Independent component analysis of magnetoencephalographic signals
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
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.