International Research Journal of Innovations in Engineering and Technology - IRJIET International Open Access, Monthly, Peer Reviewed, Reputed Journal ISSN (online): 2581-3048

Impact Factor (2025): 6.9

DOI Prefix: 10.47001/IRJIET

Review on Automatic Ocular Artifacts Removal in EEG Using Deep Learning (Combinations of CNNs and Long Short-Term Memory (LSTM)

Ms. Swati M. ShelarShivnagar Vidya Prasarak Mandal’s College of Engineering, Malegaon (Bk), Baramati, IndiaDr. Yuvraj ParkaleShivnagar Vidya Prasarak Mandal’s College of Engineering, Malegaon (Bk), Baramati, India

Vol 7 No (2023): Volume 7, Special Issue of ICRTET- 2023 | Pages: 51-56

International Research Journal of Innovations in Engineering and Technology

OPEN ACCESS | Research Article | Published Date: 16-07-2023

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Abstract

Electroencephalography (EEG) is very tedious for analysis of the dynamic behaviour of human brain. Practically, the analysis of the biomedical signal is no simple as those signals are very dynamic with respect to time, researcher have to make many computations on the different static and dynamic parameters which consumes much time. Accuracy of the signal from object depends on the experimental environment setup and environmental conditions. Enhanced research is conducted on automated EEG signal analysis using artificial intelligence and computer-aided technologies. This would make fast and accurate results. The main objective of this research is to remove unwanted and noisy signals which are mixed in the original signal generated by human brain using deep learning (DL) architectures. We can use the databases available on Kaggle, Web of science which are made free for testing purpose. All datasets and samples will be collected, then analysed and will be processed with different neural architectures and compared. DL in biomedical signal processing is efficient in various research applications. It is very helpful diagnosing the common neurological disorders diagnosis.

Keywords

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Citation of this Article

Ms. Swati M. Shelar, Dr. Yuvraj Parkale, “Review on Automatic Ocular Artifacts Removal in EEG Using Deep Learning (Combinations of CNNs and Long Short-Term Memory (LSTM)” in proceeding of International Conference of Recent Trends in Engineering & Technology ICRTET - 2023, Organized by SCOE, Sudumbare, Pune, India, Published in IRJIET, Volume 7, Special issue of ICRTET-2023, pp 51-56, June 2023.

