Thank you Jason for your answer,<br><br>Explanation under 4) is exactly what I needed, since I did the filtering, re-referencing, epoching (+baseline removal) of the data and after that I wanted to perform ICA in order to remove detected artifacts, after which I should do the ERP analysis.<br>
<br>The thing is that I have a lot of epochs overlapping, so I am thinking on switching of the steps "epoching" and ICA in way to <br><br>- first perform ICA on (on filtered, re-referenced) data<br>- after that I can do the epoching (+baseline removal) and ERP analysis<br>
<br>I will also try making non-overlapping epochs and performing ICA on them.<br><br>All the best, <br><br>Ida<br><br><div class="gmail_quote">On Wed, May 16, 2012 at 12:11 AM, Jason Palmer <span dir="ltr"><<a href="mailto:japalmer29@gmail.com" target="_blank">japalmer29@gmail.com</a>></span> wrote:<br>
<blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div link="blue" vlink="purple" lang="EN-US"><div><p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri","sans-serif"">Hi Yuan-fang,<u></u><u></u></span></p>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri","sans-serif"">Some notes in response to your questions:<u></u><u></u></span></p><p><u></u><span style="font-size:11.0pt;font-family:"Calibri","sans-serif""><span>1)<span style="font:7.0pt "Times New Roman""> </span></span></span><u></u><span dir="LTR"></span><span style="font-size:11.0pt;font-family:"Calibri","sans-serif"">Removing major artifacts, such a large spikes in the data, before filtering can be preferable since filtering can “spread” the artifact out over “good” data, requiring more data to be rejected after filtering. When you remove major artifacts, and “boundary” event replaces the removed data. Filtering is only applied to continuous data segments, not across boundaries.<u></u><u></u></span></p>
<p><u></u><span style="font-size:11.0pt;font-family:"Calibri","sans-serif""><span>2)<span style="font:7.0pt "Times New Roman""> </span></span></span><u></u><span dir="LTR"></span><span style="font-size:11.0pt;font-family:"Calibri","sans-serif"">The main considerations regarding epoched vs. continuous data are: a) amount of data given to ICA, and b) stationarity of data given to ICA. ICA expects the data to be stationary, i.e. the same statistical model is generating all time points. If you have enough data after epoching, then epoched data may be preferable since it will increase stationary as you mention. However, you may want to epoch on different events to produce different datasets, with the same ICA decomposition for all conditions. More data generally gives a better ICA decomposition, assuming all the data is similar statistically.<u></u><u></u></span></p>
<p><u></u><span style="font-size:11.0pt;font-family:"Calibri","sans-serif""><span>3)<span style="font:7.0pt "Times New Roman""> </span></span></span><u></u><span dir="LTR"></span><span style="font-size:11.0pt;font-family:"Calibri","sans-serif"">So the idea is to run ICA on the continuous, cleaned, filtered data, then epoch. Filtering will removed slow drifts in the data, and make the channels zero mean. There is obviously no baseline until you epoch the data. It is possible if you only care about one condition and want to run ICA on epoched data to remove the baseline first before running ICA. Results may differ slightly (particularly when using multiple ICA models) so you may want to compare results on your data. In my view, removing the baseline before running ICA on epochs is not “bad practice”, and shouldn’t affect the estimation of e.g. theta and alpha components, or higher frequency components. The main issue is whether the original (high-pass) filtering works and produces epochs with “flat” zero mean baseline periods, or whether the filtering does not work sufficiently well, and your epoch baselines are not always zero mean. You might also try “detrending” the epochs before ICA if they are particularly recalcitrant.<u></u><u></u></span></p>
<p><u></u><span style="font-size:11.0pt;font-family:"Calibri","sans-serif""><span>4)<span style="font:7.0pt "Times New Roman""> </span></span></span><u></u><span dir="LTR"></span><span style="font-size:11.0pt;font-family:"Calibri","sans-serif"">Longer epochs are preferable because they yield more data for ICA (assuming stationarity holds.) However, if you are epoching before ICA, you don’t want to give ICA overlapping epochs, since it will then have duplicates of some data skewing the statistical model. So the epoch start time should not be before the stop time of the previous epoch, and the stop time should not be after the start time of the next epoch.<u></u><u></u></span></p>
<p><u></u><span style="font-size:11.0pt;font-family:"Calibri","sans-serif""><span>5)<span style="font:7.0pt "Times New Roman""> </span></span></span><u></u><span dir="LTR"></span><span style="font-size:11.0pt;font-family:"Calibri","sans-serif"">Dipolarity, i.e. residual variance (R.V.) of dipole fit, of a component map is one indication of component quality. If the R.V. is low, then the component is physiologically localized and concisely accounted for potentially by a single patch of cortex. A good decomposition should have many dipolar components. Mutual information can be used to assess how independent a component is from other components. And the “Quality” of spectral peaks of independent component activation is also informative. E.g. theta and alpha components should have stronger theta or alpha peaks than the raw channel data, and should account for most of the theta or alpha activity in the data in one or a few components. ERP averages should also be “cleaner” and “simpler” in the independent components than in the raw channel data.<u></u><u></u></span></p>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri","sans-serif"">Best,<u></u><u></u></span></p><p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri","sans-serif"">Jason<u></u><u></u></span></p>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri","sans-serif""><u></u> <u></u></span></p><p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri","sans-serif""><u></u> <u></u></span></p>
