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EEG Artifact Removal Strategies for BCI Applications: A Survey

Document Type : Review Article

Authors

1 Department of Electronics & Communication Engg BVRIT HYDERABAD College of Engineering For Women Nizampet

2 School of Electronics Engineering (SENSE), Vellore Institute of Technology, Vellore, India.

3 Department of Electronics and Communications Engineering, BVRIT HYDERABAD College of Engineering for Women, Hyderabad, India.

Abstract

This paper aims to provide a comprehensive examination of the Brain-Computer Interface and the more scientific discoveries that have resulted from it. The ultimate goal of this review is to provide extensive research in BCI systems while also focusing on artifact removal techniques or methods that have recently been used in BCI and important aspects of BCIs. In its pre-processing, artifact removal methodologies were critical. Furthermore, the review emphasizes the applicability, practical challenges, and outcomes associated with BCI advancements. This has the potential to accelerate future progress in this field. This critical evaluation examines the current state of BCI technology as well as recent advancements. It also identifies various BCI technology application areas. This detailed study shows that, while progress is being made, significant challenges remain for user advancement A comparison of EEG artifact removal methods in BCI was done, and their usefulness in real-world EEG-BCI applications was talked about. Some directions and suggestions for future research in this area were also made based on the results of the review and the existing artifact removal methods.

Keywords

References
  • [1] Kübler, A. (2020). The history of BCI: From a vision for the future to real support for personhood in people with locked-in syndrome. Neuroethics, 13(2), 163-180.
  • [2] Kawala-Janik, A. Efficiency Evaluation of External Environments Control Using Bio-Signals. Ph.D. Thesis, University of Greenwich, London, UK, 2013.
  • [3] Ebersole, J.S.; Pedley, T.A. Current Practice of Clinical Electroencephalography; Lippincott Williams & Wilkins: Philadelphia, PA, USA, 2003
  • [4] Millett, D. Hans Berger: From psychic energy to the EEG. Perspect. Biol. Med. 2001, 44, 522–542. [CrossRef] Priyanka A. Abhang, Bharti W. Gawali, Suresh C. Mehrotra,
  • [5] Chapter 2 - Technological Basics of EEG Recording and Operation of Apparatus,Editor(s): Priyanka A. Abhang, Bharti W. Gawali, Suresh C. Mehrotra,Introduction to EEG- and Speech-Based Emotion Recognition,Academic Press,2016
  • [6] Aggarwal, Swati, and Nupur Chugh. "Signal processing techniques for motor imagery brain computer interface: A review."Array 1 (2019): 100003.
  • [7] Donoghue JP. Connecting cortex to machines: recent advances in brain interfaces. Nat Neurosci 2002;5:1085.
  • [8] Serruya Mijail D, et al. Brain-machine interface: instant neural control of a movement signal. Nature 2002;416:141.
  • [9] Cichocki, Andrzej, et al. "EEG filtering based on blind source separation (BSS) for early detection of Alzheimer's disease."Clinical Neurophysiology3 (2005): 729-737.
  • Al-Fahoum, Amjed S., and Ausilah A. Al-Fraihat. "Methods of EEG signal features extraction using linear analysis in frequency and time-frequency domains."International Scholarly Research Notices 2014 (2014).
  • Osalusi, Bamidele, Amole Abraham, and David Aborisade. "EEG Classification in Brain Computer Interface (BCI): A Pragmatic Appraisal."American Journal of Biomedical Engineering1 (2018): 1-11.
  • Mridha, M. F., et al. "Brain-computer interface: Advancement and challenges."Sensors17 (2021): 5746.
