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Title: Independent component analysis of magnetoencephalographic signals
Author: Papathanassiou, Christos
ISNI:       0000 0001 3467 0908
Awarding Body: University of Surrey
Current Institution: University of Surrey
Date of Award: 2003
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Magnetoencephalography (MEG) is a non-invasive brain imaging technique which allows instant tracking of changes in brain activity. However, it is affected by strong artefact signals generated by the heart or the eye blinking. The blind source separation problem is typically encountered in MEG studies when a set of unknown signals, originating from different sources inside or outside the brain, is mixed with an also unknown mixing matrix during their recording. Independent component analysis (ICA) is a recently developed technique which aims to estimate the original sources given only the observed mixtures. ICA can decompose the observed data into the original biological sources. However, ICA suffers from a major intrinsic ambiguity. In particular, it cannot determine the order of extraction of the source signals. Thus, if there are numerous source signals hidden in lengthy MEG recordings, the extraction of the biological signal of interest can be an extremely prolonged procedure. In this thesis, a modification of the ordinary ICA is introduced in order to cope with this ambiguity. In case there is prior knowledge concerning one of the original signals, this information is exploited by adding a penalty/constraint term to the standard ICA quality function in order to favour the extraction of that particular signal. Our approach requires no reference signal, but the knowledge of some statistical property of one of the original sources, namely its autocorrelation function. Our algorithm is validated with simulated data for which the mixing matrix is known, and is also applied to real MEG data to remove artefact signals. Finally, it is demonstrated how ICA can simplify the ill-posed problem of localising the sources/dipoles in the cortex (inverse problem). The advantage of ICA lies in using nonaveraged trials. In addition, there is no need to know in advance the number of dipoles.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available