[FieldTrip] strange observations using ICA

Eelke Spaak e.spaak at donders.ru.nl
Thu Aug 9 10:16:14 CEST 2018

Dear Immo,

While I don't have a clear-cut answer, I could imagine that something
like the following is going on. The ICA decomposition is never
perfect, and the isolation of individual components will get worse
with decreasing component power. So, as you say, removing the first
few components might indeed decrease the artifactual power, but at
some point removing components that have a clear 130 Hz peak might end
up instead *adding* 130 Hz power into channels for which the component
weight is nonzero (but which, in a perfect decomposition, should have
been zero). (I'm not sure I'm explaining this well enough; in any
case, the key is to keep in mind that ICA is simply a linear
projection of the data.)

Since you say that the artifact has very well-defined spectral
characteristics, might it not be simpler and more effective to use a
band-stop filter to take out 129-131 Hz (and similarly for the

Hope that helps,

On 9 August 2018 at 09:26,  <weberi at staff.uni-marburg.de> wrote:
> Dear FieldTrip community,
> I have a question concerning some strange observations when using ICA.
> In our lab we try to get rid of an EEG artefact induced by deep brain
> stimulation.
> For this purpose we compute an ICA of our EEG-channels and sort the
> components
> relative to their similarity (quantified using mutual information) with the
> raw artefact
> measured  directly from the stimulator. We then successively remove the
> components
> according to their mutual information and calculate the mean frequency
> spectrum of the
> back-transformed corrected data. The artefact has a very defined frequency
> distribution
> with peaks at multiples of 130 Hz.  With our approach, we would expect the
> artefactual
> peaks to successively become smaller. However, this is not the case. While
> the peaks
> ultimately become smaller after removal of several components, the
> artefactual power
> even rises again after the first couple of components. How can this be
> explained?
> Any insights would be much appreciated,
> Best regards
> Immo Weber
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> https://doi.org/10.1371/journal.pcbi.1002202

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