  • ] Phan A H and Cichocki A 2010 Tensor decompositions for feature extraction and classification of high dimensional datasets Nonlinear Theory Appl. 1 37–68
  • Washizawa Y, Higashi H, Rutkowski T, Tanaka T and Cichocki A 2010 Tensor based simultaneous feature extraction and sample weighting for EEG classification Int. Conf. on Neural Information Processing, ICONIP 2010: Neural Information Processing. Models and Applications (Berlin: Springer) pp 26–33
  • Onishi A, Phan A, Matsuoka K and Cichocki A 2012 Tensor classification for P300-based brain computer interface IEEE Int. Conf. on Acoustics, Speech and Signal Processing (IEEE) pp 581–4
  • Zhang Y, Zhou G, Jin J, Wang X and Cichocki A 2014, “Frequency recognition in SSVEP-based BCI using multiset canonical correlation analysis”. J. Neural Syst. 24 1450013
  • Zhang Y, Zhou G, Jin J, Wang X and Cichocki A 2015 Optimizing spatial patterns with sparse filter bands for motor-imagery based brain–computer interface J. Neurosci. Methods 255 85–91
  • Zhang, Y. U., Zhou, G., Jin, J., Wang, X., & Cichocki, A. (2014). “Frequency recognition in SSVEP-based BCI using multiset canonical correlation analysis”. International journal of neural systems, 24(04), 1450013.
  • Zhang, Y., Zhou, G., Jin, J., Wang, X., & Cichocki, A. (2015). “Optimizing spatial patterns with sparse filter bands for motor-imagery based brain–computer interface”.  Journal of neuroscience methods, 255, 85-91.
  • Zhang, Y., Zhou, G., Jin, J., Zhang, Y., Wang, X., & Cichocki, A. (2017). “Sparse Bayesian multiway canonical correlation analysis for EEG pattern recognition”. Neurocomputing, 225, 103-110.
  • Zhang Y, Zhou G, Zhao Q, Onishi A, Jin J, Wang Xand Cichocki,  2011, “Multiway canonical correlationanalysis for frequency components recognition in SSVEP-based BCIs”,  Neural Information Processing(Berlin: Springer)
  • Çınar, Salim. "Design of an automatic hybrid system for removal of eye-blink artifacts from EEG recordings."Biomedical Signal Processing and Control 67 (2021): 102543.
  • Trigui, Omar, et al. "Removal of eye blink artifacts from EEG signal using morphological modeling and orthogonal projection." Signal, Image and Video Processing1 (2022): 19-27.
  • Cao, Jiuwen, et al. "Unsupervised eye blink artifact detection from EEG with Gaussian mixture model." IEEE Journal of Biomedical and Health Informatics8 (2021): 2895-2905.
  • Wang, Jianhui, et al. "Eye blink artifact detection with novel optimized multi-dimensional electroencephalogram features." IEEE Transactions on Neural Systems and Rehabilitation Engineering 29 (2021): 1494-1503.
  • Egambaram, Ashvaany, et al. "Online detection and removal of eye blink artifacts from electroencephalogram." Biomedical Signal Processing and Control 69 (2021): 102887.
  • Borowicz, Adam. "Using a multichannel Wiener filter to remove eye-blink artifacts from EEG data." Biomedical Signal Processing and Control 45 (2018): 246-255.
  • Zhou, Weidong, and Jean Gotman. "Automatic removal of eye movement artifacts from the EEG using ICA and the dipole model." Progress in Natural Science9 (2009): 1165-1170.
  • Sreeja, S. R., et al. "Removal of eye blink artifacts from EEG signals using sparsity." IEEE journal of biomedical and health informatics5 (2017): 1362-1372.
  • He, Ping, G. Wilson, and C. Russell. "Removal of ocular artifacts from electro-encephalogram by adaptive filtering." Medical and biological engineering and computing3 (2004): 407-412.
  • Joyce, Carrie A., Irina F. Gorodnitsky, and Marta Kutas. "Automatic removal of eye movement and blink artifacts from EEG data using blind component separation." Psychophysiology2 (2004): 313-325.
