Comparison of lndependent aomponent analysis, linear regression and adaptive filtering for artifact removal in SSVEP registration

• Autorzy: Jurczak Marcin , Kołodziej Marcin , Majkowski Andrzej

Identyfikatory

DOI

10.15199/48.2024.05.54

Warianty tytułu

PL

Porównanie analizy składowych niezależnych, regresji liniowej i filtracji adaptacyjnej na użytek usuwania artefaktów w rejestracji SSVEP

• Języki publikacji: EN

Abstrakty

EN

Artifacts pose a significant challenge in the analysis of EEG signals. In this study, the authors investigated the impact of artifacts on the detection of steady-state visually evoked potentials (SSVEPs). The article explored various techniques for physiological artifact elimination, including linear regression, adaptive filters, and independent component analysis (ICA). The effectiveness of the algorithms was evaluated using classification accuracy as a metric. The results indicate that the most promising outcomes were achieved with independent component analysis.

PL

Artefakty odgrywają znaczącą rolę w analizie sygnałów EEG. Autorzy zbadali wpływ artefaktów na detekcję potencjałów wywołanych SSVEP. Artykuł przedstawia różne techniki eliminacji artefaktów fizjologicznych – regresję liniową, filtrację adaptacyjną oraz analizę składowych niezależnych (ICA). Efektywność algorytmów została oceniona z wykorzystaniem metryki jaką była skuteczność klasyfikacji. Wyniki wskazują, że najbardziej obiecujące wyniki osiągnięto dzięki analizie składowych niezależnych (ICA).

Słowa kluczowe

EN

artifact; ICA; linear regression; adaptive filtering

PL

artefakt; ICA; regresja liniowa; filtracja adaptacyjna

• Wydawca: Wydawnictwo SIGMA-NOT
• Czasopismo: Przegląd Elektrotechniczny
• Rocznik: 2024
• Tom: R. 100, nr 5
• Strony: 282--285
• Opis fizyczny: Bibliogr. 16 poz., rys.

