<html><head></head><body style="word-wrap: break-word; -webkit-nbsp-mode: space; -webkit-line-break: after-white-space; ">Hi All,<div><br></div><div>regarding running ICA on the high pass filtered data at 1 Hz, the reason this is performed is that ICA tends to focus on the data that express the most power (in case of EEG low frequencies). Since very low frequencies can represent skin conductance changes that might confuse ICA demixing, it is better to remove them. However, it might be interesting to try to run ICA on the derivative of the data. Discrete derivation using the diff function is a linear operation, and it makes the spectrum flat where the influence of all frequencies is almost made equal. ICA on this data would not focus on low frequencies. It is an interesting research project that one ought to pursue.</div><div><br></div><div>Also, I just wanted to comment on the CORRMAP plugin that Stefan Debener and Filipa Viola Campo developed. We have worked on integrating further this plugin with EEGLAB STUDY (because it proved very efficient), and we believe it is now fully integrated with many new features such as component preselection etc... It is distributed by default with EEGLAB.</div><div><br></div><div>Thanks,</div><div><br></div><div>Arno</div><div><br><div><div>On Oct 13, 2011, at 11:59 PM, Stefan Debener wrote:</div><br class="Apple-interchange-newline"><blockquote type="cite">
<meta http-equiv="Content-Type" content="text/html; charset=iso-8859-1">
<div bgcolor="#FFFFFF" text="#000000">
Hi Steve,<br>
<br>
Yes, ICA training on 1 Hz hp filtered data and applying the weights
to the unfiltered data was my suggestions (and has been used in
Scott's lab for many years, I believe). After playing a lot with
preprocessing we found this to work best as well. The ML code below
shows a typical preprocessing example implementing this. Note that,
due to the HP filter, there is no real benefit in baseline
correction of the dummy epochs. The rationale for this code, as well
as a more general introduction into the analysis of multi-channel
EEG data with ICA (inlcuding other assumptions and relazted
preprocessing issues), is descriped in our EEG-fMRI book in chapter
3.1.:<br>
<br>
Debener et al., (2010). Using ICA for the analysis of mutli-channel
EEG data. In: Ullsperger & Debener (Eds.). Simultaneous EEG and
fMRI: recording, analysis, and application (pp. 121-133). Oxford
University Press. <br>
<br>
Feel free to cite this chapter when you use the code below.<br>
<br>
Thanks,<br>
Stefan<br>
<br>
Ps: Sara, on the issue of filtering and ERPs, you should test the
effect of the filter, by plotting an overlay of the filtered and
unfiltered ERPs. This, of course, should be done on single subject
ERPs, as the grand average sometimes acts as a(nother) filter. <br>
<br>
<br>
<br>
-----------------------------------------------------------------<br>
% miu_inside_ana03.m<br>
% sd<br>
%<br>
% ica on cnt data<br>
% ----------------------------------------------------------------<br>
<br>
MAINPATH = '/data/sd_miu/pilot_inside/';<br>
PATHIN = [MAINPATH, 'data/ana02/'];<br>
PATHOUT = [MAINPATH, 'data/ana03/'];<br>
SUBJ = {'4102', '4103new', '4105', '4106'}; <br>
HPF = 1; % high pass filter for ica training only<br>
DIM = 63; % ica dimensionality<br>
EPOCHDUR = 1; % epoch duration in seconds for pruning<br>
THRES = 4; % SD cut-off for rejection <br>
<br>
for s = 1:length(SUBJ)<br>
[ALLEEG EEG CURRENTSET ALLCOM] = eeglab;<br>
EEG = pop_loadset('filename', [SUBJ{s}, '_bcgrem.set'], 'filepath',
PATHIN);<br>
<br>
% high pass filter<br>
EEG = pop_iirfilt(EEG, HPF, 0, [], [0]);<br>
<br>
% dummy trigger for epoching <br>
for i=1:(EEG.srate*EPOCHDUR):EEG.pnts<br>
EEG.event(end+1).type = 'S999';<br>
EEG.event(end).latency = i;<br>
end<br>
EEG = eeg_checkset(EEG);<br>
%eeg_eventtypes(EEG)<br>
<br>
% epoching and pruning<br>
EEG = pop_epoch(EEG, {'S999'}, [0 EPOCHDUR], 'newname', 'tmp
epochs', 'epochinfo', 'yes');<br>
EEG = pop_jointprob(EEG,1,[1:63] ,THRES, THRES,0,0);<br>
EEG = pop_rejkurt(EEG,1,[1:63] ,THRES, THRES,0,0);<br>
EEG = eeg_rejsuperpose(EEG, 1, 1, 1, 1, 1, 1, 1, 1);<br>
EEG = pop_rejepoch(EEG, EEG.reject.rejglobal ,0);<br>
<br>
% ICA<br>
EEG = pop_runica(EEG, 'icatype','runica','dataset',1,'options',
