Department of Electronics Engineering, Poornima University, Jaipur.

Electroencephalogram or EEG is a trace of Brain action from different locales of the cerebrum. It is the electrical movement and activity determined by putting the electrode terminals on the scalp. Artifacts are unnecessary noise signals in an EEG record. These noise in recording EEG sham a major mortification for EEG interpretation and disposal. Categorization of artifacts depends on source of its creation similar to Physiological artifacts along with Exterior artifacts. Recognition of artifacts, identification of artifacts as well as eradication of artifacts is a significant procedure to diminish the possibility of false impression of EEG not only for clinical but also for non-clinical fields. The majority of recording convention. There are various strategies for artifact removal which incorporates manual and automatic techniques. Morphology and Electrical distinctiveness of artifacts can show the way to fake elucidation that is intolerable in support of clinical as well as non clinical utilization. Thus artifacts in EEG signals must be removed or minimized before further interpretation. The presented paper describes a review on detection, classification and removal/ minimization of the recorded EEG signal artifacts.

Keywords: EEG Signal, Artifacts, Artifact Detection, Artifact Removal.

Full-text PDF

How to cite this article:
SAGAR S. MOTDHARE and ARUN DEV DHAR DWIVEDI. DETECTION AND REMOVAL OF ARTIFACTS FROM EEG RECORDS- A REVIEW. Subclinical chronic sinusitis causing presumed ventriculoperitoneal shunt sepsis in a child. American Journal of Engineering Research and Reviews, 2020, 3:22


