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Link to original content: https://doi.org/10.1007/s11517-022-02692-z
SNOAR: a new regression approach for the removal of ocular artifact from multi-channel electroencephalogram signals | Medical & Biological Engineering & Computing Skip to main content
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SNOAR: a new regression approach for the removal of ocular artifact from multi-channel electroencephalogram signals

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Abstract

Electroencephalogram (EEG) signals are often corrupted by undesirable sources like electrooculogram (EOG) artifacts, which have a substantial impact on the performance of EEG-based systems. This study proposes a new singular spectrum analysis (SSA)–non-negative matrix factorization (NMF)-based ocular artifact removal (SNOAR) method to suppress ocular artifacts from multi-channel EEG signals. First, SSA was used to estimate EOG artifacts using a small subset of frontal electrodes. Then, NMF was applied to decompose the estimated EOG artifacts into vertical EOG (VEOG) and horizontal EOG (HEOG) signals. Finally, a simple linear regression with estimated VEOG and HEOG signals was used to remove artifacts from multi-channel EEG signals. EEG recordings from two EEG datasets (Klados dataset and KARA ONE) were used to evaluate the efficiency of the proposed method. From the simulation results, it was observed that the proposed method achieved betters results in terms of low root-mean-square error (RMSE), low delta band energy ratio, and less power spectral density (PSD) difference between the original clean EEG signal and its filtered version of contaminated EEG signal compared to selected EOG artifact removal methods (independent component analysis (ICA), wavelet-enhanced ICA (wICA), improved wICA, and multivariate empirical mode decomposition (MEMD)).

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References

  1. Jiang X, Bian G, Tian Z (2019) Removal of artifacts from EEG signals: a review. In Sensors 19(5):987. https://doi.org/10.3390/s19050987

    Article  Google Scholar 

  2. Croft RJ, Barry RJ (2000) Removal of ocular artifact from the EEG: a review. In Neurophysiologie Clinique 30(1):5–19. https://doi.org/10.1016/S0987-7053(00)00055-1

    Article  CAS  Google Scholar 

  3. Wallstrom GL, Kass RE, Miller A, Cohn JF, Fox NA (2004) Automatic correction of ocular artifacts in the EEG: a comparison of regression-based and component-based methods. International Journal of Psychophysiology 53(2):105–119. https://doi.org/10.1016/j.ijpsycho.2004.03.007

    Article  PubMed  Google Scholar 

  4. Mumtaz W, Rasheed S, Irfan A (2021) Review of challenges associated with the EEG artifact removal methods. Biomed Signal Process Control 68:102741. https://doi.org/10.1016/j.bspc.2021.102741

    Article  Google Scholar 

  5. Klados MA, Bamidis PD (2016) A semi-simulated EEG/EOG dataset for the comparison of EOG artifact rejection techniques. Data Brief 8:1004–1006. https://doi.org/10.1016/j.dib.2016.06.032

    Article  PubMed  PubMed Central  Google Scholar 

  6. Zhao S, Rudzicz F (2015) Classifying phonological categories in imagined and articulated speech. In: IEEE International Conference on Acoustics, Speech and Signal Processing, 922–996. https://doi.org/10.1109/ICASSP.2015.7178118

  7. Ranjan R, Sahana BC, Bhandari AK (2021) Ocular artifact elimination from electroencephalography signals: a systematic review. Biocybernetics and Biomedical Engineering 41(3):960–996

    Article  Google Scholar 

  8. He P, Wilson G, Russell C (2004) Removal of ocular artifacts from electro-encephalogram by adaptive filtering. Med Biol Eng Compu 42(3):407–412. https://doi.org/10.1007/BF02344717

    Article  CAS  Google Scholar 

  9. Somers B, Francart T, Bertrand A (2018) A generic EEG artifact removal algorithm based on the multi-channel Wiener filter. J Neural Eng 15(3):036007. https://doi.org/10.1088/1741-2552/aaac92

    Article  PubMed  Google Scholar 

  10. Castellanos NP, Makarov VA (2006) Recovering EEG brain signals: artifact suppression with wavelet enhanced independent component analysis. J Neurosci Methods 158(2):300–312. https://doi.org/10.1016/j.jneumeth.2006.05.033

    Article  PubMed  Google Scholar 

  11. Inuso G, La Foresta F, Mammone N, Morabito F C(2007) Wavelet-ICA methodology for efficient artifact removal from Electroencephalographic recordings. In: International Conference on Neural Networks - Conference Proceedings. IEEE, pp 1524–1529. https://doi.org/10.1109/IJCNN.2007.4371184

  12. Molla MKI, Tanaka T, Rutkowski TM (2012) Multivariate EMD based approach to EOG artifacts separation from EEG. In: International Conference on Acoustics, Speech and Signal Processing. IEEE, pp 653–656. https://doi.org/10.1109/ICASSP.2012.6287968

  13. Molla Md KI, Tanaka T, Rutkowski TM, Cichocki A (2010) Separation of EOG artifacts from EEG signals using Bivariate EMD. In: International Conference on Acoustics, Speech and Signal Processing. IEEE, pp 562–565. https://doi.org/10.1109/ICASS P.2010.5495594