Twórcy

autor

Jurczak Marcin

• Warsaw University of Technology, pl. Politechniki 1, 00-661 Warszawa

• https://orcid.org/0000-0002-0426-3639

autor

Kołodziej Marcin

• Warsaw University of Technology, pl. Politechniki 1, 00-661 Warszawa

• https://orcid.org/0000-0003-2856-7298

autor

Majkowski Andrzej

• Warsaw University of Technology, pl. Politechniki 1, 00-661 Warszawa

• https://orcid.org/0000-0002-6557-836X

Bibliografia

• [1] Chen, X., Xu, X., Liu, A., Lee, S., Chen, X., Zhang, X., et al. (2019). Removal of Muscle Artifacts From the EEG: A Review and Recommendations. IEEE Sensors Journal 19, 5353– 5368. doi: 10.1109/JSEN.2019.2906572.K. Elissa, "Title of paper if known," unpublished.
• [2] Cheng, J., Li, L., Li, C., Liu, Y., Liu, A., Qian, R., et al. (2019). Remove Diverse Artifacts Simultaneously From a Single-Channel EEG Based on SSA and ICA: A Semi-Simulated Study. IEEE Access 7, 2019. doi: 10.1109/ACCESS.2019.2915564.Y. Yorozu, M. Hirano, K. Oka, and Y. Tagawa, "Electron spectroscopy studies on magneto-optical media and plastic substrate interface," IEEE Transl. J. Magn. Japan, vol. 2, pp. 740–741, August 1987 [Digests 9th Annual Conf. Magnetics Japan, p. 301, 1982].
• [3] Jiang, X., Bian, G.-B., and Tian, Z. (2019). Removal of Artifacts from EEG Signals: A Review. Sensors 19, 987. doi: 10.3390/s19050987.
• [4] Lee, S. S., Lee, K., and Kang, G. (2020). EEG Artifact Removal by Bayesian Deep Learning amp; ICA. in 2020 42nd Annual International Conference of the IEEE Engineering in Medicine Biology Society (EMBC), 932–935. doi: 10.1109/EMBC44109.2020.9175785.
• [5] Mashhadi, N., Khuzani, A. Z., Heidari, M., and Khaledyan, D. (2020). Deep learning denoising for EOG artifacts removal from EEG signals. in 2020 IEEE Global Humanitarian Technology Conference (GHTC), 1–6. doi: 10.1109/GHTC46280.2020.9342884.
• [6] Moncada, F., González, V. M., Álvarez, V., García, B., and Villar, J. R. (2021). A Preliminary Study on Automatic Detection and Filtering of Artifacts from EEG Signals. in 2021 IEEE 34th International Symposium on Computer-Based Medical Systems (CBMS), 420–425. doi: 10.1109/CBMS52027.2021.00046.
• [7] Mumtaz, W., Rasheed, S., and Irfan, A. (2021). Review of challenges associated with the EEG artifact removal methods. Biomedical Signal Processing and Control 68, 102741. doi: 10.1016/j.bspc.2021.102741.
• [8] Li, P., Chen, Z., and Hu, Y. (2017). A method for automatic removal of EOG artifacts from EEG based on ICA-EMD. in 2017 Chinese Automation Congress (CAC), 1860–1863. doi: 10.1109/CAC.2017.8243071.
• [9] Mannan, M. M. N., Kamran, M. A., and Jeong, M. Y. (2018). Identification and Removal of Physiological Artifacts From Electroencephalogram Signals: A Review. IEEE Access 6, 30630–30652. doi: 10.1109/ACCESS.2018.2842082.
• [10] Urigüen, J., and Zapirain, B. (2015). EEG artifact removal – State-of-the-art and guidelines. Journal of neural engineering 12, 031001. doi: 10.1088/1741-2560/12/3/031001.
• [11] Rosanne, O., Albuquerque, I., Cassani, R., Gagnon, J.-F., Tremblay, S., and Falk, T. H. (2021). Adaptive Filtering for Improved EEG-Based Mental Workload Assessment of Ambulant Users. Frontiers in Neuroscience 15. Available at: https://www.frontiersin.org/articles/10.3389/fnins.2021.611962 [Accessed January 9, 2023].
• [12] A Turnip, A., Setiawan, I., Junaidi, E., and Nguyen, L. (2014). An Experiment of Ocular Artifacts Elimination from EEG Signals using ICA and PCA Methods. Mechatronics, Electrical Power, and Vehicular Technology 5. doi: 10.14203/j.mev.2014.v5.129-138.
• [13] Zheng, Y., and Hu, X. (2019). Interference Removal From Electromyography Based on Independent Component Analysis. IEEE Transactions on Neural Systems and Rehabilitation Engineering 27, 887–894. doi: 10.1109/TNSRE.2019.2910387
• [14] Jurczak, M., Kołodziej, M., and Majkowski, A. (2022). Implementation of a Convolutional Neural Network for Eye Blink Artifacts Removal From the Electroencephalography Signal. Frontiers in Neuroscience 16. Available at: https://www.frontiersin.org/articles/10.3389/fnins.2022.782367 [Accessed July 11, 2023].
• [15] Liu, Q., Liu, A., Zhang, X., Chen, X., Qian, R., and Chen, X. (2019). Removal of EMG Artifacts from Multichannel EEG Signals Using Combined Singular Spectrum Analysis and Canonical Correlation Analysis. Journal of Healthcare Engineering 2019, e4159676. doi: 10.1155/2019/4159676.
• [16] Single channel approach for filtering electroencephalographic signals strongly contaminated with facial electromyography. Frontiers in Computational Neuroscience 16. Available at: https://www.frontiersin.org/articles/10.3389/fncom.2022.82298 7 [Accessed July 12, 2023].

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki i promocja sportu (2025).