Artifact Removal Methods in EEG Recordings: A Review
To obtain the correct analysis of electroencephalogram (EEG) signals, non-physiological and physiological artifacts should be removed from EEG signals. This study aims to give an overview on the existing methodology for removing physiological artifacts, e.g., ocular, cardiac, and muscle artifacts. The datasets, simulation platforms, and performance measures of artifact removal methods in previous related research are summarized. The advantages and disadvantages of each technique are discussed, including regression method, filtering method, blind source separation (BSS), wavelet transform (WT), empirical mode decomposition (EMD), singular spectrum analysis (SSA), and independent vector analysis (IVA). Also, the applications of hybrid approaches are presented, including discrete wavelet transform - adaptive filtering method (DWT-AFM), DWT-BSS, EMD-BSS, singular spectrum analysis - adaptive noise canceler (SSA-ANC), SSA-BSS, and EMD-IVA. Finally, a comparative analysis for these existing methods is provided based on their performance and merits. The result shows that hybrid methods can remove the artifacts more effectively than individual methods.
H. Hu, S. Guo, R. Liu, and P. Wang, “An Adaptive Singular Spectrum Analysis Method for Extracting Brain Rhythms of Electroencephalography,” Peerj, vol. 5, e3474, June 2017.
A. K. Abdullah, C. Z. Zhang, A. A. A. Abdullah, and S. Lian, “Automatic Extraction System for Common Artifacts in EEG Signals Based on Evolutionary Stone’s BSS Algorithm,” Mathematical Problems in Engineering, vol. 2014, 324750, August 2014.
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.
R. Zafar, A. Qayyum, and W. Mumtaz, “Automatic Eye Blink Artifact Removal for EEG Based on a Sparse Coding Technique for Assessing Major Mental Disorders,” Journal of Integrative Neuroscience, vol. 18, no. 3, pp. 217-229, 2019.
A. Morley, L. Hill, and A. G. Kaditis, “10-20 System EEG Placement,”
https://www.Ers-Education.Org/Lrmedia/2016/Pdf/298830.Pdf, March 15, 2016.
S. K. Goh, H. A. Abbass, K. C. Tan, A. Al-Mamun, C. Wang, and C. Guan, “Automatic EEG Artifact Removal Techniques by Detecting Influential Independent Components,” IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 1, no. 4, pp. 270-279, August 2017.
S. R. Sreeja, R. R. Sahay, D. Samanta, and P. Mitra, “Removal of Eye Blink Artifacts from EEG Signals Using Sparsity,” IEEE Journal of Biomedical and Health Informatics, vol. 22, no. 5, pp. 1362-1372, September 2018.
X. Jiang, G. B. Bian, and Z. Tian, “Removal of Artifacts from EEG Signals: A Review,” Sensors, vol. 19, no. 5, 987, March 2019.
S. S. Menon and K. Krishnamurthy, “A Study of Brain Neuronal and Functional Complexities Estimated Using Multiscale Entropy in Healthy Young Adults,” Entropy, vol. 21, no. 10, 995, October 2019.
K. T. Sweeney, S. F. McLoone, and T. E. Ward, “The Use of Ensemble Empirical Mode Decomposition with Canonical Correlation Analysis as a Novel Artifact Removal Technique,” IEEE Transactions on Biomedical Engineering, vol. 60, no. 1, pp. 97-105, January 2013.
M. Chavez, F. Grosselin, A. Bussalb, F. D. V. Fallani, and X. Navarro-Sune, “Surrogate-Based Artifact Removal from Single-Channel EEG,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 26, no. 3, pp. 540-550, March 2018.
Y. Zou, V. Nathan, and R. Jafari, “Automatic Identification of Artifact-Related Independent Components for Artifact Removal in EEG Recordings,” IEEE Journal of Biomedical and Health Informatics, vol. 20. no. 1, pp. 73-81, January 2016.