{'extended' 1, 'pca' DIM },'chanind',[1:63]);<br>
<br>
TMP.icawinv = EEG.icawinv;<br>
TMP.icasphere = EEG.icasphere;<br>
TMP.icaweights = EEG.icaweights;<br>
TMP.icachansind = EEG.icachansind;<br>
<br>
% apply to cnt dataset<br>
clear EEG;<br>
EEG = pop_loadset('filename', [SUBJ{s}, '_bcgrem.set'], 'filepath',
PATHIN);<br>
EEG.icawinv = TMP.icawinv;<br>
EEG.icasphere = TMP.icasphere;<br>
EEG.icaweights = TMP.icaweights;<br>
EEG.icachansind = TMP.icachansind;<br>
clear TMP;<br>
EEG = pop_saveset(EEG, 'filename',[SUBJ{s}, '_cnt.set'],
'filepath',PATHOUT);<br>
[ALLEEG, EEG, CURRENTSET] = eeg_store(ALLEEG, EEG, 0);<br>
<br>
end<br>
eeglab redraw;<br>
<br>
----------------------------------------<br>
<br>
<br>
Am 10/13/11 6:09 PM, schrieb Steve Luck:
<blockquote cite="mid:800D0ADC-274D-401F-8705-D1469236B295@ucdavis.edu" type="cite">
Hi Sara. Unless you care about frequencies per se, epoching and
baseline-correcting the data won't be a problem. From a
time-domain perspective, this won't change anything.
<div><br>
</div>
<div>BTW, someone else suggested using the 1-Hz high-pass cutoff,
performing ICA, and then applying the component coefficients to
the unfiltered data. That sounds like a great suggestion,
although I don't know if there is a technical reason why it
wouldn't work. Does anyone out there know if there would be a
problem with this?</div>
<div><br>
</div>
<div>Steve</div>
<div><br>
</div>
<div>ps- The email trail on this topic has gotten out of hand, so
I deleted everything except the most recent message and your
original message.</div>
<div><br>
<div>
<div>On Oct 13, 2011, at 7:20 AM, Sara Graziadio wrote:</div>
<br class="Apple-interchange-newline">
<blockquote type="cite">
<div>Steve,<br>
actually I was refering to your book when I was writing
that the filter would deforme/reduce the erp. But
following David Groppe's suggestion would mean to reduce
activity at different frequency all across the spectrum,
wihtout exactly knowing which frequencies I am reducing,
am I right? If I want to look at the psd as well as at the
erps, would this analysis just be correct? I am always
concerned about applying data modification that I cannot
fully control..if you know what I mean...<br>
Thank you very much<br>
Best<br>
<br>
Sara<br>
</div>
</blockquote>
</div>
<div><br>
</div>
<blockquote type="cite">On Wed, Oct 5, 2011 at 10:46 AM, Sara
Graziadio<br>
<<a moz-do-not-send="true" href="mailto:sara.graziadio@newcastle.ac.uk">sara.graziadio@newcastle.ac.uk</a>>
wrote:<br>
Hello,<br>
I would like just a suggestion about some data
cleaning/analysis I am doing. I<br>
am doing an ERP analysis and I want to clean my data first
with the ICA. In<br>
theory, though, I should not use an high-pass cutoff higher
than 0.1 Hz to not<br>
reduce the erp amplitude. On the other side the ICA does not
work well if the<br>
high-pass cutoff is lower than 0.5 Hz...what is then the best
method to apply?<br>
Has anybody tested how robust the ica is with a 0.1Hz filter?<br>
I have also another question: I am doing the analysis on 94
electrodes<br>
referenced to Fz. I planned to average reference the data but
actually there is<br>
quite a large spread of noise on all the electrodes with this
method (muscular<br>
artefacts for example from the temporal electrodes). But
actually almost all<br>
the papers are using the average reference so I was surprised,
am I the only<br>
one having this problem of noise? Would not be better just to
keep the Fz<br>
reference and then perhaps to average the erps for every
different cortical<br>
area and do the analysis on these averaged erps?<br>
<br>
Thank you very much<br>
<br>
Best wishes<br>
<br>
Sara Graziadio<br>
Research Associate<br>
Newcastle University<br>
</blockquote>
<br>
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<pre class="moz-signature" cols="72">--
Prof. Dr. Stefan Debener
Neuropsychology Lab
Department of Psychology
University of Oldenburg
D-26111 Oldenburg
Germany
Office: A7 0-038
Phone: +49-441-798-4271
Fax: +49-441-798-5522
Email: <a class="moz-txt-link-abbreviated" href="mailto:stefan.debener@uni-oldenburg.de">stefan.debener@uni-oldenburg.de</a>
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