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2018 | OriginalPaper | Chapter

Validation of EEG Pre-processing Pipeline by Test-Retest Reliability

Authors : Jazmín Ximena Suárez-Revelo, John Fredy Ochoa-Gómez, Carlos Andrés Tobón-Quintero

Published in: Applied Computer Sciences in Engineering

Publisher: Springer International Publishing

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Abstract

Artifact removal and validation of pre-processing approaches remain as an open problem in EEG analysis. Cleaning data is a critical step in EEG analysis, per-formed to increase the signal-to-noise ratio and to eliminate unwanted artifacts. Methodologies commonly used for EEG pre-processing are: filtering, interpolation of bad channels, epoch segmentation, re-referencing, and elimination of physiological artifacts such as eye blinking or muscular activity. It is important to consider that the order and application of these steps affect signal quality for further analysis. In order to validate a pre-processing pipeline that can be considered in a clinical follow-up, this paper evaluated test-retest reliability of EEG recordings. EEG signals were acquired during eyes-closed resting state condition in two groups of healthy subjects with a follow-up of one and six months respectively. Signals were pre-processed with five different methodologies commonly used in literature. Test-retest reliability by intraclass correlation coefficient was calculated for power spectrum measures in each pre-processing approach and group. The results showed how test-retest reliability was significantly affected by pre-processing pipeline in both follow-ups. The pre-processing pipeline that com-bines robust reference to average and wavelet ICA improves the test-retest reliability.

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MacDonald, D.B.: Electroencephalography: Basic Principles and Applications. Elsevier, New York City (2015)

Millett, D., Coutin-Churchman, P., Stern, J.M.: Basic principles of electroencephalography. In: Brain Mapping, pp. 75–80. Elsevier (2015)

Indic, P., Pratap, R., Nampoori, V.P., Pradhan, N.: Significance of time scales in nonlinear dynamical analysis of electroencephalogram signals. Int. J. Neurosci. 99, 181–194 (1999)CrossRef

Medeiros, A.P., Anghinah, R., Smidth, M.T., Silva, J.M.: The clinical use of quantitative EEG in cognitive disorders. Dement Neuropsychol. 3, 195–203 (2009)CrossRef

Sanei, S., Chambers, J.A.: EEG Signal Processing. Wiley, Hoboken (2007)CrossRef

Başar, E., Güntekin, B.: A short review of alpha activity in cognitive processes and in cognitive impairment. Int. J. Psychophysiol. 86, 25–38 (2012). https://doi.org/10.1016/j.ijpsycho.2012.07.001CrossRef

Babiloni, C., et al.: Sources of cortical rhythms in adults during physiological aging: a multicentric EEG study. Hum. Brain Mapp. 27, 162–172 (2006). https://doi.org/10.1002/hbm.20175CrossRef

Başar, E.: A review of alpha activity in integrative brain function: fundamental physiology, sensory coding, cognition and pathology. Int. J. Psychophysiol. 86, 1–24 (2012). https://doi.org/10.1016/j.ijpsycho.2012.07.002CrossRef

Lizio, R., Vecchio, F., Frisoni, G.B., Ferri, R., Rodriguez, G., Babiloni, C.: Electroencephalographic rhythms in alzheimer’s disease. Int. J. Alzheimers. Dis. 2011, 1–11 (2011). https://doi.org/10.4061/2011/927573CrossRef

10.

Widmann, A., Schröger, E.: Filter effects and filter artifacts in the analysis of electrophysiological data. Front Psychol. 3, 233 (2012)

11.

Winkler, I., Haufe, S., Tangermann, M.: Automatic classification of artifactual ICA-components for artifact removal in EEG signals. Behav. Brain Funct. 7, 30 (2011). https://doi.org/10.1186/1744-9081-7-30CrossRef

12.

Tavakoli, P., Campbell, K.: The recording and quantification of event-related potentials: II. Signal processing and analysis. Quant. Methods Psychol. 11, 98–112 (2015)CrossRef

13.

Gross, J., et al.: Good practice for conducting and reporting MEG research. Neuroimage 65, 349–363 (2013). https://doi.org/10.1016/j.neuroimage.2012.10.001CrossRef

14.

Tavakoli, P., Campbell, K.: The recording and quantification of event-related potentials: I. Stimulus presentation and data acquisition. Quant. Methods Psychol 11, 89–97 (2015)CrossRef

15.