R. Mahajan and B. I. Morshed, “Unsupervised Eye Blink Artifact Denoising of EEG Data with Modified Multiscale Sample Entropy, Kurtosis, and Wavelet-ICA,” IEEE Journal of Biomedical and Health Informatics, vol. 19, no. 1, pp. 158-165, January 2015.
P. P. Acharjee, R. Phlypo, L. Wu, V. D. Calhoun, and T. Adalı, “Independent Vector Analysis for Gradient Artifact Removal in Concurrent EEG-fMRI Data,” IEEE Transactions on Biomedical Engineering, vol. 62, no. 7, pp. 1750-1758, July 2015.
S. Khatun, R. Mahajan, and B. I. Morshed, “Comparative Study of Wavelet-Based Unsupervised Ocular Artifact Removal Techniques for Single-Channel EEG Data,” IEEE Journal of Translational Engineering in Health and Medicine, vol. 4, pp. 1-8, March 2016.
M. N. Tibdewal, R. R. Fate, M. Mahadevappa, A. K. Ray, and M. Malokar, “Classification of Artifactual EEG Signal and Detection of Multiple Eye Movement Artifact Zones Using Novel Time-Amplitude Algorithm,” Signal, Image, and Video Processing, vol. 11, no. 2, pp. 333-340, February 2017.
Q. Liu, Y. F. Chen, S. Z. Fan, M. F. Abbod, and J. S. Shieh, “EEG Artifacts Reduction by Multivariate Empirical Mode Decomposition and Multiscale Entropy for Monitoring Depth of Anaesthesia During Surgery,” Medical and Biological Engineering and Computing, vol. 55, no. 8, pp. 1435-1450, August 2017.
S. Barua, M. U. Ahmed, C. Ahlstrom, S. Begum, and P. Funk, “Automated EEG Artifact Handling with Application in Driver Monitoring,” IEEE Journal of Biomedical and Health Informatics, vol. 22, no. 5, pp. 1350-1361, September 2018.
C. Y. Sai, N. Mokhtar, H. Arof, P. Cumming, and M. Iwahashi, “Automated Classification and Removal of EEG Artifacts with SVM and Wavelet-ICA,” IEEE Journal of Biomedical and Health Informatics, vol. 22, no. 3, pp. 664-670, May 2018.
J. Li, Y. Chen, F. Taya, J. Lim, K. Wong, Y. Sun, et al., “A Unified Canonical Correlation Analysis-Based Framework for Removing Gradient Artifact in Concurrent EEG/fMRI Recording and Motion Artifact in Walking Recording from EEG Signal,” Medical and Biological Engineering and Computing, vol. 55, no. 9, pp. 1669-1681, September 2017.
V. Roy and S. Shukla, “Effective EEG Motion Artifacts Elimination Based on Comparative Interpolation Analysis,” Wireless Personal Communications, vol. 97, no. 4, pp. 6441-6451, December 2017.
C. Y. Chang, S. H. Hsu, L. Pion-Tonachini, and T. P. Jung, “Evaluation of Artifact Subspace Reconstruction for Automatic Artifact Components Removal in Multi-Channel EEG Recordings,” IEEE Transactions on Biomedical Engineering, vol. 67, no. 4, pp. 1114-1121, April 2020.
J. Cheng, L. Li, C. Li, Y. Liu, A. Liu, R. Qian, et al., “Remove Diverse Artifacts Simultaneously from a Single-Channel EEG Based on SSA and ICA: A Semi-Simulated Study,” IEEE Access, vol. 7, pp. 60276-60289, 2019.
M. Saini and U. Satija, “An Effective and Robust Framework for Ocular Artifact Removal from Single-Channel EEG Signal Based on Variational Mode Decomposition,” IEEE Sensors Journal, vol. 20, no. 1, pp. 369-376, January 2020.
