Published March 30, 2023
| Version CC BY-NC-ND 4.0
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A Review on Detection and Correction of Artifacts from EEG Data
Creators
- 1. Assistant Professor, Indian Institute of Information Technology, Nagpur (Maharashtra), India
- 2. Professor, Poornima University, Jaipur (Rajasthan), India
Contributors
Contact person:
- 1. Assistant Professor, Indian Institute of Information Technology, Nagpur (Maharashtra), India
Description
Abstract: Electroencephalography (EEG) offers a wide range of uses in a variety of industries. Low SNR (signal to noise ratios), nevertheless, limit EEG applicability. EEG noise is caused by a variety of artifacts and numerous strategies have already been developed to identify and eliminate these inconsistencies. Various methods differ from merely identifying and discarding artifact ridden segments to isolating the EEG signal's noise content. With an emphasis on the previous half decade, we discuss a range of contemporary and traditional strategies for EEG data artifact recognition and removal. We assess the approaches' merits and drawbacks before proposing potential prospects for the area.
Notes
Published By: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP)
© Copyright: All rights reserved.
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- Journal article: 2277-3878 (ISSN)
References
- S. Sadiya, T. Alhanai and M. M. Ghassemi, "Artifact Detection and Correction in EEG data: A Review," 2021 10th International IEEE/EMBS Conference on Neural Engineering (NER), 2021, pp. 495-498, doi: 10.1109/NER49283.2021.9441341.
- N. B. Shamlo, K. Kreutz-Delgado, C. Kothe, and S. Makeig, "Eyecatch: Data-mining over half a million eeg independent components to construct a fully-automated eye-component detector," 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 5845–5848, 2013.
- E. Nedelcu, R. Portase, R. Tolas, R. Muresan, M. Dinsoreanu, and R. Potolea, "Artifact detection in eeg using machine learning," in 2017 13th IEEE International Conference on Intelligent Computer Communication and Processing (ICCP), 2017, pp. 77–83.
- K. K. Dutta, K. Venugopal, and S. A. Swamy, "Removal of muscle artifacts from eeg based on ensemble empirical mode decomposition and classification of seizure using machine learning techniques," in 2017 International Conference on Inventive Computing and Informatics (ICICI), 2017, pp. 861–866.
- R. C. M. P. Gilberet, R. S. Roy, N. J. Sairamya, D. N. Ponraj, and S. T. George, "Automated artifact rejection using ica and image processing algorithms," in 2017 International Conference on Signal Processing and Communication (ICSPC), 2017, pp. 354–358.
- P. Nejedly, J. Cimbalnik, P. Klimeˇs, F. Plesinger, J. Halamek, V. Kˇremen, I. Viscor, B. Brinkmann, M. Pail, M. Brazdil, G. Worrell, and P. Jurak, "Intracerebral eeg artifact identification using convolutional neural networks," Neuroinformatics, vol. 17, 08 2018.
- B. Somers, T. Francart, and A. Bertrand, "A generic eeg artifact removal algorithm based on the multi-channel wiener filter." Journal of neural engineering, vol. 15 3, p. 036007, 2018.
- M. Agarwal and R. Sivakumar, "Blink: A fully automated unsupervised algorithm for eye-blink detection in eeg signals," in 2019 57th Annual Allerton Conference on Communication, Control, and Computing (Allerton), 2019, pp. 1113–1121.
- R. Ghosh, N. Sinha, and S. K. Biswas, "Automated eye blink artefact removal from eeg using support vector machine and autoencoder," IET Signal Processing, vol. 13, no. 2, pp. 141–148, 2019.
- 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, 09 2019.
- S. Blum, N. S. J. Jacobsen, M. G. Bleichner, and S. Debener, "A riemannian modification of artifact subspace reconstruction for eeg artifact handling," Frontiers in Human Neuroscience, vol. 13, 2019.
- S. Saba-Sadiya, E. Chantland, T. Alhanai, T. Liu, and M. M. Ghassemi, "Unsupervised eeg artifact detection and correction," Frontiers in Digital Health, 2021.
- S. Phadikar, N. Sinha, and R. Ghosh, "Automatic eeg eyeblink artefact identification and removal technique using independent component analysis in combination with support vector machines and denoising autoencoder," IET Signal Processing, vol. 14.6, pp. 396–405, 2020.
- H. Zhang, M. Zhao, C. Wei, D. Mantini, Z. Li, and Q. Liu, "Eegdenoisenet: A benchmark dataset for deep learning solutions of eeg denoising," 2020.
- E. K. S. Louis, L. C. Frey, J. W. Britton, J. L. Hopp, P. J. Korb, M. Z. Koubeissi, W. E. Lievens, and E. M. Pestana-Knight, "Electroencephalography (eeg): An introductory text and atlas of normal and abnormal findings in adults, children, and infants," 2016.
- M. M. Ghassemi, B. E. Moody, L. H. Lehman, C. Song, Q. Li, H. Sun, R. G. Mark, M. B. Westover, and G. D. Clifford, "You snooze, you win: the physionet/computing in cardiology challenge 2018," in 2018 Computing in Cardiology Conference (CinC), vol. 45, 2018, pp. 1–4.
- S. Saba-Sadiya, T. Liu, T. Alhanai, and M. Ghassemi, "Eeg channel interpolation using deep encoder-decoder networks," in IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2020.
- 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, 2020.
Subjects
- ISSN: 2277-3878 (Online)
- https://portal.issn.org/resource/ISSN/2277-3878
- Retrieval Number: 100.1/ijrte.F74970311623
- https://www.ijrte.org/portfolio-item/F74970311623/
- Journal Website
- https://www.ijrte.org/
- Publisher: Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP)
- https://www.blueeyesintelligence.org/