Independent component analysis for biomedical signals
Identifieur interne : 000160 ( Main/Exploration ); précédent : 000159; suivant : 000161Independent component analysis for biomedical signals
Auteurs : Christopher J. James [Royaume-Uni] ; Christian W. Hesse [Royaume-Uni]Source :
- Physiological Measurement [ 0967-3334 ] ; 2005.
Abstract
Independent component analysis (ICA) is increasing in popularity in the field of biomedical signal processing. It is generally used when it is required to separate measured multi-channel biomedical signals into their constituent underlying components. The use of ICA has been facilitated in part by the free availability of toolboxes that implement popular flavours of the techniques. Fundamentally ICA in biomedicine involves the extraction and separation of statistically independent sources underlying multiple measurements of biomedical signals. Technical advances in algorithmic developments implementing ICA are reviewed along with new directions in the field. These advances are specifically summarized with applications to biomedical signals in mind. The basic assumptions that are made when applying ICA are discussed, along with their implications when applied particularly to biomedical signals. ICA as a specific embodiment of blind source separation (BSS) is also discussed, and as a consequence the criterion used for establishing independence between sources is reviewed and this leads to the introduction of ICA/BSS techniques based on time, frequency and joint timefrequency decomposition of the data. Finally, advanced implementations of ICA are illustrated as applied to neurophysiologic signals in the form of electro-magnetic brain signals data.
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DOI: 10.1088/0967-3334/26/1/R02
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It is generally used when it is required to separate measured multi-channel biomedical signals into their constituent underlying components. The use of ICA has been facilitated in part by the free availability of toolboxes that implement popular flavours of the techniques. Fundamentally ICA in biomedicine involves the extraction and separation of statistically independent sources underlying multiple measurements of biomedical signals. Technical advances in algorithmic developments implementing ICA are reviewed along with new directions in the field. These advances are specifically summarized with applications to biomedical signals in mind. The basic assumptions that are made when applying ICA are discussed, along with their implications when applied particularly to biomedical signals. ICA as a specific embodiment of blind source separation (BSS) is also discussed, and as a consequence the criterion used for establishing independence between sources is reviewed and this leads to the introduction of ICA/BSS techniques based on time, frequency and joint timefrequency decomposition of the data. Finally, advanced implementations of ICA are illustrated as applied to neurophysiologic signals in the form of electro-magnetic brain signals data.</div></front></TEI><affiliations><list><country><li>Royaume-Uni</li></country></list><tree><country name="Royaume-Uni"><noRegion><name sortKey="James, Christopher J" sort="James, Christopher J" uniqKey="James C" first="Christopher J" last="James">Christopher J. James</name></noRegion><name sortKey="Hesse, Christian W" sort="Hesse, Christian W" uniqKey="Hesse C" first="Christian W" last="Hesse">Christian W. Hesse</name><name sortKey="James, Christopher J" sort="James, Christopher J" uniqKey="James C" first="Christopher J" last="James">Christopher J. James</name></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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