophysiology, 3rd edn, pp. 56–84. Cambridge University Press, Cambridge (2007)CrossRef

17.

Krishnaveni, V., Jayaraman, S., Anitha, L., Ramadoss, K.: Removal of ocular artifacts from EEG using adaptive thresholding of wavelet coefficients. J. Neural Eng. 3, 338–346 (2006)CrossRef

18.

Indiradevi, K.P., Elias, E., Sathidevi, P.S., Nayak, S.D., Radhakrishman, K.: A multilevel wavelet approach for automatic detection of epileptic spikes in the electroencephalogram. J. Comput. Biol. Med. 38(7), 805–816 (2008)CrossRef

19.

Zhou, W., Gotman, J.: Removal of EMG and ECG artifacts from EEG based on wavelet transform and ICA. In: Proceedings of 26th Annual International Conference IEEE Engineering in Medicine and Biology Society(EMBS04), San Francisco, CA, Sep. 15, pp. 392–395 (2004)

20.

Nguyen, H.-A.T., Musson, J., Li, J., McKenzie, F., Zhang, G., Zu, R., Richey, C., Schell, T.: EEG artifact removal using a wavelet neural network. In: Proceedings of Mod Sim World (2010)

21.

Jung, T.-P., Humphries, C., Lee, T.-W., Makeig, S., McKeown, M.J., Iragui, V., Sejnowski, T.J.: Extended ICA removes artifacts from electroencephalographic recordings. Adv. Neural Inf. Process. Syst. 10, 894–900 (1998)

22.

Vigario, R.N.: Extraction of ocular artifacts from EEG using independent component analysis. Electroencephal. Clin. Neurophysiol. 103, 395–404 (1997)CrossRef

23.

He, P., Wilson, G., Russell, C.: Removal of ocular artifacts from electro-encephalogram by adaptive filtering. Med. Biol. Eng. Comput. 42, 407–412 (2004)CrossRef

24.

Belouchrani, A., Abed-Meraim, K., Cardoso, J.F., Moulines, E.: A blind source separation technique using second-order statistics. IEEE Trans. Signal Proc. 45, 434–444 (1997)CrossRef

25.

Joyce, C.A., Gorodnitsky, I.F., Kutas, M.: Automatic removal of eye movement and blink artifacts from EEG data using blind component separation. Psychophysiology 41(2), 313–325 (2004)CrossRef

26.

Croft, R.J., Barry, R.J.: Removal of ocular artifact from the EEG: a review. Neurophysiol. Clin. 30, 5–19 (2000)CrossRef

27.

Rowland, V.: Cortical steady potential (direct current potential) in reinforcement and learning. Prog. Physiol. Psychol. 2, 1–77 (1968)

Titel: Evaluating algorithms of removing EOG artifacts with experimental data in brain computer interface
verfasst von: Wen Qi
Publikationsdatum: 30.12.2017
Verlag: Springer US
Erschienen in: Cluster Computing / Ausgabe Sonderheft 4/2019
Print ISSN: 1386-7857
Elektronische ISSN: 1573-7543
DOI: https://doi.org/10.1007/s10586-017-1555-3

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