1. Tandle, Avinash, et al. “Classification of Artefacts in EEG Signal Recordings and EOG Artefact Removal Using EOG Subtraction.” Communications on Applied Electronics, vol. 4, no. 1, pp. 12–19., doi:10.5120/cae2016651997, 2016.
2. Savelainen, Antti. “An Introduction to EEG Artifacts.” Semantic Scholar,, , 2010
3. Mayeli, A., Zotev, V., Refai, H. and Bodurka, J., “Real-time EEG artifact correction during fMRI using ICA”. Journal of Neuroscience Methods, pp.27-37, 2016
4. Zhou, W. and Gotman, J., “Automatic removal of eye movement artifacts from the EEG using ICA and the dipole model”. Progress in Natural Science, Science Direct 19(9), pp.1165-1170.
5. Mammone, N., La Foresta, F. and Morabito, F., “Automatic Artifact Rejection From Multichannel Scalp EEG by Wavelet ICA”. IEEE Sensors Journal, 12(3), pp.533-542, 2012
6. Joyce, C., Gorodnitsky, I. and Kutas, M., “Automatic removal of eye movement and blink artifacts from EEG data using blind component separation”. Psychophysiology, Blackwell Publishing, 41(2), pp.313-325, 2004
7. Mognon, A., Jovicich, J., Bruzzone, L. and Buiatti, M., “ADJUST: An automatic EEG artifact detector based on the joint use of spatial and temporal features”. Psychophysiology, Wiley Periodicals, 48(2), pp.229-240, 2011
8. Radüntz, T., Scouten, J., Hochmuth, O. and Meffert, B. “Automated EEG artifact elimination by applying machine learning algorithms to ICA-based features” Journal of Neural Engineering, 14(4), p.046004, 2017.
9. Chaumon, M., Bishop, D. and Busch, N. “A practical guide to the selection of independent components of the electroencephalogram for artifact correction.” Journal of Neuroscience Methods, 250, pp.47-63, 2015
10. Delorme, A., Sejnowski, T. and Makeig, S., “Enhanced detection of artifacts in EEG data using higher-order statistics and independent component analysis.” NeuroImage, 34(4), pp.1443-1449, 2007
11. Porcaro, C., Medaglia, M. and Krott, A, “Removing speech artifacts from electroencephalographic recordings during overt picture naming.” NeuroImage, 105, pp.171-180, 2015
12. Jervis, B., Garcia, M., Thomlinson, M., Lopez, J. and Mair, C. “Residual ocular artefact subsequent to ocular artefact removal from the electroencephalogram”. IEEE Proceedings – Science, Measurement and Technology, 146(6), pp.293-298, 1999
13. Puce, A. and Hämäläinen, M., “A Review of Issues Related to Data Acquisition and Analysis in EEG/MEG Studies.” Brain Sciences, 7(12), p.58, 2017
14. Delorme, A., Sejnowski, T. and Makeig, S. “Enhanced detection of artifacts in EEG data using higher-order statistics and independent component analysis.” NeuroImage, 34(4), pp.1443-1449, 2007
15. Gwin, Joseph T., et al. “Removal of Movement Artifact From High-Density EEG Recorded During Walking and Running.” Journal of Neurophysiology, vol. 103, no. 6, 2010, pp. 3526–3534., doi:10.1152/jn.00105, 2010.
16. Antti Savelainin (2010), “An Introduction to EEG Artifacts”, 63220J Independent Research Projects in Applied Mathematics. 63220J, 2010.
17. Gandhi, Tapan, et al. “Expert Model for Detection of Epileptic Activity in EEG Signature.” Expert Systems with Applications, vol. 37, no. 4, 2010, pp. 3513–3520., doi:10.1016/j.eswa.2009.10.036.
18. Kiret Dhindsa, “Filter-Bank Artifact Rejection: High Performance Real-Time Single-Channel Artifact Detection for EEG” Biomedical Signal Processing and Control, ResearchGate, pp 1-16, September 2019 DOI:
19. L. F. Nicolas-Alonso and J. Gomez-Gil, “Brain computer interfaces, a review.” Sensors (Basel, Switzerland), vol. 12, no. 2, pp. 1211–79, 2012.
20. J. J. Shih, D. J. Krusienski, and J. R. Wolpaw, “Braincomputer interfaces in medicine.” Mayo Clinic proceedings. Mayo Clinic, vol. 87, no. 3, pp. 268–279, Mar. 2012.
21. M. Fatourechi, A. Bashashati, R. K. Ward, and G. E. Birch, “EMG and EOG artifacts in brain computer interface systems: A survey.” Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology, vol. 118, no. 3, pp. 480–94, Mar. 2007. [Online]. Available:
22. J. A. Uriguen and B. Garcia-Zapirain, “Eeg artifact¨ removalstate-of-the-art and guidelines,” Journal of neural engineering, vol. 12, no. 3, p. 031001, 2015.
23. Daly, R. Scherer, M. Billinger, and G. Muller-Putz,¨ “FORCe: Fully online and automated artifact removal for brain-computer interfacing,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 23, no. 5, pp. 725– 736, 2015.
24. J. Minguillon, M. A. Lopez-Gordo, and F. Pelayo, “Trends in EEG-BCI for daily-life: Requirements for artifact removal,” Biomedical Signal Processing and Control, vol. 31, pp. 407–418, 2017.
25. X. Chen, A. Liu, J. Chiang, Z. J. Wang, M. J. McKeown, and R. K. Ward, “Removing muscle artifacts from EEG data: Multichannel or single-channel techniques?” IEEE Sensors Journal, vol. 16, no. 7, pp. 1986–1997, 2016.
26. X. Li, C. Guan, H. Zhang, and K. K. Ang, “Discriminative ocular artifact correction for feature learning in eeg analysis,” IEEE Transactions on Biomedical Engineering, 2016.
