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On independent component analysis based on spatial, temporal and spatio-temporal information in biomedical signals

On independent component analysis based on spatial, temporal and spatio-temporal information in biomedical signals
On independent component analysis based on spatial, temporal and spatio-temporal information in biomedical signals
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.
blind source separation, independent component analysis, spatial ica, temporal ica, spatiotemporal ica
9783540892076
22
34-37
Springer
James, C.J.
b3733b1f-a6a1-4c9b-b75c-6191d4142e52
James, C.J.
b3733b1f-a6a1-4c9b-b75c-6191d4142e52

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. Springer. pp. 34-37 . (doi:10.1007/978-3-540-89208-3_10).

Record type: Conference or Workshop Item (Paper)

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.

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More information

Published date: 2008
Venue - Dates: 4th European Congress for Medical and Biomedical Engineering, Antwerp, Belgium, 2008-11-23 - 2008-11-27
Keywords: blind source separation, independent component analysis, spatial ica, temporal ica, spatiotemporal ica

Identifiers

Local EPrints ID: 65325
URI: http://eprints.soton.ac.uk/id/eprint/65325
ISBN: 9783540892076
PURE UUID: bf8e13b6-7958-49f2-8250-06e78f53d6fd

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Date deposited: 04 Mar 2009
Last modified: 15 Mar 2024 12:07

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Author: C.J. James

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