The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Independent component approach to the analysis of EEG and MEG recordings
Abstract
Multichannel recordings of the electromagnetic fields emerging from neural currents in the brain generate large amounts of data. Suitable feature extraction methods are, therefore, useful to facilitate the representation and interpretation of the data. Recently developed independent component analysis (ICA) has been shown to be an efficient tool for artifact identification and extraction from electroencephalographic (EEG) and magnetoencephalographic (MEG) recordings. In addition, ICA has been applied to the analysis of brain signals evoked by sensory stimuli. This paper reviews our recent results in this field.
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Cites
An Information-Maximization Approach to Blind Separation and Blind Deconvolution
Bell, A. J., & Sejnowski, T. J. (1995) · Neural Computation · doi:10.1162/neco.1995.7.6.1129 · Cited by 8,986
Emergence of simple-cell receptive field properties by learning a sparse code for natural images
Olshausen, B. A., & Field, D. J. (1996) · Nature · doi:10.1038/381607a0 · Cited by 5,694
Cited by
Default-mode brain dysfunction in mental disorders: A systematic review
Broyd, S. J., Demanuele, C., Debener, S., Helps, S. K., James, C. J., & Sonuga-Barke, E. J. S. (2009) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2008.09.002 · Cited by 1,675
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
EEG frequency bands in psychiatric disorders: a review of resting state studies
Newson, J. J., & Thiagarajan, T. C. (2019) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2018.00521 · Cited by 863
MEG: An Introduction to Methods
(2010) · Oxford University Press eBooks · doi:10.1093/acprof:oso/9780195307238.001.0001 · Cited by 408
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
Metadata
- Authors
- Vigario, R., Sarela, J., Jousmiki, V., Hamalainen, M., & Oja, E.
- Venue
- IEEE Transactions on Biomedical Engineering
- Published
- 2000-05
- Volume
- 47
- Issue
- 5
- Pages
- 589-593
- ISSN
- 0018-9294 (ISSN-L)
- OpenAlex
- W2124433161
- Cited by
- 796
- References
- 2
- FWCI
- 25.95
- Type
- journal article
- Language
- en
- Open access
- closed
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- Vigario, R., Sarela, J., Jousmiki, V., Hamalainen, M., & Oja, E. (2000). Independent component approach to the analysis of EEG and MEG recordings. IEEE Transactions on Biomedical Engineering. https://doi.org/10.1109/10.841330
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