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<div style="direction:ltr; font-family:Tahoma; color:#000000; font-size:10pt">Hi, Tessa,<br>
<br>
I do not have much experience for myself with coherence, but I'm very interested in the question you raised. Here is what I've seen in R.Thatcher's tutorial on
<span style="font-weight:bold">Adulteration of Phase Relations when using Independent Components Analysis/Blind Identification and other Regression Methods to “Correct for Artifact</span><br>
<div style="margin:0pt"><font face="Calibri,sans-serif" size="2"><span style="font-size:11pt"></span></font></div>
<div style="margin:0pt"><font face="Calibri,sans-serif" size="2"><span style="font-size:11pt"><a href="http://www.appliedneuroscience.com/Tutorial%20on%20ICA%20Phase%20Adulteration.pdf" target="_blank">http://www.appliedneuroscience.com/Tutorial%20on%20ICA%20Phase%20Adulteration.pdf</a></span></font>.
<br>
</div>
<br>
"Let us consider the rejection of eye movement artifact (EOG) using the ICA method which is the mathematical basis for Blind Source analysis. The ICA method decomposes a time series into a set of globally independent time series. Mathematically ICA can
invert the equation and exactly reproduce the original time series based on the mathematical components. In this case the originally measured time or phase differences between each electrode are preserved and the cross-spectrum is unaltered.<br>
However, everything changes when the ICA or PCA or Blind Source, etc. methods are used to remove artifact by reconstructing 19 channels based on a smaller set of components, e.g., 17 or 18 channels and then deleting or omitting the EOG “components” to reconstruct
19 channels. This is because a regression process (interpolation) is used to minimize the deviation of the original 19 channel time series from the 17 or 18 “components” by which the remainder is used as weights to produce a second time series that has
altered all of the phase relations in the original time series. The regression uses the average of the entire time series and therefore “smoothes” time or phase at each time point. Averaging a time series and computing deviations (whether information min-max,
equimax, or simple linear regression) and then using the result to weight each time point by averages thereby smoothes each data point’s phase relations at each moment of time, distorting coherence, and destroys the time and phase relations present in the
original time series."<br>
<div><br>
Hope it can help...<br>
Best Regards,<br>
Olga<br>
<br>
<div class="BodyFragment"><font size="2">
<div class="PlainText">Olga Sysoeva,<br>
Research Associate, PhD<br>
Washington University School of Medicine<br>
Campus Box 8134<br>
660 South Euclid Ave<br>
Saint Louis, MO 63110-9909</div>
</font></div>
</div>
<div style="font-family:Times New Roman; color:rgb(0,0,0); font-size:16px">
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<div id="divRpF191380" style="direction:ltr"><font color="#000000" face="Tahoma" size="2"><b>From:</b> fieldtrip-bounces@donders.ru.nl [fieldtrip-bounces@donders.ru.nl] on behalf of Rodolphe Nenert [batrod@gmail.com]<br>
<b>Sent:</b> Tuesday, March 27, 2012 7:20 PM<br>
<b>To:</b> Email discussion list for the FieldTrip project<br>
<b>Subject:</b> Re: [FieldTrip] coherence/connectivity measures after applying ICA<br>
</font><br>
</div>
<div></div>
<div>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" 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">
<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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