27. R. Patel, M. P. Janawadkar, S. Sengottuvel, K. Gireesan, and T. S. Radhakrishnan, “Suppression of eye-blink associated artifact using single channel eeg data by combining crosscorrelation with empirical mode decomposition,” IEEE Sensors Journal, vol. 16, no. 18, pp. 6947–6954, 2016.
28. Adeli, H., Zhou, Z., & Dadmehr, N. “Analysis of {EEG} records in an epileptic patient using wavelet transform. Journal of Neuroscience Methods”, 123(1), 69 – 87. Retrieved from article/pii/S0165027002003400 doi: -0270(02)00340-0, 2003.
29. Frederik, V.; Luca, F.; Esin, K.; Jitkomut, S.; Pedro, A.V.; Daniele, M. “Critical comments on EEG sensor space dynamical connectivity analysis”. Brain Topogr. 2016, 1–12.
30. K. G. A. Lakshmi, S. N. N. Surling and O. Sheeba, “A novel approach for the removal of artifacts in EEG signals,” 2017 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET), Chennai, 2017, pp. 2595-2599.
doi: 10.1109/WiSPNET.2017.8300232
31. Kumar, P. S., Arumuganathan, R., Sivakumar, K., & Vimal, C. (2008). “Removal of artifacts from EEG signals using adaptive filter through wavelet transform”. 2008 9th International Conference on Signal Processing. doi:10.1109/icosp.2008.4697569.
32. Rahman, F. A., Othman, M. F., & Shaharuddin, N. A. (2015). “A review on the current state of artifact removal methods for electroencephalogram signals”. 2015 10th Asian Control Conference (ASCC). doi:10.1109/ascc.2015.7244679.
33. Cassani, R., Falk, T. H., Fraga, F. J., Kanda, P. A., & Anghinah, R. (2014). “The effects of automated artifact removal algorithms on electroencephalography-based Alzheimers disease diagnosis”. Frontiers in Aging Neuroscience, 6. doi:10.3389/fnagi.2014.00055.
34. Priyanka Jain (2014), “Removal of Artifacts and analysis of EEG signal using data driven parameters”, Proceedings of IEEE TechSym 2014 Satellite Conference, VIT University, 7 to 8 March 2014.
35. Valipour, S., Kulkarni, G. R., & Shaligram, A. D. (2015). “Study on Performance Metrics for Consideration of Efficiency of the Ocular Artifact Removal Algorithms for EEG Signals”. Indian Journal of Science and Technology, 8(30). doi:10.17485/ijst/2015/v8i1/76018.
36. Rashid, A., & Qureshi, I. M. (2015). “Eliminating Electroencephalogram Artefacts Using Independent Component Analysis.” International Journal of Applied Mathematics, Electronics and Computers,3(1), 48. doi:10.18100/ijamec.99374.
37. Achanccaray, David & Meggiolaro, Marco. (2008) “Detection of artifacts from EEG data using wavelet transform, high-order statistics and neural networks”. XVII Brazilian Conference on Automatica.
38. Puce, Aina, and Matti Hämäläinen. “A Review of Issues Related to Data Acquisition and Analysis in EEG/MEG Studies.” Brain Sciences, vol. 7, no. 12, 2017, p. 58., doi:10.3390/brainsci7060058.
39. Akhtar, Muhammad Tahir, Mitsuhashi, Wataru, & James, Christopher
J. (2012). Employing spatially constrained ICA and wavelet
denoising, for automatic removal of artifacts from multichannel
EEG data. Signal Processing, 92(2), 401 -416.
40. Geetha G. and Geethalakshmi S.N., “Artifact Removal from EEG using Spatially Constrained Independent Component Analysis and Wavelet Denoising with Otsu’s Thresholding Technique”, International Conference on Communication
Technology and System Design, 2012.
41. Petr Nejedly, “Intracerebral EEG Artifact Identification using Convolutional Neural Networks”, Neuroinformatics (2019), pp-225-234,
42. Devuyst, S., et al. “Removal of ECG Artifacts from EEG Using a Modified Independent Component Analysis Approach.” 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2008, doi:10.1109/iembs.2008.4650387.
43. Dora, Chinmayee, and Pradyut Kumar Biswal. “Robust ECG Artifact Removal from EEG Using Continuous Wavelet Transformation and Linear Regression.” 2016 International Conference on Signal Processing and Communications (SPCOM), 2016, doi:10.1109/spcom.2016.7746620.
44. Patel, Rajesh, et al. “Common Methodology for Cardiac and Ocular Artifact Suppression from EEG Recordings by Combining Ensemble Empirical Mode Decomposition with Regression Approach.” Journal of Medical and Biological Engineering, vol. 37, no. 2, 2017, pp. 201–208., doi:10.1007/s40846-016-0208-y.
45. Kashid, Rupal, and K. P. Paradeshi. “Design of Effective Algorithm for EMG Artifact Removal from Multichannel EEG Data Using ICA and Wavelet Method.” Advances in Intelligent Systems and Computing Intelligent Systems Design and Applications, 2019, pp. 955–964., doi:10.1007/978-3-030-16660-1_93.

Terms of Use/Privacy Policy/ Disclaimer/ Other Policies:
You agree that by using our site, you have read, understood, and agreed to be bound by all of our terms of use/privacy policy/ disclaimer/ other policies (click here for details).

This work and its PDF file(s) are licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.