Y. Liu, Y. Zhou, X. Lang, Y. Liu, Q. Zheng, Y. Zhang, et al., “An Efficient and Robust Muscle Artifact Removal Method for Few-Channel EEG,” IEEE Access, vol. 7, pp. 176036-176050, 2019.
C. Dora and P. K. Biswal, “Correlation-Based ECG Artifact Correction from Single Channel EEG Using Modified Variational Mode Decomposition,” Computer Methods and Programs in Biomedicine, vol. 183, 105092, January 2020.
C. Dora, R. N. Patro, S. K. Rout, P. K. Biswal, and B. Biswal, “Adaptive SSA Based Muscle Artifact Removal from Single Channel EEG Using Neural Network Regressor,” Innovation and Research in Biomedical Engineering, in press.
D. P. Yedurkar and S. P. Metkar, “Multiresolution Approach for Artifacts Removal and Localization of Seizure onset Zone in Epileptic EEG Signal,” Biomedical Signal Processing and Control, vol. 57, 101794, March 2020.
M. S. Islam, A. M. El-Hajj, H. Alawieh, Z. Dawy, N. Abbas, and J. El-Imad, “EEG Mobility Artifact Removal for Ambulatory Epileptic Seizure Prediction Applications,” Biomedical Signal Processing and Control, vol. 55, 101638, January 2020.
N. Bajaj, J. R. Carrión, F. Bellotti, R. Berta, and A. De Gloria, “Automatic and Tunable Algorithm for EEG Artifact Removal Using Wavelet Decomposition with Applications in Predictive Modeling During Auditory Tasks,” Biomedical Signal Processing and Control, vol. 55, 101624, January 2020.
S. K. Noorbasha and G. F. Sudha, “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,” Biomedical Signal Processing and Control, vol. 63, 102168, January 2021.
M. A. Klados, C. Papadelis, C. D. Lithari, and P. D. Bamidis, “The Removal of Ocular Artifacts from EEG Signals: A Comparison of Performances for Different Methods,” 4th European Conference of the International Federation for Medical and Biological Engineering, November 2008, pp. 1259-1263.
Y. Chen, Q. Zhao, B. Hu, J. Li, H. Jiang, W. Lin, et al., “A Method of Removing Ocular Artifacts from EEG Using Discrete Wavelet Transform and Kalman Filtering,” IEEE International Conference on Bioinformatics and Biomedicine, December 2016, pp. 15-18.
A. Zhang and W. Li, “Adaptive Noise Cancellation for Removing Cardiac and Respiratory Artifacts from EEG Recordings,” 5th World Congress on Intelligent Control and Automation, June 2004, pp. 15-19.
H. Shahabi, S. Moghimi, and H. Zamiri-Jafarian, “EEG Eye Blink Artifact Removal by EOG Modeling and Kalman Filter,” 5th International Conference on Biomedical Engineering and Informatics, October 2012, pp. 356-367.
A. Mur, R. Dormido, and N. Duro, “An Unsupervised Method for Artefact Removal in EEG Signals,” Sensors, vol. 19, no. 10, 2302, May 2019.
B. Mijović, M. De Vos, I. Gligorijević, J. Taelman, and S. Van Huffel, “Source Separation from Single-Channel Recordings by Combining Empirical-Mode Decomposition and Independent Component Analysis,” IEEE Transactions on Biomedical Engineering, vol. 57, no. 9, pp. 2188-2196, September 2010.
V. Roy and S. Shukla, “A Survey on Artifacts Detection Techniques for Electro-Encephalography (EEG) Signals,” International Journal of Multimedia and Ubiquitous Engineering, vol. 10, no. 3, pp. 425-442, 2015.
G. Wang, C. Teng, K. Li, Z. Zhang, and X. Yan, “The Removal of EOG Artifacts from EEG Signals Using Independent Component Analysis and Multivariate Empirical Mode Decomposition,” IEEE Journal of Biomedical and Health Informatics, vol. 20, no. 5, pp. 1301-1308, September 2016.
