DocumentCode :
2942780
Title :
Space-Time Independent Component Analysis: The definitive BSS technique to use in biomedical signal processing?
Author :
James, Christopher J. ; Demanuele, Charmaine
Author_Institution :
Inst. of Digital Healthcare, Univ. of Warwick, Coventry, UK
fYear :
2010
fDate :
Aug. 31 2010-Sept. 4 2010
Firstpage :
1898
Lastpage :
1901
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.
Keywords :
blind source separation; electroencephalography; independent component analysis; magnetoencephalography; medical signal processing; signal denoising; spatiotemporal phenomena; time series; BSS technique; biomedical signal processing; blind source separation; denoising; multichannel recordings; multichannel time series; process subspace identification; space-time independent component analysis; spatiotemporal ICA; Data mining; Delay; Electroencephalography; Finite impulse response filter; Independent component analysis; Signal processing algorithms; Algorithms; Biomedical Engineering; Cluster Analysis; Data Interpretation, Statistical; Electroencephalography; Electrophysiology; Equipment Design; Head; Humans; Principal Component Analysis; Signal Processing, Computer-Assisted; Time Factors;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Engineering in Medicine and Biology Society (EMBC), 2010 Annual International Conference of the IEEE
Conference_Location :
Buenos Aires
ISSN :
1557-170X
Print_ISBN :
978-1-4244-4123-5
Type :
conf
DOI :
10.1109/IEMBS.2010.5627351
Filename :
5627351
Link To Document :
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