The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.
Canonical Correlation Analysis Applied to Remove Muscle Artifacts From the Electroencephalogram
Abstract
The electroencephalogram (EEG) is often contaminated by muscle artifacts. In this paper, a new method for muscle artifact removal in EEG is presented, based on canonical correlation analysis (CCA) as a blind source separation (BSS) technique. This method is demonstrated on a synthetic data set. The method outperformed a low-pass filter with different cutoff frequencies and an independent component analysis (ICA)-based technique for muscle artifact removal. In addition, the method is applied on a real ictal EEG recording contaminated with muscle artifacts. The proposed method removed successfully the muscle artifact without altering the recorded underlying ictal activity.
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Citations
Cites
RELATIONS BETWEEN TWO SETS OF VARIATES
HOTELLING, H. (1936) · Biometrika · doi:10.1093/biomet/28.3-4.321 · Cited by 5,263
A blind source separation technique using second-order statistics
Belouchrani, A., Abed-Meraim, K., Cardoso, J.-F., & Moulines, E. (1997) · IEEE Transactions on Signal Processing · doi:10.1109/78.554307 · Cited by 2,775
Relations Between Two Sets of Variates
Hotelling, H. (1936) · Biometrika · doi:10.2307/2333955 · Cited by 1,645
High-Order Contrasts for Independent Component Analysis
Cardoso, J.-F. (1999) · Neural Computation · doi:10.1162/089976699300016863 · Cited by 1,213
EMG contamination of EEG: spectral and topographical characteristics
Goncharova, I. I., McFarland, D. J., Vaughan, T. M., & Wolpaw, J. R. (2003) · Clinical Neurophysiology · doi:10.1016/s1388-2457(03)00093-2 · Cited by 696
IFCN standards for digital recording of clinical EEG
Nuwer, M. R., Comi, G., Emerson, R., Fuglsang-Frederiksen, A., Guérit, J.-M., Hinrichs, H., Ikeda, A., Jose C. Luccas, F., & Rappelsburger, P. (1998) · Electroencephalography and Clinical Neurophysiology · doi:10.1016/s0013-4694(97)00106-5 · Cited by 671
Yeredor, A. (2002) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2002.1011195 · Cited by 397
TDSEP — an efficient algorithm for blind separation using time structure
Ziehe, A., & Müller, K.-R. (1998) · Perspectives in Neural Computing · doi:10.1007/978-1-4471-1599-1_103 · Cited by 334
Independent Component Analysis as a Tool to Eliminate Artifacts in EEG: A Quantitative Study
Iriarte, J., Urrestarazu, E., Valencia, M., Alegre, M., Malanda, A., Viteri, C., & Artieda, J. (2003) · Journal of Clinical Neurophysiology · doi:10.1097/00004691-200307000-00004 · Cited by 289
Detection of neural activity in functional MRI using canonical correlation analysis
Friman, O., Cedefamn, J., Lundberg, P., Borga, M., & Knutsson, H. (2001) · Magnetic Resonance in Medicine · doi:10.1002/1522-2594(200102)45:2<323::aid-mrm1041>3.0.co;2-# · Cited by 240
Cited by
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
Review on solving the forward problem in EEG source analysis
Hallez, H., Vanrumste, B., Grech, R., Muscat, J., De Clercq, W., Vergult, A., D'Asseler, Y., Camilleri, K. P., Fabri, S. G., Van Huffel, S., et al. (2007) · Journal of NeuroEngineering and Rehabilitation · doi:10.1186/1743-0003-4-46 · Cited by 527
Self-citationSignal Artifacts and Techniques for Artifacts and Noise Removal
Islam, M. K., Rastegarnia, A., & Sanei, S. (2021) · Intelligent Systems Reference Library · doi:10.1007/978-3-030-54932-9_2 · Cited by 54
Role of N-Methyl D-aspartate receptors in action-based predictive coding deficits in schizophrenia
Kort, N. S., Ford, J. M., Roach, B. J., Gunduz-Bruce, H., Krystal, J. H., Jaeger, J., Reinhart, R. M. G., & Mathalon, D. H. (2017) · Biological Psychiatry · doi:10.1016/j.biopsych.2016.06.019 · Cited by 48
Spectral Variability in the Aged Brain during Fine Motor Control
Quandt, F., Bönstrup, M., Schulz, R., Timmermann, J. E., Zimerman, M., Nolte, G., & Hummel, F. C. (2016) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2016.00305 · Cited by 38
“Each participant performed 80 pinch and 80 whole hand grips.”
Method
Metadata
- Authors
- Clercq, W. D., Vergult, A., Vanrumste, B., Van Paesschen, W., & Van Huffel, S.
- Venue
- IEEE Transactions on Biomedical Engineering
- Published
- 2006-11
- Volume
- 53
- Issue
- 12
- Pages
- 2583-2587
- ISSN
- 0018-9294 (ISSN-L)
- 1558-2531
- OpenAlex
- W2082930519
- Cited by
- 522
- References
- 14
- FWCI
- 4.30
- Type
- journal article
- Language
- en
- Open access
- closed
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