  • Chintala, Sridhar, and Jaisingh Thangaraj. "Ocular artifact elimination from eeg signals using rvff-rls adaptive algorithm." 2020 National Conference on Communications (NCC). IEEE, 2020.
  • Yadav, Anchal, and Mahipal Singh Choudhry. "A new approach for ocular artifact removal from EEG signal using EEMD and SCICA." Cogent Engineering1 (2020): 1835146.
  • Gajbhiye, Pranjali, Rajesh Kumar Tripathy, and Ram Bilas Pachori. "Elimination of ocular artifacts from single channel EEG signals using FBSE-EWT based rhythms." IEEE Sensors Journal7 (2019): 3687-3696.
  • Islam, Md Kafiul, Parviz Ghorbanzadeh, and Amir Rastegarnia. "Probability mapping based artifact detection and removal from single-channel EEG signals for brain–computer interface applications." Journal of Neuroscience Methods 360 (2021): 109249.
  • Lee, Young-Eun, No-Sang Kwak, and Seong-Whan Lee. "A real-time movement artifact removal method for ambulatory brain-computer interfaces." IEEE Transactions on Neural Systems and Rehabilitation Engineering12 (2020): 2660-2670.
  • Song, Y., & Sepulveda, F. (2018). “A novel technique for selecting EMG-contaminated EEG channels in self-paced brain–computer Interface task onset”.IEEE Transactions on neural systems and rehabilitation engineering, 26(7), 1353-1362.
  • Krauledat, Matthias, et al. "Robustifying EEG data analysis by removing outliers." Chaos and Complexity Letters 2.3 (2007): 259-274. J. Clerk Maxwell, A Treatise on Electricity and Magnetism, 3rd ed., vol. 2. Oxford: Clarendon, 1892, pp.68–73.
  • Gouy-Pailler, Cédric, et al. "Iterative subspace decomposition for ocular artifact removal from EEG recordings." International Conference on Independent Component Analysis and Signal Separation. Springer, Berlin, Heidelberg, 2009. K. Elissa, “Title of paper if known,”
  • Croft, Rodney J., et al. "EOG correction: a comparison of four methods." Psychophysiology 42.1 (2005): 16-24. 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].
  • Young, The Technical Writer’s Handbook. Mill Valley, CA: University Science, 1989.
  • Jiang, Aimin, et al. "Efficient CSP algorithm with spatio-temporal filtering for motor imagery classification." IEEE Transactions on Neural Systems and Rehabilitation Engineering4 (2020): 1006-1016.
  • Isa, NE Md, et al. "Motor imagery classification in Brain computer interface (BCI) based on EEG signal by using machine learning technique." Bulletin of Electrical Engineering and Informatics1 (2019): 269-275.
  • Ang, Kai Keng, et al. "Filter bank common spatial pattern (FBCSP) in brain-computer interface." 2008 IEEE international joint conference on neural networks (IEEE world congress on computational intelligence). IEEE, 2008.
  • Ramoser, Herbert, Johannes Muller-Gerking, and Gert Pfurtscheller. "Optimal spatial filtering of single trial EEG during imagined hand movement." IEEE transactions on rehabilitation engineering4 (2000): 441-446.
  • Oh, Seung-Hyeon, Yu-Ri Lee, and Hyoung-Nam Kim. "A novel EEG feature extraction method using Hjorth parameter." International Journal of Electronics and Electrical Engineering2 (2014): 106-110.
  • Übeyli, Elif Derya, and İnan Güler. "Features extracted by eigenvector methods for detecting variability of EEG signals." Pattern Recognition Letters5 (2007): 592-603.
  • Stancin, Igor, Mario Cifrek, and Alan Jovic. "A review of EEG signal features and their application in driver drowsiness detection systems." Sensors11 (2021): 3786.
  • Stam, CJ van, and E. C. W. Van Straaten. "The organization of physiological brain networks." Clinical neurophysiology6 (2012): 1067-1087.