  14. Damon C, Liutkus A, Gramfort A, Essid S (2013) Non-negative matrix factorization for single-channel EEG artifact rejection. In: International Conference on Acoustics Speech and Signal Processing. IEEE, pp 1177–1181. https://doi.org/10.1109/ICASSP. 2013. 6637836

  15. Gu Y, Li X, Chen S, Li X (2021) AOAR: an automatic ocular artifact removal approach for multi-channel electroencephalogram data based on non-negative matrix factorization and empirical mode decomposition. J Neural Eng 18(5):056012. https://doi.org/10.1088/1741-2552/abede0

    Article  PubMed  Google Scholar 

  16. Maddirala AK, Shaik RA (2016) Removal of EOG artifacts from single channel EEG signals using combined singular spectrum analysis and adaptive noise canceler. IEEE Sens J 16(23):8279–8287. https://doi.org/10.1109/JSEN.2016.2560219

    Article  Google Scholar 

  17. Noorbasha SK, Sudha GF (2020) Removal of EOG artifacts from single channel EEG – an efficient model combining overlap segmented ASSA and ANC. Biomed Signal Process Control 60:101987. https://doi.org/10.1016/j.bspc.2020.101987

    Article  Google Scholar 

  18. Noorbasha SK, Sudha GF (2021) Removal of EOG artifacts and separation of different cerebral activity components from single channel EEG—an efficient approach combining SSA–ICA with wavelet thresholding for BCI applications. Biomed Signal Process Control 63:102168. https://doi.org/10.1016/j.bspc.2020.102168

    Article  Google Scholar 

  19. Maddirala AK, Veluvolu KC (2021) Eye-blink artifact removal from single channel EEG with k-means and SSA. Sci Rep 11(1):1–4

    Article  Google Scholar 

  20. Patel R, Sengottuvel S, Janawadkar MP, Gireesan K, Radhakrishnan TS, Mariyappa N (2016) Ocular artifact suppression from EEG using ensemble empirical mode decomposition with principal component analysis. Comput Electr Eng 60:101987. https://doi.org/10.1016/j.compeleceng.2015.08.019

    Article  Google Scholar 

  21. Kanoga S, Mitsukura Y (2015) Eye blink artifact rejection in single-channel electroence-phalographic signals by complete ensemble empirical mode decomposition and independent component analysis. In: Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE, pp 121–124. https://doi.org/10.1109/EMBC.2015.7318315

  22. Sweeney KT, McLoone SF, Ward TE (2013) 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. https://doi.org/10.1109/TBME.2012.2225427

    Article  PubMed  Google Scholar 

  23. Saini M, Payal SU (2020) An effective and robust framework for ocular artifact removal from single-channel EEG Signal based on variational mode decomposition. IEEE Sens J 20(1):369–376

    Article  Google Scholar 

  24. Gajbhiye P, Tripathy RK, Pachori RB (2020) Elimination of ocular artifacts from single channel EEG signals using FBSE-EWT based rhythms. IEEE Sens J 20(7):3687–3696. https://doi.org/10.1109/JSEN.2019.2959697

    Article  Google Scholar 

  25. Hassani H, Zhigljavsky A (2009) Singular spectrum analysis: methodology and application to economics data. J Syst Sci Complexity 22(3):372–394. https://doi.org/10.1007/s11424-009-9171-9

    Article  Google Scholar 

  26. Naik GR (2015) Non-negative matrix factorization techniques: advances in theory and applications. In Non-negative matrix factorization techniques: advances in theory and applications. https://doi.org/10.1007/978-3-662-48331-2

    Article  Google Scholar 

  27. Issa MF, Juhasz Z (2019) Improved EOG artifact removal using wavelet enhanced independent component analysis. Brain Sci 9(12):355. https://doi.org/10.3390/brainsci9120355

    Article  PubMed Central  Google Scholar 

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Acknowledgements

The first author acknowledges the support from the Ministry of Education, Government of India. The second and third authors acknowledge the financial grant received from Core Research Grant of the Science and Engineering Research Board (SERB), Department of Science and Technology (DST), Government of India (CRG/2021/007147).

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Authors and Affiliations

  1. Department of Electronics Engineering, National Institute of Technology Uttarakhand, Srinagar Garhwal, 246174, Uttarakhand, India

    Ruchi Juyal & Hariharan Muthusamy

  2. Department of Neurology, All India Institute of Medical Science Rishikesh, Rishikesh, 249203, Uttarakhand, India

    Niraj Kumar

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  1. Ruchi Juyal
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  2. Hariharan Muthusamy
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  3. Niraj Kumar
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Contributions

Ruchi Juyal, methodology, software, investigation, and writing — original draft. Hariharan Muthusamy, conceptualization, investigation, writing — review and critical revision of the manuscript, validation, and supervision. Niraj Kumar, conceptualization, writing — review and critical revision of the manuscript, and supervision. All authors approved the final version of the manuscript.

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Correspondence to Hariharan Muthusamy.

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Juyal, R., Muthusamy, H. & Kumar, N. SNOAR: a new regression approach for the removal of ocular artifact from multi-channel electroencephalogram signals. Med Biol Eng Comput 60, 3567–3583 (2022). https://doi.org/10.1007/s11517-022-02692-z

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