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Effective removal of eye-blink artifacts in EEG signals with semantic segmentation

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Abstract

Artifacts in electroencephalography (EEG) signals have negative effects on the analysis of the EEG signals. Although a number of techniques were developed to remove artifacts, new methods still need to be developed to remove artifacts with high accuracy and minimal data loss from EEG signals. In this context, there has been an increasing trend to combine machine learning and traditional approaches for effective automatic artifact removal. In this work, we have focused on eye-blink artifact removal, which is the most common and critical type of artifact. Unlike other studies, the artifact part of the EEG signal segment was detected by the semantic segmentation deep learning algorithm. Then, Butterworth filter was applied to only the artifact part of the EEG signal segment, and low-frequency noises in the region of interest containing only artifact were filtered. Since there is no filtering in the part of the signal without artifacts, the mean square error (MSE), normalized mean square error (NMSE) and structure similarity metric (SSIM) proved that there was no signal distortion in the non-artifact region of the metric. Thus, it became possible to examine only with the artifact signal segment.

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Acknowledgements

This study was approved by the university human research ethics committee with this number of 2015-KAEK-86/15. All procedures performed in studies involving human participants anonymously were in accordance with the ethical standards of the institutional research committee and with the Helsinki Declaration as revised in 2013.

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Authors and Affiliations

  1. Department of Electrical and Electronics Engineering, Simav Technology Faculty, Kutahya Dumlupinar University, Kutahya, 43500, Turkey

    Ömer Kasim & Mustafa Tosun

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  1. Ömer Kasim
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  2. Mustafa Tosun
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Correspondence to Ömer Kasim.

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Kasim, Ö., Tosun, M. Effective removal of eye-blink artifacts in EEG signals with semantic segmentation. SIViP 16, 1289–1295 (2022). https://doi.org/10.1007/s11760-021-02080-4

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  • DOI: https://doi.org/10.1007/s11760-021-02080-4

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