X. Chen, A. Liu, H. Peng, and R. K. Ward, “A Preliminary Study of Muscular Artifact Cancellation in Single-Channel EEG,” Sensors, vol. 14, no. 10, pp. 18370-18389, October 2014.
D. R. Hardoon, S. Szedmak, and J. Shawe-Taylor, “Canonical Correlation Analysis: An Overview with Application to Learning Methods,” Neural Computation, vol. 16, no. 12, pp. 2639-2664, December 2004.
W. De Clercq, A. Vergult, B. Vanrumste, W. Van Paesschen, and S. Van Huffel, “Canonical Correlation Analysis Applied to Remove Muscle Artifacts from the Electroencephalogram,” IEEE Transactions on Biomedical Engineering, vol. 53, no. 12, pp. 2583-2587, 2006.
X. Chen, X. Xu, A. Liu, M. J. Mckeown, and Z. J. Wang, “The Use of Multivariate EMD and CCA for Denoising Muscle Artifacts from Few-Channel EEG Recordings,” IEEE Transactions on Instrumentation and Measurement, vol. 67, no. 2, pp. 359-370, February 2018.
S. Khatun, R. Mahajan, and B. I. Morshed, “Comparative Study of Wavelet-Based Unsupervised Ocular Artifact Removal Techniques for Single Channel EEG Data,” IEEE Journal of Translational Engineering in Health and Medicine, vol. 4, 2000108, 2016.
P. Gajbhiye, R. K. Tripathy, A. Bhattacharyya, and R. B. Pachori, “Novel Approaches for the Removal of Motion Artifact from EEG Recordings,” IEEE Sensors Journal, vol. 19, no. 22, pp. 10600-10608, November 2019.
B. S. Raghavendra and D. N. Dutt, “Wavelet Enhanced CCA for Minimization of Ocular and Muscle Artifacts in EEG,” World Academy of Science, Engineering, and Technology, vol. 5, no. 9, pp. 419-424, January 2011.
E. Karatoprak and S. Seker, “An Improved Empirical Mode Decomposition Method Using Variable Window Median Filter for Early Fault Detection in Electric Motors,” Mathematical Problems in Engineering, vol. 2019, 8015295, 2019.
B. Yang, T. Zhang, Y. Zhang, W. Liu, J. Wang, and K. Duan, “Removal of Electrooculogram Artifacts from Electroencephalogram Using Canonical Correlation Analysis with Ensemble Empirical Mode Decomposition,” Cognitive Computation, vol. 9, no. 5, pp. 626-633, October 2017.
N. E. Huang and Z. Wu, “A Review on Hilbert-Huang Transform: Method and its Applications to Geophysical Studies,” Reviews of Geophysics, vol. 46, no. 2, RG2006, June 2008.
N. E. Huang, M. L. C. Wu, S. R. Long, S. S. Shen, W. Qu, P. Gloersen, et al., “A Confidence Limit for the Empirical Mode Decomposition and Hilbert Spectral Analysis,” Proceedings of the Royal Society A: Mathematical, Physical, and Engineering Sciences, vol. 459, no. 2037. pp. 2317-2345, September 2003.
S. Xu, H. Hu, L. Ji, and P. Wang, “Embedding Dimension Selection for Adaptive Singular Spectrum Analysis of EEG Signal,” Sensors, vol. 18, no. 3, 697, March 2018.
Q. Liu, A. Liu, X. Zhang, X. Chen, R. Qian, and X. Chen, “Removal of EMG Artifacts from Multichannel EEG Signals Using Combined Singular Spectrum Analysis and Canonical Correlation Analysis,” Journal of Healthcare Engineering, vol. 2019, 4159676, 2019.
M. Anderson, T. Adali, and X. L. Li, “Joint Blind Source Separation with Multivariate Gaussian Model: Algorithms and Performance Analysis,” IEEE Transaction on Signal Process, vol. 60, no. 4, pp. 1672-1683, April 2012.