  • Übeyli, Elif Derya. "Analysis of EEG signals by implementing eigenvector methods/recurrent neural networks." Digital Signal Processing1 (2009): 134-143.
  • Gaur, Pramod, et al. "A sliding window common spatial pattern for enhancing motor imagery classification in EEG-BCI." IEEE Transactions on Instrumentation and Measurement 70 (2021): 1-9.
  • Bose, Rohit, et al. "Performance analysis of left and right lower limb movement classification from EEG." 2016 3rd International Conference on Signal Processing and Integrated Networks (SPIN). IEEE, 2016.
  • Raschka, Sebastian, David Julian, and John Hearty. Python: deeper insights into machine learning. Packt Publishing Ltd, 2016.
  • Isa, NE Md, et al. "Motor imagery classification in Brain computer interface (BCI) based on EEG signal by using machine learning technique." Bulletin of Electrical Engineering and Informatics1 (2019): 269-275.
  • Rish, Irina. "An empirical study of the naive Bayes classifier." IJCAI 2001 workshop on empirical methods in artificial intelligence. Vol. 3. No. 22. 2001.
  • Leung, K. Ming. "Naive bayesian classifier." Polytechnic University Department of Computer Science/Finance and Risk Engineering 2007 (2007): 123-156.
  • Berrar, Daniel. "Cross-Validation." (2019): 542-545.
  • Ang, Kai Keng, et al. "Filter bank common spatial pattern algorithm on BCI competition IV datasets 2a and 2b." Frontiers in neuroscience 6 (2012): 39.
  • Shenoy, H. Vikram, A. Prasad Vinod, and Cuntai Guan. "Shrinkage estimator based regularization for EEG motor imagery classification." 2015 10th International Conference on Information, Communications and Signal Processing (ICICS). IEEE, 2015.
  • Lupu, R. G., Ungureanu, F., & Cimpanu, C. (2019, May). “Brain-computer interface: Challenges and research perspectives”. In 2019 22nd International Conference on Control Systems and Computer Science (CSCS)(pp. 387-394). IEEE.
  • Fouad, M. M., Amin, K. M., El-Bendary, N., & Hassanien, A. E. (2015). “Brain computer interface: a review”.Brain-computer interfaces, 3-30.
  • Urigüen, J. A., & Garcia-Zapirain, B. (2015). “EEG artifact removal—state-of-the-art and guidelines”. Journal of neural engineering, 12(3), 031001.
  • Islam, M. K., Rastegarnia, A., & Yang, Z. (2016). “Methods for artifact detection and removal from scalp EEG: A review”. Neurophysiologie Clinique/Clinical Neurophysiology, 46(4-5), 287-305.
  • Mumtaz, W., Rasheed, S., & Irfan, A. (2021). “Review of challenges associated with the EEG artifact removal methods”. Biomedical Signal Processing and Control, 68, 102741.
  • Radüntz, T., Scouten, J., Hochmuth, O., & Meffert, B. (2015). “EEG artifact elimination by extraction of ICA-component features using image processing algorithms”. Journal of neuroscience methods, 243, 84-93.
  • Radüntz, T., Scouten, J., Hochmuth, O., & Meffert, B. (2017). “Automated EEG artifact elimination by applying machine learning algorithms to ICA-based features”. Journal of neural engineering, 14(4), 046004.
  • Roy, V., Shukla, P. K., Gupta, A. K., Goel, V., Shukla, P. K., & Shukla, S. (2021). “Taxonomy on EEG artifacts removal methods, issues, and healthcare applications”. Journal of Organizational and End User Computing (JOEUC), 33(1), 19-46.
  • Mannan, M. M. N., Kamran, M. A., & Jeong, M. Y. (2018). “Identification and removal of physiological artifacts from electroencephalogram signals: A review”. Ieee Access, 6, 30630-30652.
