[FieldTrip] coherence/connectivity measures after applying ICA

Rodolphe Nenert batrod at gmail.com
Wed Mar 28 02:20:42 CEST 2012

Dear Tessa,

ok this is more clear now.
ICA is a wonderful tool which can easily become your worst enemy in data

By removing yourself certains epochs with blinks before doing an ICA, its
is possible that you remove relevant data that can be used by your ICA
algorithm to better separate components.  Certain people would argue that
even filtering your data before ICA can be a bad idea.
Therefore, when you apply the ICA on your "cleaned" data, there is more
chance that the component that will reflect the most what is left from
blinks will also contains more relevant data.
This data removal could potentially decrease your data variability
therefore increase your coherence.
Also, the number of epochs you keep after your cleaning (which should be
different from ICA compared to visual removal) could potentially explain
such a difference.

Hope this helps,


On Tue, Mar 27, 2012 at 4:34 PM, Tessa van Leeuwen <
tessa.vanleeuwen at fcdonders.ru.nl> wrote:

> Dear Rodolphe,
> Thank you for your response, I tried to clarify below.
> 1) After your first "cleaning", do you test coherence on particular
> components or your entire data minus the "EOG" component?
> I tested the coherence on the entire dataset after removal of the EOG
> component.
> 2) When you say that you redo ICA on cleaned data (of course, an ICA
> analysis made on a result of a previous ICA analysis with components
> removed is a bad idea), do you remove another component or do you  test
> your coherence on particular components?
> Sorry, I could have been more clear about this. I initially compared two
> versions of the same dataset: one in which all trials containing a blink
> were removed from the dataset after manual inspection; another version in
> which only trials with a blink during the actual stimulus period were
> removed manually and the rest of the trials, including trials with blinks
> outside the stimulus window, were 'cleaned' with ICA, i.e. the EOG
> component was removed. Eye-balling coherence in occipital channels, this
> was increased in the ICA-cleaned version.
> To check whether the increase in coherence could have been explained by an
> increased number of retained trials in the ICA-cleaned-version, I also
> applied the unmixing matrix obtained from the ICA to the manually cleaned
> version of the data, i.e., in which only smaller eye-movements or
> unidentified EOG artifacts would have been remaining after manual
> inspection. Now of course I could have been over-removing non-existent
> noise in this case, but also here the same difference in coherence
> appeared, now with the same number of trials in both condition. When I look
> at the time-frequency representations from both versions, these look highly
> similar, only small intensity differences can be seen.
> I only used a small amount of data to test this and these results might
> therefore not be completely reliable. But I know other people have
> experienced similar problems with altered coherence and I was wondering
> whether any effect of ICA-preprocessing on coherence/connectivity measures
> was generally known on the list and in the literature. Perhaps the removal
> of common noise with ICA can already explain the differences?
> Best wishes,
> Tessa
> Rodolphe
> On Tue, Mar 27, 2012 at 11:27 AM, Tessa van Leeuwen <
> tessa.vanleeuwen at fcdonders.ru.nl> wrote:
>> Dear Fieldtrip experts,
>> I have noticed enhanced coherence (sensor level) in my data after
>> applying ICA during preprocessing, removing only 1 EOG component. Of course
>> the (mainly quantitatively) enhanced coherence could be due to the removal
>> of (artifact induced) noise from the data. But this increase also occured
>> when applying ICA to previously cleaned data, implying changes induced by
>> ICA somehow affect coherence.
>> One of the aims of our project is to compute coherence/connectivity
>> measures at the source level. Since connectivity measures are often
>> difficult to interpret as they are, I would like to ask whether anyone has
>> experience with connectivity analyses after preprocessing that involved
>> ICA. Are people aware of possible influences of ICA on connectivity
>> measures and is there a way to deal with this? Or would it be advisable NOT
>> to use ICA when later looking at coherence/connectivity at the source level?
>> We initially aim to compare across conditions (data that have been
>> preprocessed together and from which the same ICA component has been
>> removed). But we also have different experimental groups for which we would
>> like to qualitatively compare active networks during our task.
>> Thank you in advance for any input, it is highly appreciated.
>> Best wishes,
>> Tessa
>> ---
>> Tessa van Leeuwen, PhD
>> postdoctoral researcher
>> Department of Neurophysiology
>> Max Planck Institute for Brain Research
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>> fieldtrip at donders.ru.nl
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