Abstract
EOG is one of the major artifacts in EEG signal processing. There are varieties of methods have been proposed that aim to eliminate the influence of Occular artifacts on the EEG signals. However, the problem is the trade-off between their performance of removing EOG artifact and their simplicity. In this study, we propose a simple and reliable method but giving a good performance. The idea of this method is to use a multi-threshold technique to target EOG contaminated parts in the signal then selectively subtract it out in order to get a corrected signal with a minimum alteration on the uncontaminated parts. In this study, we used triple-threshold, both in time and frequency domain, to target the contaminated parts (or EOG artifact component). The result shows that besides its simplicity, this method also reliable and effective when selectively removed some typical EOG artifacts like blinks or eye movements without altering other clean parts in the EEG signals. More than that, our method is also able to extract the estimated EOG artifact component from the EEG signal. The need for this method is only one single prefrontal EEG channel, no need for an EOG reference channel for the input. The source code of this method is freely available to download in the form of a MATLAB function by request. We encourage the researchers to give it a try.
Quoc Tuong Minh and Sieu Le Thi Be: These authors contributed equally.
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References
Fatourechi M, Bashashati A, Ward RK, Birch GE (2007) EMG and EOG artifacts in brain computer interface systems: a survey. Clin Neurophysiol 118:480–494
Chang W-D, Cha H-S, Kim K, Im C-H (2016) Detection of eye blink artifacts from single prefrontal channel electroencephalogram. Comput Methods Programs Biomed 124:19–30
Issa MF, Juhasz Z (2019) Improved EOG artifact removal using wavelet enhanced independent component analysis. Brain Sci 9:355
Joyce CA, Gorodnitsky IF, Kutas M (2004) Automatic removal of eye movement and blink artifacts from EEG data using blind component separation. Psychophysiology 41:313–325
Klados MA, Papadelis C, Braun C, Bamidis PD (2011) 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
Bizopoulos PA, Al-Ani T, Tsalikakis DG, Tzallas AT, Koutsouris DD, Fotiadis DI (2013) An automatic electroencephalography blinking artefact detection and removal method based on template matching and ensemble empirical mode decomposition. In: 2013 35th annual international conference of the IEEE engineering in medicine and biology society (EMBC)
Yong X, Fatourechi M, Ward RK, Birch GE (2012) Automatic artefact removal in a self-paced hybrid brain-computer interface system. J Neuroeng Rehabil 9:50
Krishnaveni V, Jayaraman S, Malmurugan N, Kandaswamy A, Ramadoss K (2004) Non adaptive thresholding methods for correcting ocular artifacts in EEG. Acad Open Int J 13
Mannan MMN, Kamran MA, Kang S, Jeong MY (2018) Effect of EOG signal filtering on the removal of ocular artifacts and EEG-based brain-computer interface: a comprehensive study. Complexity 2018
Klados MA, Bamidis PD (2016) A semi-simulated EEG/EOG dataset for the comparison of EOG artifact rejection techniques. Data Brief 8:1004–1006
Lins OG, Picton TW, Berg P, Scherg M (1993) Ocular artifacts in EEG and event-related potentials I: Scalp topography. Brain Topogr 6:51–63
Libenson MH (2012) Practical approach to electroencephalography E-Book. Elsevier Health Sciences
Acknowledgements
We acknowledge the support of time and facilities from Ho Chi Minh City University of Technology (HCMUT), VNU-HCM for this study.
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The authors have no conflict of interest to declare.
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Tuong Minh, Q., Le Thi Be, S., Le Quoc, K., Huynh Quang, L. (2022). A Selective EOG Removal Method for EEG Signals: The Multi-thresholding Technique. In: Van Toi, V., Nguyen, TH., Long, V.B., Huong, H.T.T. (eds) 8th International Conference on the Development of Biomedical Engineering in Vietnam. BME 2020. IFMBE Proceedings, vol 85. Springer, Cham. https://doi.org/10.1007/978-3-030-75506-5_78
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