Temporally constrained ICA: an application to artifact rejection in electromagnetic brain signal analysis

James, C.J. and Gibson, O.J. (2003) Temporally constrained ICA: an application to artifact rejection in electromagnetic brain signal analysis IEEE Transactions on Biomedical Engineering, 50, (9), pp. 1108-1116. (doi:10.1109/TBME.2003.816076).


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Independent component analysis (ICA) is a technique which extracts statistically independent components from a set of measured signals. The technique enjoys numerous applications in biomedical signal analysis in the literature, especially in the analysis of electromagnetic (EM) brain signals. Standard implementations of ICA are restrictive mainly due to the square mixing assumption-for signal recordings which have large numbers of channels, the large number of resulting extracted sources makes the subsequent analysis laborious and highly subjective. There are many instances in neurophysiological analysis where there is strong a priori information about the signals being sought; temporally constrained ICA (cICA) can extract signals that are statistically independent, yet which are constrained to be similar to some reference signal which can incorporate such a priori information. We demonstrate this method on a synthetic dataset and on a number of artifactual waveforms identified in multichannel recordings of EEG and MEG. cICA repeatedly converges to the desired component within a few iterations and subjective analysis shows the waveforms to be of the expected morphologies and with realistic spatial distributions. This paper shows that cICA can be applied with great success to EM brain signal analysis, with an initial application in automating artifact extraction in EEG and MEG.

Item Type: Article
Digital Object Identifier (DOI): doi:10.1109/TBME.2003.816076
ISSNs: 0018-9294 (print)
Keywords: artifact rejection, constrained ica, eeg, ica, independent component analysis, meg

ePrint ID: 10980
Date :
Date Event
September 2003Published
Date Deposited: 28 Apr 2005
Last Modified: 16 Apr 2017 23:56
Further Information:Google Scholar
URI: http://eprints.soton.ac.uk/id/eprint/10980

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