Abstract
Line noise is artifact affecting a gamma band of the EEG. Conventional filters are used for removing the line noise artifact, but these filters, also remove physiological activity and could induce spurious oscillations. We tested two alternative methods for removing line noise artifact without removing a physiological activity, specifically Adaptive noise cancellation (ANC) method with Linear regression and modified Independent Component Analysis (ICA). We proposed a new protocol for statistical evaluation of line noise removal effectiveness. The line noise artifact was physically simulated and the protocol for statistical evaluation was verified. The ANC method shows good results according to statistical evaluation. The lowest residuum of simulated line noise artifact corresponds to 0.0005 \(\upmu \)V\(^2\)/Hz. Nevertheless, the ANC is very sensitive to the quality of the reference signal, which heavily depends on the reference electrode selection.
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Acknowledgements
This work was supported by the Grant Agency of the Czech Technical University in Prague, registr. numb. SGS19/136/OHK4/2T/17, by the Grant Agency of the Czech Technical University in Prague, registr. numb. SGS18/159/OHK4/2T/17, by the Grant Agency of Czech Republic, register number 17-20480S, by the Grant Agency of Czech Republic, register number 19-15728S and project LO1611 with a financial support from the MEYS under the NPU I program.
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The authors declare that there is no conflict of interest regarding the publication of this article. The study protocol and patient informed consent have been approved by National Institute of Mental Health ethical committee. The procedures followed were in compliance with the World Medical Association Declaration of Helsinki on Ethical Principles for Medical Research Involving Human Subjects.
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Strobl, J., Piorecky, M., Koudelka, V., Nagy, T., Krajca, V. (2020). Methods for Removing of Line Noise Artifact from EEG Records with Minimization of Neural Information Loss. In: Henriques, J., Neves, N., de Carvalho, P. (eds) XV Mediterranean Conference on Medical and Biological Engineering and Computing – MEDICON 2019. MEDICON 2019. IFMBE Proceedings, vol 76. Springer, Cham. https://doi.org/10.1007/978-3-030-31635-8_22
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