  • Gevins, A. S., Yeager, C. L., Zeitlin, G. M., Ancoli, S., & Dedon, M. F. (1977). “On-line computer rejection of EEG artifact”. Electroencephalography and clinical Neurophysiology, 42(2), 267-274.
  • Park, H. J., Jeong, D. U., & Park, K. S. (2002). “Automated detection and elimination of periodic ECG artifacts in EEG using the energy interval histogram method”. IEEE transactions on Biomedical Engineering, 49(12), 1526-1533.
  • Nolan, H., Whelan, R., & Reilly, R. B. (2010). “FASTER: fully automated statistical thresholding for EEG artifact rejection”. Journal of neuroscience methods, 192(1), 152-162.
  • Tatum, W. O., Dworetzky, B. A., & Schomer, D. L. (2011). “Artifact and recording concepts in EEG”. Journal of clinical neurophysiology, 28(3), 252-263.
  • Jung, C. Y., & Saikiran, S. S. (2016). “A review on EEG artifacts and its different removal technique”. Asia-pacific Journal of Convergent Research Interchange, 2(4), 43-60.
  • Jiang, X., Bian, G. B., & Tian, Z. (2019). “Removal of artifacts from EEG signals: a review”. Sensors, 19(5), 987.
  • Roháľová, M., Sykacek, P., Koskaand, M., & Dorffner, G. (2001). “Detection of the EEG Artifacts by the Means of the (Extended) Kalman Filter”.  Sci. Rev, 1(1), 59-62.
  • Blum, S., Jacobsen, N. S., Bleichner, M. G., & Debener, S. (2019). “A Riemannian modification of artifact subspace reconstruction for EEG artifact handling”. Frontiers in human neuroscience, 13, 141.
  • Shao, S. Y., Shen, K. Q., Ong, C. J., & Wilder-Smith, E. P. (2008). “Automatic EEG artifact removal: a weighted support vector machine approach with error correction”. IEEE Transactions on Biomedical Engineering, 56(2), 336-344.
  • Nejedly, P., Cimbalnik, J., Klimes, P., Plesinger, F., Halamek, J., Kremen, V., ... & Jurak, P. (2019). “Intracerebral EEG artifact identification using convolutional neural networks”. Neuroinformatics, 17(2), 225-234.
  • Somers, B., Francart, T., & Bertrand, A. (2018). “A generic EEG artifact removal algorithm based on the multi-channel Wiener filter”. Journal of neural engineering, 15(3), 036007.
  • Saba-Sadiya, S., Chantland, E., Alhanai, T., Liu, T., & Ghassemi, M. M. (2021). “Unsupervised EEG artifact detection and correction”. Frontiers in digital health, 2, 608920.
  • Islam, M. K., Rastegarnia, A., & Yang, Z. (2016). “Methods for artifact detection and removal from scalp EEG: A review”. Neurophysiologie Clinique/Clinical Neurophysiology, 46(4-5), 287-305.
  • Abreu, R., Leal, A., & Figueiredo, P. (2018). “EEG-informed fMRI: a review of data analysis methods”. Frontiers in human neuroscience, 12, 29.
  • Varone, G., Hussain, Z., Sheikh, Z., Howard, A., Boulila, W., Mahmud, M., ... & Hussain, A. (2021). “Real-time artifacts reduction during TMS-EEG co-registration: a comprehensive review on technologies and procedures”. Sensors, 21(2), 637.
  • Jung, T. P., Humphries, C., Lee, T. W., Makeig, S., McKeown, M. J., Iragui, V., & Sejnowski, T. J. (1998, September). “Removing electroencephalographic artifacts: comparison between ICA and PCA”. In Neural Networks for Signal Processing VIII. Proceedings of the 1998 IEEE Signal Processing Society Workshop (Cat. No. 98TH8378) (pp. 63-72). IEEE.
