<div dir="ltr"><div>Dear Helen,<br><br>> Is it fine to feed all 16 conditions into ICA?<br><br>Sure.<br><br>> Or is it more advisable to use only mutually exclusive conditions?<br><br>If you have EEG.nbchan^2 x 30 or more datapoints for each condition. However, when you separate ICA for each condition, you'll have A LOT of trouble later at the group-level analysis (or it's the same after all?) You need to use 'session'... avoid this kind of complexity if you say you are new to this field. Clean the continuous data and run ICA. For that, 1) run 1-Hz high-pass filter 2) use clean_rawdata plugin by Christian Kothe (simpler alternative is my trimOutlier... with this you can also check the whole data at a glance)<br><br>> "Your memory options for saving datasets does not correspond to the format of the datasets on disk (ignoring memory options). Saving to matlab.mat"<br><br>Wow that's the strange one. You may need to talk to Arno/Ramon. Before trying that, I'd like you to check if your EEGLAB has successfully created eeg_option() file (or something like that) Sometimes people fail to create it and have weird trouble like that, and I saw a few suspicious cases these days... there maybe a cause that creates this trouble systematically. Your code looks fine to me (without running it on Matlab).<br><br>> Third, our EEG data consists of 128 channels. We are considering reducing that number so that we don't end up with 128 ICs --- but is there any consensus on an optimal number of channels/components to aim for? Any recommended reduction method?<br><br>(number of channels)^2 x 30 when you have 30 channels, and '30' should go up exponentially when you have more channels. Jason told me for 100 ch, one million datapoints as a rule of thumb. How many datapoints do you have?<br><br>> And finally, at what point is it best to run DIPFIT? All the files consist of trials from the same subject and session - so then is it redundant to compute DIPFIT separately for each condition? <br><br>See the recommended pipeline<br><a href="http://sccn.ucsd.edu/wiki/Makoto's_preprocessing_pipeline">http://sccn.ucsd.edu/wiki/Makoto's_preprocessing_pipeline</a><br>Each DIPFIT is per ICA. This is because you are computing independent sources.<br><br>> The ultimate goal is to run our data through connectivity analysis in SIFT, but of course we first need to iron out these details in the pre-processing stage.<div class="gmail_extra"><br></div><div class="gmail_extra">If that's the case, you should not apply ordinary high-pass filter for the final analysis. Run 1-Hz high-pass only for ICA purpose. SIFT has linear regression based high-pass solution. Use it instead of FIR/IIR. Also, use CleanLine to remove 50/60 Hz line noise. Don't use standard low-pass filter; use very mild one, even for anti-aliasing purpose. Set your multivariate Granger upper limit freq range far from the nyquist. See details for recent my question to the list. Andreas Widmann is answering in detail about these issues. Read also Seth et al. (2015) JoN, and Barnett and Seth (2011) for J Neurosci Method.</div><div class="gmail_extra"><br></div><div class="gmail_extra">Makoto</div><div class="gmail_extra"><br></div><div class="gmail_extra"><br></div><div class="gmail_extra"><br><div class="gmail_quote">On Wed, Jul 1, 2015 at 12:36 PM, Helen L. Wieffering <span dir="ltr"><<a href="mailto:hwieffer@bowdoin.edu" target="_blank">hwieffer@bowdoin.edu</a>></span> wrote:<br><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left-width:1px;border-left-color:rgb(204,204,204);border-left-style:solid;padding-left:1ex">
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<div style="direction:ltr;font-family:Tahoma;color:rgb(0,0,0);font-size:10pt">Hi everyone,<br>
<br>
I am a relatively new user of EEGlab and have a few questions regarding ICA and DIPFIT. I would greatly appreciate any available help -- thanks in advance.<br>
<br>
First, our data was taken as subjects completed two separate tasks. In the interest of making detailed comparisons, we have sorted the data into 16 separate files according to condition. However, some of these conditions contain overlap among trials: e.g. Correct_Task1,
Correct_Task_2 will overlap with Correct_All_Tasks. Is it fine to feed all 16 conditions into ICA? Or is it more advisable to use only mutually exclusive conditions?<br>
<br>
Second, I've had significant trouble saving the datasets after computing ICA. Here's the code we used:<br>
<br>
% concatenate each subject's condition datasets and run ICA<br>
[ALLEEG EEG CURRENTSET] = pop_newset(ALLEEG, EEG, CURRENTSET, 'retrieve', cleandata, 'study', 0);
<br>
EEG = eeg_checkset(EEG);<br>
EEG = pop_runica(EEG, 'icatype', 'runica', 'concatcond', 'on', 'options', {'extended', 1});<br>
[ALLEEG EEG CURRENTSET] = pop_newset(ALLEEG, EEG, CURRENTSET, 'savenew', ICAfile, 'gui', 'off');
<br>
EEG = eeg_checkset(EEG);<br>
<br>
After which, Matlab outputs a message reading: <br>
"Your memory options for saving datasets does not correspond to the format of the datasets on disk (ignoring memory options). Saving to matlab.mat"<br>
<br>
This seems to occur consistently, no matter which memory options I choose. How can I make sure that ICA components are saved to the corresponding data file?<br>
So far, the only solution has been to build a study out of all 16 files and then running ICA on the study ... however, this seems to apply identical ICA components to each file, which doesn't seem right either. Any ideas on what the problem might be?<br>
<br>
Third, our EEG data consists of 128 channels. We are considering reducing that number so that we don't end up with 128 ICs --- but is there any consensus on an optimal number of channels/components to aim for? Any recommended reduction method?<br>
<br>
And finally, at what point is it best to run DIPFIT? All the files consist of trials from the same subject and session - so then is it redundant to compute DIPFIT separately for each condition?
<br>
<br>
Again, thanks for any and all help. The ultimate goal is to run our data through connectivity analysis in SIFT, but of course we first need to iron out these details in the pre-processing stage.<br>
<br>
Best,<br>
<br>
Helen Wieffering<br>
Bowdoin College<br>
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</blockquote></div><br><br clear="all"><div><br></div>-- <br><div class="gmail_signature"><div dir="ltr">Makoto Miyakoshi<br>Swartz Center for Computational Neuroscience<br>Institute for Neural Computation, University of California San Diego<br></div></div>
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