T. Kim, T. Eltoft, and T. W. Lee, “Independent Vector Analysis: An Extension of ICA to Multivariate Components,” International Conference on Independent Component Analysis and Signal Separation, March 2006, pp. 165-172.
X. Chen, A. Liu, Q. Chen, Y. Liu, L. Zuo, and M. J. Mckeown, “Simultaneous Ocular and Muscle Artifact Removal from EEG Data by Exploiting Diverse Statistics,” Computers in Biology and Medicine, vol. 88, pp. 1-10, September 2017.
H. Peng, B. Hu, Q. Shi, M. Ratcliffe, Q. Zhao, Y. Qi, et al., “Removal of Ocular Artifacts in EEG—An Improved Approach Combining DWT and ANC for Portable Applications,” IEEE Journal of Biomedical and Health Informatics, vol. 17, no. 3, pp. 600-607, May 2013.
L. Zou, X. Chen, G. Dang, Y. Guo, and Z. J. Wang, “Removing Muscle Artifacts from EEG Data via Underdetermined Joint Blind Source Separation: A Simulation Study,” IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 67, no. 1, pp. 187-191, January 2020.
A. K. Maddirala and R. A. Shaik, “Removal of EOG Artifacts from Single Channel EEG Signals Using Combined Singular Spectrum Analysis and Adaptive Noise Canceler,” IEEE Sensors Journal, vol. 16, no. 23, pp. 8279-8287, December 2019.
X. Chen, A. Liu, J. Chiang, Z. J. Wang, M. J. Mckeown, and R. K. Ward, “Removing Muscle Artifacts from EEG Data: Multi-Channel or Single-Channel Techniques,” IEEE Sensors Journal, vol. 16, no. 7, pp. 1986-1997, April 2016.
X. Xu, X. Chen, and Y. Zhang, “Removal of Muscle Artefacts from Few-Channel EEG Recordings Based on Multivariate Empirical Mode Decomposition and Independent Vector Analysis,” Electronics Letters, vol. 54, no. 14, pp. 866-868, July 2018.
X. Chen, Q. Liu, W. Tao, L. Li, S. Lee, A. Liu, et al., “ReMAE: User-Friendly Toolbox for Removing Muscle Artifacts from EEG,” IEEE Transactions on Instrumentation and Measurement, vol. 69, no. 5, pp. 2105-2119, May 2020.
X. Chen, Q. Chen, Y. Zhang, and Z. J. Wang, “A Novel EEMD-CCA Approach to Removing Muscle Artifacts for Pervasive EEG,” IEEE Sensors Journal, vol. 19, no. 19. pp. 8420-8431, October 2019.
C. Dai, J. Wang, J. Xie, W. Li, Y. Gong, and Y. Li, “Removal of ECG Artifacts from EEG Using an Effective Recursive Least Square Notch Filter,” IEEE Access, vol. 7, pp. 158872-158880, October 2019.
X. Chen, X. Xu, A. Liu, S. Lee, X. Chen, X. Zhang, et al., “Removal of Muscle Artifacts from the EEG: A Review and Recommendations,” IEEE Sensors Journal, vol. 19, no. 14, pp. 5353-5368, July 2019.
Copyright (c) 2021 Mariyadasu Mathe, Padmaja Mididoddi, Battula Tirumala Krishna
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Submission of a manuscript implies: that the work described has not been published before that it is not under consideration for publication elsewhere; that if and when the manuscript is accepted for publication. Authors can retain copyright of their article with no restrictions. Also, author can post the final, peer-reviewed manuscript version (postprint) to any repository or website.
Since Oct. 01, 2015, PETI will publish new articles with Creative Commons Attribution Non-Commercial License, under The Creative Commons Attribution Non-Commercial 4.0 International (CC BY-NC 4.0) License.
The Creative Commons Attribution Non-Commercial (CC-BY-NC) License permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes