The DOI matches its Crossref record; 61 of the 61 references it cites were found in a registry or an index.
Imaging brain dynamics using independent component analysis
Open accessAbstract
The analysis of electroencephalographic (EEG) and magnetoencephalographic (MEG) recordings is important both for basic brain research and for medical diagnosis and treatment. Independent component analysis (ICA) is an effective method for removing artifacts and separating sources of the brain signals from these recordings. A similar approach is proving useful for analyzing functional magnetic resonance brain imaging (fMRI) data. In this paper, we outline the assumptions underlying ICA and demonstrate its application to a variety of electrical and hemodynamic recordings from the human brain.
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Cites
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Cited by
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Self-citationDynamic Brain Sources of Visual Evoked Responses
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Self-citationMining event-related brain dynamics
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Self-citationFrontal midline EEG dynamics during working memory
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Self-citationAnalysis and visualization of single‐trial event‐related potentials
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Metadata
- Authors
- Jung, T.-P., Makeig, S., McKeown, M. J., Bell, A. J., Lee, T.-W., & Sejnowski, T. J.
- Venue
- Proceedings of the IEEE
- Published
- 2001-07
- Volume
- 89
- Issue
- 7
- Pages
- 1107-1122
- ISSN
- 0018-9219 (ISSN-L)
- OpenAlex
- W2162726907
- Cited by
- 592
- References
- 61
- FWCI
- 30.82
- Type
- journal article
- Language
- en
- Open access
- green
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- Jung, T.-P., Makeig, S., McKeown, M. J., Bell, A. J., Lee, T.-W., & Sejnowski, T. J. (2001). Imaging brain dynamics using independent component analysis. Proceedings of the IEEE. https://doi.org/10.1109/5.939827
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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.