  • Anderer, P., Roberts, S., Schlögl, A., Gruber, G., Klösch, G., Herrmann, W., ... & Saletu, B. (1999). “Artifact processing in computerized analysis of sleep EEG–a review”. Neuropsychobiology, 40(3), 150-157.
  • Chen, X., Xu, X., Liu, A., Lee, S., Chen, X., Zhang, X., ... & Wang, Z. J. (2019). “Removal of muscle artifacts from the EEG: a review and recommendations”. IEEE Sensors Journal, 19(14), 5353-5368.
  • Cao, K., Guo, Y., & Su, S. W. (2015, December). “A review of motion related EEG artifact removal techniques”. In 2015 9th International Conference on Sensing Technology (ICST) (pp. 600-604). IEEE.
  • Klekowicz, H., Malinowska, U., Piotrowska, A. J., Wołyńczyk-Gmaj, D., Niemcewicz, S., & Durka, P. J. (2009). “On the robust parametric detection of EEG artifacts in polysomnographic recordings”. Neuroinformatics, 7(2), 147-160.
  • Minguillon, J., Lopez-Gordo, M. A., & Pelayo, F. (2017). “Trends in EEG-BCI for daily-life: Requirements for artifact removal”. Biomedical Signal Processing and Control, 31, 407-418.
  • Sadiya, S., Alhanai, T., & Ghassemi, M. M. (2021, May). “Artifact detection and correction in eeg data: A review”. In 2021 10th International IEEE/EMBS Conference on Neural Engineering (NER) (pp. 495-498). IEEE.
  • Craik, A., He, Y., & Contreras-Vidal, J. L. (2019). “Deep learning for electroencephalogram (EEG) classification tasks: a review”. Journal of neural engineering, 16(3), 031001.
  • Haumann, N. T., Parkkonen, L., Kliuchko, M., Vuust, P., & Brattico, E. (2016). “Comparing the performance of popular MEG/EEG artifact correction methods in an evoked-response study”. Computational Intelligence and Neuroscience, 2016.
  • Sazgar, M., & Young, M. G. (2019). “EEG artifacts”.  Absolute epilepsy and EEG rotation review (pp. 149-162). Springer, Cham.
  • Jung, T. P., Makeig, S., Humphries, C., Lee, T. W., Mckeown, M. J., Iragui, V., & Sejnowski, T. J. (2000). “Removing electroencephalographic artifacts by blind source separation”. Psychophysiology, 37(2), 163-178.
  • Kaya, I. (2019). “A brief summary of EEG artifact handling”. Brain-Computer Interface.
  • Taherisadr, M., Dehzangi, O., & Parsaei, H. (2017). “Single channel EEG artifact identification using two-dimensional multi-resolution analysis”. Sensors, 17(12), 2895.
  • Jafarifarmand, A., & Badamchizadeh, M. A. (2019). “EEG artifacts handling in a real practical brain–computer interface controlled vehicle”. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 27(6), 1200-1208.
  • Gorjan, D., Gramann, K., De Pauw, K., & Marusic, U. (2022). “Removal of movement-induced EEG artifacts: current state of the art and guidelines”. Journal of neural engineering.
  • Hartmann, M. M., Schindler, K., Gebbink, T. A., Gritsch, G., & Kluge, T. (2014). “PureEEG: Automatic EEG artifact removal for epilepsy monitoring”. Neurophysiologie Clinique/Clinical Neurophysiology, 44(5), 479-490.
  • Muthukumaraswamy, S. D. (2013). “High-frequency brain activity and muscle artifacts in MEG/EEG: a review and recommendations”.Frontiers in human neuroscience, 7, 138.
  • Kang, G., Jin, S. H., Kim, D. K., & Kang, S. W. (2018). T59. “EEG artifacts removal using machine learning algorithms and independent component analysis”.Clinical Neurophysiology, 129, e24.
Majlesi Journal of Electrical Engineering
Volume 18, Issue 1
March 2024
Pages 187-197
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