Licence Copyright (c) 2026 International Research Journal of Innovations in Engineering and Technology creative common licence This work is licensed under Creative common Attribution Non Commercial 4.0 Internation Licence
References
  1. P. L. Nunez and R. Srinivasan, "The physics—EEG interface" in Electric Fields of the Brain: The Neurophysics of EEG, New York, NY, USA:Oxford Univ. Press, pp. 3-55, 2006.
  2. S. Sanei and J. A. Chambers, "Introduction to EEG" in EEG Signal Processing, Hoboken, NJ, USA:Wiley, pp. 1-34, 2007.
  3. V. Jurcak, D. Tsuzuki and I. Dan, "10/20 10/10 and 10/5 systems revisited: Their validity as relative head-surface-based positioning systems", Neuroimage, vol. 34, no. 4, pp. 1600-1611, 2007.
  4. K. T. Sweeney, T. E. Ward and S. F. McLoone, "Artifact removal in physiological signals— Practices and possibilities", IEEE Trans. Inf. Technol. Biomed., vol. 16, no. 3, pp. 488-500, May 2012.
  5. M. K. Islam, A. Rastegarnia and Z. Yang, "Methods for artifact detection and removal from scalp EEG: A review", Clin. Neurophysiol., vol. 46, no. 4, pp. 287-305, 2016.
  6. J. A. Urigüen and B. Garcia-Zapirain, "EEG artifact removal—State-of-the-art and guidelines", J. Neural Eng., vol. 12, no. 3, pp. 1-23, 2015.
  7. I.I. Goncharova, D. J. McFarland, T. M. Vaughan and J. R. Wolpaw, "EMG contamination of EEG: Spectral and topographical characteristics", Clin. Neurophysiol., vol. 114, no. 9, pp. 1580-1593, 2003.
  8. S. Boudet, L. Peyrodie, P. Gallois and C. Vasseur, "A global approach for automatic artifact removal for standard EEG record", Proc. Int. Conf. IEEE Eng. Med. Biol. Soc., pp. 5719-5722, Aug. 2006.
  9. R. N. Roy, S. Charbonnier and S. Bonnet, "Eye blink characterization from frontal EEG electrodes using source separation and pattern recognition algorithms", Biomed. Signal Process. Control, vol. 14, pp. 226-264, Nov. 2014.
  10. X. Jiang, G.-B. Bian and Z. Tian, "Removal of artifacts from EEG signals: A review", Sensors, vol. 19, no. 5, pp. 1-18, 2019.
  11. P. J. Karoly, H. Ung, D. B. Grayden, L. Kuhlmann, K. Leyde, M. J. Cook, et al., "The circadian profile of epilepsy improves seizure forecasting", Brain, vol. 140, no. 8, pp. 2169- 2182, Aug. 2017.
  12. M. O. Baud, J. K. Kleen, E. A. Mirro, J. C. Andrechak, D. King-Stephens, E. F. Chang, et al., "Multi-day rhythms modulate seizure risk in epilepsy", Nature Commun., vol. 9, no. 1, pp. 88, Dec. 2018.
  13. A.Delorme, J. Palmer, J. Onton, R. Oostenveld and S. Makeig, "Independent EEG sources are dipolar", PLoS ONE, vol. 7, no. 2, pp. 1-14, 2012.
  14. S. Choi, A. Cichocki, H. M. Park and S.-Y. Lee, "Blind source separation and independent component analysis: A review", Neural Inf. Process. Lett. Rev., vol. 6, no. 1, pp. 1-57, 2005.
  15. H. Nolan, R. Whelan and R. B. Reilly, "FASTER: Fully automated statistical thresholding for EEG artifact rejection", J. Neurosci. Methods, vol. 192, no. 1, pp. 152-162, 2010.
  16. I.Winkler, S. Haufe and M. Tangermann, "Automatic classification of artifactual ICA- components for artifact removal in EEG signals", Behav. Brain Functions, vol. 7, Aug. 2011.
  17. A.Mognon, J. Jovicich, L. Bruzzone and M. Buiatti, "ADJUST: An automatic EEG artifact detector based on the joint use of spatial and temporal features", Psychophysiology, vol. 48, no. 2, pp. 229-240, 2011.
  18. T. Radüntz, J. Scouten, O. Hochmuth and B. Meffert, "EEG artifact elimination by extraction of ICA-component features using image processing algorithms", J. Neurosci. Methods, vol. 243, pp. 84-93, Mar. 2015.
  19. T. Radüntz, J. Scouten, O. Hochmuth and B. Meffert, "Automated EEG artifact elimination by applying machine learning algorithms to ICA-based features", J. Neural Eng., vol. 14, no. 4, 2017.
  20. P.Croce, F. Zappasodi, L. Marzetti, A. Merla, V. Pizzella and A. M. Chiarelli, "Deep convolutional neural networks for feature-less automatic classification of independent components in multi-channel electrophysiological brain recordings", IEEE Trans. Biomed. Eng., vol. 66, no. 8, pp. 2282-2290, Aug. 2019.
  21. L. Pion-Tonachini, K. Kreutz-Delgado and S. Makeig, "ICLabel: An automated electroencephalographic independent component classifier dataset and website", NeuroImage, vol. 198, pp. 181-197, Sep. 2019.
  22. H. Zhang, C. Wei, M. Zhao, Q. Liu and H. Wu, "A novel convolutional neural network model to remove muscle artifacts from EEG", Proc. IEEE Int. Conf. Acoust. Speech Signal Process. (ICASSP), pp. 1265-1269, Jun. 2021.
  23. M. F. Pinto, A. Leal, F. Lopes, A. Dourado, P. Martins and C. A. Teixeira, "A personalized and evolutionary algorithm for interpretable EEG epilepsy seizure prediction", Sci. Rep., vol. 11, no. 1, pp. 3415, Dec. 2021.
  24. A.Delorme and S. Makeig, "EEGLAB: An open source toolbox for analysis of single-trial EEG dynamics including independent component analysis", J. Neurosci. Methods, vol. 134, no. 1, pp. 9-21, 2004.
  25. B. Xu, N. Wang, T. Chen and M. Li, "Empirical evaluation of rectified activations in convolutional network", arXiv:1505.00853, 2015.
  26. M. M. N. Mannan, M. A. Kamran and M. Y. Jeong, "Identification and removal of physiological artifacts from electroencephalogram signals: A review", IEEE Access, vol. 6, pp. 30630-30652, 2018.
  27. H.-T. Chiang, Y.-Y. Hsieh, S.-W. Fu, K.-H. Hung, Y. Tsao and S.-Y. Chien, "Noise reduction in ECG signals using fully convolutional denoising auto encoders", IEEE Access, vol. 7, pp. 60806-60813, 2019.
  28. Y. LeCun, Y. Bengio and G. Hinton, "Deep learning", Nature, vol. 521, pp. 436-444, May 2015.
  29. A.L. Maas, A. Y. Hannun and A. Y. Ng, "Rectifier nonlinearities improve neural network acoustic models", Proc. 30th Int. Conf. Mach. Learn., vol. 28, pp. 1-6, 2013. 

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