The Library
Space-time independent component analysis : the definitive BSS technique to use in biomedical signal processing?
Tools
James, C. J. and Demanuele, Charmaine (2010) Space-time independent component analysis : the definitive BSS technique to use in biomedical signal processing? In: 32nd Annual International Conference of the IEEE Engineering-in-Medicine-and-Biology-Society (EMBC 10), Buenos Aires, Argentina, 30 Aug -04 Sep 2010. Published in: 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) pp. 1898-1901. doi:10.1109/IEMBS.2010.5627351 ISSN 1557-170X.
Research output not available from this repository.
Request-a-Copy directly from author or use local Library Get it For Me service.
Official URL: http://dx.doi.org/10.1109/IEMBS.2010.5627351
Abstract
Independent Component Analysis (ICA) is a very common instantiation of the Blind Source Separation (BSS) problem. In the context of biomedical signal analysis, ICA is generally applied to multi-channel recordings of physiological phenomena in order to de-noise and extract meaningful information underlying the recordings. This paper assesses the Spatio-Temporal ICA (ST-ICA) framework, which uses both spatial and temporal information derived from multi-channel time-series to extract underlying sources. In contrast, the standard implementation of the ICA algorithm generally uses only limited spatial information to inform the separation process. One of the major steps in the implementation of any ICA algorithm is the selection of relevant components from the many ICA usually returns. With ST-ICA there is a rich data-set of components exhibiting spatial as well as temporal/spectral information that could be used to identify the underlying process subspaces extracted by the ST-ICA algorithm. This paper highlights the methodology for performing ST-ICA and assesses the possible ways in which process subspace identification may take place.
Item Type: | Conference Item (Paper) | ||||
---|---|---|---|---|---|
Subjects: | Q Science > QA Mathematics T Technology > TK Electrical engineering. Electronics Nuclear engineering |
||||
Divisions: | Faculty of Science, Engineering and Medicine > Engineering > WMG (Formerly the Warwick Manufacturing Group) | ||||
Library of Congress Subject Headings (LCSH): | Independent component analysis, Blind source separation, Signal processing, Biomedical engineering | ||||
Journal or Publication Title: | 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) | ||||
Publisher: | IEEE | ||||
ISSN: | 1557-170X | ||||
Book Title: | 2010 Annual International Conference of the IEEE Engineering in Medicine and Biology | ||||
Official Date: | 2010 | ||||
Dates: |
|
||||
Page Range: | pp. 1898-1901 | ||||
DOI: | 10.1109/IEMBS.2010.5627351 | ||||
Status: | Peer Reviewed | ||||
Publication Status: | Published | ||||
Conference Paper Type: | Paper | ||||
Title of Event: | 32nd Annual International Conference of the IEEE Engineering-in-Medicine-and-Biology-Society (EMBC 10) | ||||
Type of Event: | Conference | ||||
Location of Event: | Buenos Aires, Argentina | ||||
Date(s) of Event: | 30 Aug -04 Sep 2010 |
Data sourced from Thomson Reuters' Web of Knowledge
Request changes or add full text files to a record
Repository staff actions (login required)
View Item |