[FieldTrip] strange observations using ICA

[weberi at staff.uni-marburg.de]

Dear Eelke,

thank you for the explanation. I am not sure I understand
completely, but I get the gist of it. The reason why we don't use
a band-stop filter is that we fear it may have a great influence on  
the subsequent
analysis methods, especially regarding directionality measures or measures
working in the time domain.

Thank you and best regards,
Immo

Zitat von Eelke Spaak <e.spaak at donders.ru.nl>:

> 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
> harmonics)?
>
> Hope that helps,
> Best,
> Eelke
>
> 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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