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.

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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)
Subjects:	R Medicine > R Medicine (General) T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions:	Faculty of Science > 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
Date:	16 December 2008
Volume:	Vol.22
Page Range:	pp. 34-37
Identification Number:	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
URI:	http://wrap.warwick.ac.uk/id/eprint/47161

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