Dear Tessa, <div><br></div><div>ok this is more clear now.</div><div>ICA is a wonderful tool which can easily become your worst enemy in data analyzing.</div><div><br></div><div>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. </div>
<div>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. </div><div>This data removal could potentially decrease your data variability therefore increase your coherence. </div>
<div>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.</div><div><br></div><div>Hope this helps,</div><div>
<br></div><div>Rodolphe</div><div><br></div><div><div><br><div class="gmail_quote">On Tue, Mar 27, 2012 at 4:34 PM, Tessa van Leeuwen <span dir="ltr"><<a href="mailto:tessa.vanleeuwen@fcdonders.ru.nl">tessa.vanleeuwen@fcdonders.ru.nl</a>></span> wrote:<br>
<blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div><div style="font-size:12pt;font-family:Times New Roman">Dear Rodolphe,<br><br>Thank you for your response, I tried to clarify below.<br>
<blockquote style="border-left:2px solid rgb(16,16,255);margin-left:5px;padding-left:5px"><br><div>1) After your first "cleaning", do you test coherence on particular components or your entire data minus the "EOG" component?</div>
</blockquote>I tested the coherence on the entire dataset after removal of the EOG component. <br><br><blockquote style="border-left:2px solid rgb(16,16,255);margin-left:5px;padding-left:5px"><div></div>
<div></div><div>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?</div>
</blockquote>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. <br>
<br>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.<br>
<br>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?<br>
<br>Best wishes,<br>Tessa<br><br><blockquote style="border-left:2px solid rgb(16,16,255);margin-left:5px;padding-left:5px"><div></div>
<div><br></div><div>Rodolphe</div><div><br><div class="gmail_quote">On Tue, Mar 27, 2012 at 11:27 AM, Tessa van Leeuwen <span dir="ltr"><<a href="mailto:tessa.vanleeuwen@fcdonders.ru.nl" target="_blank">tessa.vanleeuwen@fcdonders.ru.nl</a>></span> wrote:<br>
<blockquote class="gmail_quote" style="margin:0pt 0pt 0pt 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex">Dear Fieldtrip experts,<br>
<br>
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.<br>
<br>
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?<br>
<br>
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.<br>
<br>
Thank you in advance for any input, it is highly appreciated.<br>
<br>
Best wishes,<br>
Tessa<br>
<br>
<br>
---<br>
<br>
Tessa van Leeuwen, PhD<br>
postdoctoral researcher<br>
<br>
Department of Neurophysiology<br>
Max Planck Institute for Brain Research<br>
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