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On independent component analysis based on spatial, temporal and spatio-temporal information in biomedical signals
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James, C. J. (2008) On independent component analysis based on spatial, temporal and spatio-temporal information in biomedical signals. In: 4th European Conference Of The International Federation For Medical And Biological Engineering, Antwerp, Belgium, Nov 23-27, 2008. Published in: IFMBE Proceedings, Vol.22 pp. 34-37. doi:10.1007/978-3-540-89208-3_10 ISSN 1680-0737.
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Official URL: http://dx.doi.org/10.1007/978-3-540-89208-3_10
Abstract
Independent Component Analysis (ICA) as a Blind Source Separation technique has been used in biomedical signal processing applications for over a decade now. A common goal for ICA is in de-noising multiple signal recordings, for artefact removal and source separation and extraction. ICA decomposes a set of multi-channel measurements into a corresponding set of underlying sources using the assumption of independence between the sources as the separation criterion. Most commonly, ICA is applied as ensemble ICA (E-ICA), where a series of spatial filters are derived from the multi-channel recordings giving rise to independent components underlying the measurements. Where single channel recordings only are available or desirable it is not possible to apply the standard E-ICA model. In previous work we have introduced a Single-Channel ICA (SC-ICA) algorithm that can extract multiple underlying sources from a single channel measurement. Whereas E-ICA utilizes spatial information in the multi-channel recordings, SC-ICA utilizes wholly temporal information to inform the separation process. The two algorithms have differing underlying assumptions for the separation process. A natural extension is to combine the information inherent in both spatial and temporal recordings through the use of a Spatio- Temporal ICA (ST-ICA) algorithm. Here we review three implementations of these ICA algorithms as outlined above, and as applied to biomedical signal recordings. We show that standard implementations of ICA (E-ICA) can be lacking when attempting to extract complex underlying activity. SCICA performs well in separating underlying sources from a single measurement channel, although it is clearly lacking in spatial information, whereas ST-ICA uses both temporal as well as spatial information to inform the ICA process. ST-ICA results in information rich Spatio-Temporal filters which allows the extraction of independent sources which are both quasi-spectrally overlapping as well as having very similar spatial profiles — both of which are not possible in SC-ICA and E-ICA respectively.
Item Type: | Conference Item (Paper) | ||||
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Subjects: | R Medicine > R Medicine (General) T Technology > TK Electrical engineering. Electronics Nuclear engineering |
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Divisions: | Faculty of Science, Engineering and Medicine > Engineering > WMG (Formerly the Warwick Manufacturing Group) | ||||
Journal or Publication Title: | IFMBE Proceedings | ||||
Publisher: | International Federation for Medical and Biological Engineering | ||||
ISSN: | 1680-0737 | ||||
Book Title: | 4th European Conference of the International Federation for Medical and Biological Engineering | ||||
Official Date: | 16 December 2008 | ||||
Dates: |
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Volume: | Vol.22 | ||||
Page Range: | pp. 34-37 | ||||
DOI: | 10.1007/978-3-540-89208-3_10 | ||||
Status: | Peer Reviewed | ||||
Publication Status: | Published | ||||
Access rights to Published version: | Restricted or Subscription Access | ||||
Conference Paper Type: | Paper | ||||
Title of Event: | 4th European Conference Of The International Federation For Medical And Biological Engineering | ||||
Type of Event: | Conference | ||||
Location of Event: | Antwerp, Belgium | ||||
Date(s) of Event: | Nov 23-27, 2008 |
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