Abstract
EEG recordings are frequently contaminated with unavoidable artifacts. Preprocessing in EEG has been a dynamic field of investigation as none of the reported methods can be reviewed as a standard approach for effective artifact removal. This paper presents a broad survey of the existing artifact removal methods. This review is expected to help researchers in improving the existing artifact handling techniques so as to relieve the expert’s burden by ensuring efficient analysis.
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References
Jung, C.Y., Saikiran, S.S.: A review on EEG artifacts and its different removal technique. Asia-Pac. J. Converg. Res. Interchang. 2(4), 43–60 (2016)
Klados, M. A., Papadelis, C., Braun, C., Bamidis, P.D.: REG-ICA: a hybrid methodology combining blind source separation and regression techniques for the rejection of ocular artifacts. Biomed. Signal Process. Control. 6, 291–300 (2011). Elsevier
Zachariah, A., Jai, J., Titus, G.: Automatic EEG artifact removal by independent component analysis using critical EEG rhythms. In: International Conference on Control Communication and Computing (2013). https://doi.org/10.1109/iccc.2013.6731680
Clercq, W.D., Vergult, A., Vanrumste, B., Paesschen, W.V., Huffel, S.V.: Canonical correlation analysis applied to remove muscle artifacts from the electroencephalogram. IEEE Trans. Biomed. Eng. 53(12), 2583–2587 (2006)
Yong, X., Ward, R.K., Birch, G.E.: Artifact removal in EEG using morphological component analysis. In: IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 345–348 (2009)
Khatun, S., Mahajan, R., Morshed, B.I.: Comparative analysis of wavelet based approaches for reliable removal of ocular artifacts from single channel EEG. In: IEEE International Conference on Electro/Information Technology (2015). https://doi.org/10.1109/eit.2015.7293364
Pachori, R.B.: Discrimination between ictal and seizure-free EEG signals using empirical mode decomposition. Res. Lett. Signal Process. 2008(14) (2008). Hindawi
Stearns, S. D.: Adaptive Signal Processing. Prentice Hall, Englewood Cliffs (1985)
Jung, T., Makeig, S., Humphries, C., Lee, T., Mckeown, M.J., Iragui, V., Sejnowski, T.J.: Removing electroencephalographic artifacts by blind source separation. Psychophysiology 3, 163–178 (2000)
Zou, Y., Nathan, V., Jafari, R.: Automatic identification of artifact-related independent components for artifact removal in EEG recordings. IEEE J. Biomed. Health Inform. 20(1), 73–81 (2016)
Safieddine, D., Kachenoura, A., Albera, L., Birot, G., Karfoul, A., Pasnicu, A., Biraben, A., Wendling, F., Senhadji, L., Merlet, I.: Removal of muscle artifact from EEG data: comparison between stochastic (ICA and CCA) and deterministic (EMD and wavelet-based) approaches. Eurasip J. Adv. Signal Process. 2012(1) (2012)
Sweeney, K.T., McLoone, S.F., Ward, T.E.: The use of ensemble empirical mode decomposition with canonical correlation analysis as a novel artifact removal technique. IEEE Trans. Biomed. Eng. 60(1), 97–105 (2013)
Chen, X., Xu, T.E., Liu, A., McKeown, M.J., Wang, Z.J.: The use of multivariate EMD and CCA for denoising muscle artifacts from few-channel EEG recordings. IEEE Trans. Instrum. Meas. 67(2), 359–370 (2018)
Hamaneh, M.B., Chitravas, N., Kaiboriboon, K., Lhatoo, S.D., Loparo, K.A.: Automated removal of EKG artifact from EEG data using independent component analysis and continuous wavelet transformation. IEEE Trans. Biomed. Eng. 61(6), 1634–1641 (2014)
Zhao, C., Qui, T.: An automatic ocular artifacts removal method based on wavelet-enhanced canonical correlation analysis. IEEE 33rd Annual International Conference of the Engineering in Medicine and Biology Society (2011). https://doi.org/10.1109/iembs.2011.6091040
Lindsen, J.P., Bhattacharya, J.: Correction of blink artifacts using independent component analysis and empirical mode decomposition. Psychophysiology 47, 955–960 (2010)
Chen, X., He, C., Peng, H.: Removal of muscle artifacts from single-channel EEG based on ensemble empirical mode decomposition and multiset canonical correlation analysis. J. Appl. Math. 2014 (2014). Hindawi
Shoker, L., Sanei, S., Chambers, J.A.: Artifact removal from electroencephalograms using a hybrid BSS-SVM algorithm. IEEE Signal Process. Lett. 12, 721–724 (2005)
Navarro, X., Poree, F., Carrault, G.: ECG removal in preterm EEG combining empirical mode decomposition and adaptive filtering. In: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), vol. 2012 pp. 661–664 (2012)
Chen, Y., Zhao, Q., Hu, B., Li, J., Jiang, H., Lin, W., Li, Y., Zhou, S., Peng, H.: Method of removing ocular artifacts from eeg using discrete wavelet transform and kalman filtering. In: IEEE International Conference on Bioinformatics and Biomedicine, pp. 1485–1492 (2016).
Chavez, M., Grosselin, F., Bussalb, A., Fallani, F.D.V., Navarro-Sune, X.: Surrogate-based artifact removal from single-channel EEG. IEEE Trans. Neural Syst. Rehabil. Eng. 26(3), 540–555 (2018)
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Bisht, A., Kaur, C., Singh, P. (2020). Recent Advances in Artifact Removal Techniques for EEG Signal Processing. In: Choudhury, S., Mishra, R., Mishra, R., Kumar, A. (eds) Intelligent Communication, Control and Devices. Advances in Intelligent Systems and Computing, vol 989. Springer, Singapore. https://doi.org/10.1007/978-981-13-8618-3_41
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DOI: https://doi.org/10.1007/978-981-13-8618-3_41
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