Wallstrom, G.L., Kass, R.E., Miller, A., Cohn, J.F., Fox, N.A.: Automatic correction of ocular artifacts in the EEG: a comparison of regression-based and component-based methods. Int. J. Psychophysiol. 53, 105–119 (2004). https://doi.org/10.1016/j.ijpsycho.2004.03.007CrossRef

16.

Bigdely-Shamlo, N., Mullen, T., Kothe, C., Su, K.-M., Robbins, K.A.: The PREP pipeline: standardized preprocessing for large-scale EEG analysis. Front. Neuroinform. 9, 1–20 (2015). https://doi.org/10.3389/fninf.2015.00016CrossRef

17.

Castellanos, N.P., Makarov, V.A.: Recovering EEG brain signals: artifact suppression with wavelet enhanced independent component analysis. J. Neurosci. Methods. 158, 300–312 (2006). https://doi.org/10.1016/j.jneumeth.2006.05.033CrossRef

18.

Cannon, R.L., et al.: Reliability of quantitative EEG (qEEG) measures and LORETA current source density at 30 days. Neurosci. Lett. 518, 27–31 (2012). https://doi.org/10.1016/j.neulet.2012.04.035CrossRef

19.

Delorme, A., Makeig, S.: EEGLAB: an open source toolbox for analysis of single-trial EEG dynamics including independent component analysis. J. Neurosci. Methods 134, 9–21 (2004). https://doi.org/10.1016/j.jneumeth.2003.10.009CrossRef

20.

Delorme, A., Sejnowski, T.J., Makeig, S.: Enhanced detection of artifacts in EEG data using higher-order statistics and independent component analysis. Neuroimaging. 34, 1443–1449 (2007). https://doi.org/10.1016/j.neuroimage.2006.11.004CrossRef

21.

Suárez-Revelo, J., Ochoa-Gomez, J., Duque-Grajales, J., Montoya-Betancur, A., Sanchez-Lopez, S.: Test – retest reliability in electroencephalographic recordings. In: 2015 20th Symposium on Signal Processing, Images and Computer Vision (STSIVA), pp. 1–5 (2015). https://doi.org/10.1109/stsiva.2015.7330412

22.

Suarez-Revelo, J., Ochoa-Gomez, J., Duque-Grajales, J.: Improving test-retest reliability of quantitative electroencephalography using different preprocessing approaches. In: 2016 IEEE 38th Annual International Conference of the Engineering in Medicine and Biology Society (EMBC), pp. 961–964. IEEE (2016)

23.

Mitra, P., Bokil, H.: Observed Brain Dynamics. Oxford University Press, New York (2008)

24.

McGraw, K.O., Wong, S.P.: Forming inferences about some intraclass correlations coefficients: Correction. Psychol. Methods 1, 390 (1996). https://doi.org/10.1037/1082-989x.1.4.390CrossRef

25.

McEvoy, L., Smith, M., Gevins, A.: Test–retest reliability of cognitive EEG. Clin. Neurophysiol. 111, 457–463 (2000). https://doi.org/10.1016/S1388-2457(99)00258-8CrossRef

26.

Corsi-Cabrera, M., Galindo-Vilchis, L., del-Río-Portilla, Y., Arce, C., Ramos-Loyo, J.: Within-subject reliability and inter-session stability of EEG power and coherent activity in women evaluated monthly over nine months. Clin. Neurophysiol. 118, 9–21 (2007). https://doi.org/10.1016/j.clinph.2006.08.013CrossRef

27.

Hatz, F., Hardmeier, M., Bousleiman, H., Rüegg, S., Schindler, C., Fuhr, P.: Reliability of fully automated versus visually controlled pre- and post-processing of resting-state EEG. Clin. Neurophysiol. 126, 268–274 (2015). https://doi.org/10.1016/j.clinph.2014.05.014CrossRef

Title: Validation of EEG Pre-processing Pipeline by Test-Retest Reliability
Authors: Jazmín Ximena Suárez-Revelo
John Fredy Ochoa-Gómez
Carlos Andrés Tobón-Quintero
Publisher: Springer International Publishing
Book: Applied Computer Sciences in Engineering
Print ISBN: 978-3-030-00352-4

Electronic ISBN: 978-3-030-00353-1

Copyright Year: 2018
DOI: https://doi.org/10.1007/978-3-030-00353-1_26

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