The DOI matches its Crossref record; 93 of the 93 references it cites were found in a registry or an index.
Removal of Artifacts from EEG Signals: A Review
Open accessAbstract
Electroencephalogram (EEG) plays an important role in identifying brain activity and behavior. However, the recorded electrical activity always be contaminated with artifacts and then affect the analysis of EEG signal. Hence, it is essential to develop methods to effectively detect and extract the clean EEG data during encephalogram recordings. Several methods have been proposed to remove artifacts, but the research on artifact removal continues to be an open problem. This paper tends to review the current artifact removal of various contaminations. We first discuss the characteristics of EEG data and the types of different artifacts. Then, a general overview of the state-of-the-art methods and their detail analysis are presented. Lastly, a comparative analysis is provided for choosing a suitable methods according to particular application.
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Huang, N. E., Shen, Z., Long, S. R., Wu, M. C., Shih, H. H., Zheng, Q., Yen, N.-C., Tung, C. C., & Liu, H. H. (1998) · Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences · doi:10.1098/rspa.1998.0193 · Cited by 23,564
Matching pursuits with time-frequency dictionaries
Mallat, S. G., & Zhang, Z. (1993) · IEEE Transactions on Signal Processing · doi:10.1109/78.258082 · Cited by 9,112
ENSEMBLE EMPIRICAL MODE DECOMPOSITION: A NOISE-ASSISTED DATA ANALYSIS METHOD
WU, Z., & HUANG, N. E. (2009) · Advances in Adaptive Data Analysis · doi:10.1142/s1793536909000047 · Cited by 8,715
HomER: A review of time-series analysis methods for near-infrared spectroscopy of the brain
Huppert, T. J., Diamond, S. G., Franceschini, M. A., & Boas, D. A. (2009) · Applied Optics · doi:10.1364/ao.48.00d280 · Cited by 1,817
Identifying true brain interaction from EEG data using the imaginary part of coherency
Nolte, G., Bai, O., Wheaton, L., Mari, Z., Vorbach, S., & Hallett, M. (2004) · Clinical Neurophysiology · doi:10.1016/j.clinph.2004.04.029 · Cited by 1,756
Jung, T.-P., Makeig, S., Westerfield, M., Townsend, J., Courchesne, E., & Sejnowski, T. J. (2000) · Clinical Neurophysiology · doi:10.1016/s1388-2457(00)00386-2 · Cited by 1,322
EEG artifact removal—state-of-the-art and guidelines
Urigüen, J. A., & Garcia-Zapirain, B. (2015) · Journal of Neural Engineering · doi:10.1088/1741-2560/12/3/031001 · Cited by 963
Chaumon, M., Bishop, D. V. M., & Busch, N. A. (2015) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2015.02.025 · Cited by 833
Independent component approach to the analysis of EEG and MEG recordings
Vigario, R., Sarela, J., Jousmiki, V., Hamalainen, M., & Oja, E. (2000) · IEEE Transactions on Biomedical Engineering · doi:10.1109/10.841330 · Cited by 796
Removal of ocular artifact from the EEG: a review
Croft, R. J., & Barry, R. J. (2000) · Neurophysiologie Clinique/Clinical Neurophysiology · doi:10.1016/s0987-7053(00)00055-1 · Cited by 756
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Summary of over Fifty Years with Brain-Computer Interfaces—A Review
Kawala-Sterniuk, A., Browarska, N., Al-Bakri, A., Pelc, M., Zygarlicki, J., Sidikova, M., Martinek, R., & Gorzelanczyk, E. J. (2021) · Brain Sciences · doi:10.3390/brainsci11010043 · Cited by 292
Khosla, A., Khandnor, P., & Chand, T. (2020) · Biocybernetics and Biomedical Engineering · doi:10.1016/j.bbe.2020.02.002 · Cited by 267
Neural Decoding of EEG Signals with Machine Learning: A Systematic Review
Saeidi, M., Karwowski, W., Farahani, F. V., Fiok, K., Taiar, R., Hancock, P. A., & Al-Juaid, A. (2021) · Brain Sciences · doi:10.3390/brainsci11111525 · Cited by 258
Decoding Covert Speech From EEG-A Comprehensive Review
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Metadata
- Authors
- Jiang, X., Bian, G.-B., & Tian, Z.
- Venue
- Sensors
- Publisher
- MDPI AG
- Published
- 2019-02-26
- Volume
- 19
- Issue
- 5
- Pages
- 987
- ISSN
- 1424-8220 (ISSN-L)
- OpenAlex
- W2915196348
- Cited by
- 826
- References
- 93
- FWCI
- 46.51
- Type
- journal article
- Language
- en
- Open access
- gold
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