<p class="MsoNormal"><span style="font-size:11.0pt;font-family:"Calibri","sans-serif""><u></u> <u></u></span></p><p class="MsoNormal"><b><span style="font-size:10.0pt;font-family:"Tahoma","sans-serif"">From:</span></b><span style="font-size:10.0pt;font-family:"Tahoma","sans-serif""> <a href="mailto:eeglablist-bounces@sccn.ucsd.edu" target="_blank">eeglablist-bounces@sccn.ucsd.edu</a> [mailto:<a href="mailto:eeglablist-bounces@sccn.ucsd.edu" target="_blank">eeglablist-bounces@sccn.ucsd.edu</a>] <b>On Behalf Of </b>Yuan-fang Chou<br>
<b>Sent:</b> Sunday, May 13, 2012 6:41 AM<br><b>To:</b> Matthew Stief<br><b>Cc:</b> <a href="mailto:smakeig@ucsd.edu" target="_blank">smakeig@ucsd.edu</a>; <a href="mailto:eeglablist@sccn.ucsd.edu" target="_blank">eeglablist@sccn.ucsd.edu</a>; <a href="mailto:mmiyakoshi@ucsd.edu" target="_blank">mmiyakoshi@ucsd.edu</a><br>
<b>Subject:</b> Re: [Eeglablist] epoch baseline removal experience?<u></u><u></u></span></p><div><div class="h5"><p class="MsoNormal"><u></u> <u></u></p><p class="MsoNormal">Dear Makeig,<u></u><u></u></p><div><p class="MsoNormal">
<u></u> <u></u></p></div><div><p class="MsoNormal">I still feel quite confused about your post.Please forgive me for my ignorance as a newbie in ICA.<u></u><u></u></p></div><div><p class="MsoNormal">My questions can be outlined as follows:<u></u><u></u></p>
</div><div><p class="MsoNormal">1)Why should we first remove major artifacts and then filter the data?Can we invert these two procedures?<u></u><u></u></p></div><div><p class="MsoNormal">2)ICA should be conducted in epoched data or continuous data?If both are ok,which is better?For continuous data often contains large amounts of artifacts,which happens during the interval of each trial in experiment,I think it may be better to do ICA on epoched data.<u></u><u></u></p>
</div><div><p class="MsoNormal">3)Why should baseline removal be done after ICA?I really don't understand the reason under this practice.<u></u><u></u></p></div><div><p class="MsoNormal">4)Why longer epochs are more enjoyable for ICA?<u></u><u></u></p>
</div><div><p class="MsoNormal">5)Are there some indicators which can used to identify if the result of ICA are good enough to make inference?<u></u><u></u></p></div><div><p class="MsoNormal">Sorry for the long question list and wish for your reply.<u></u><u></u></p>
</div><div><p class="MsoNormal"><u></u> <u></u></p><div><p class="MsoNormal">2012/5/13 Matthew Stief <<a href="mailto:ms2272@cornell.edu" target="_blank">ms2272@cornell.edu</a>><u></u><u></u></p><p class="MsoNormal">
Hi Scott,<br><br>Thanks for this. If you're going to baseline-zero epochs after ICA, then what's the point of baselining the whole dataset before epoching? Just to have an additional kind of high pass filter? You're saying that doing this AND a ~1Hz high-pass filter would be better for the ICA than just doing the high-pass filter, right? I thought that the advantage of doing the whole-epoch baseline (and thus also i assume this whole dataset baseline removal), was that it ameliorated problems of low frequency drift for the ICA without suffering from the attenuation of large later components caused by an aggressive high pass filter. So I was thinking of it as an alternative to high pass filtering, not an addition to it. In my current data processing strategy I've gone for not baseline removing before ICA at all, and just relying on an aggressive 2 Hz high-pass filter (all I care about is the P1), and then doing a baseline removal for epochs after the ICA. But you're saying doing this big baseline removal and a high pass produces superior results, right?<br>
<br>Also, I wasn't sure from your e-mail whether you thought the whole dataset baseline removal should occur before or after filtering. I've been doing major artifact removal after filtering because it makes bad patches easier to see, but i'd be happy to do it this way if it creates a better ICA decomposition to do this kind of total baseline removal.<br>
<br>Thank you!<br><br>-Matthew<u></u><u></u></p><div><div><p class="MsoNormal" style="margin-bottom:12.0pt"><br><br><u></u><u></u></p><div><p class="MsoNormal">On Fri, May 11, 2012 at 11:09 PM, Scott Makeig <<a href="mailto:smakeig@gmail.com" target="_blank">smakeig@gmail.com</a>> wrote:<u></u><u></u></p>
<p class="MsoNormal">Even whole-epoch baseline removal is not ideal. It is better to zero-baseline the data after major artifact-period removal but before epoching (and, typically, high-pass filtering above ~1 Hz). Only then extract epochs for ICA decomposition (IF you do not want to decompose the continuous data -- our more typical procedure). After ICA decomposition, data epochs can be individually baseline-zeroed without affecting the ICA account of them.<u></u><u></u></p>
<div><p class="MsoNormal"><u></u> <u></u></p></div><div><p class="MsoNormal" style="margin-bottom:12.0pt">Scott<u></u><u></u></p><div><p class="MsoNormal">On Fri, May 11, 2012 at 12:31 PM, Makoto Miyakoshi <<a href="mailto:mmiyakoshi@ucsd.edu" target="_blank">mmiyakoshi@ucsd.edu</a>> wrote:<u></u><u></u></p>
<p class="MsoNormal">Dear Ida and Scott,<br><br>> As I understood, the purpose of Baseline Removal is for me/us to have<br>> better insight when event in observed epoch happened, so the value around<br>> corresponding marker is expected to be zero. Right?<br>
<br>That sounds right, although I may not understand you perfectly.<br>ERP show up usually after the event (unless it is expectation-related<br>nature), so it makes sense to set the baseline period before stimulus<br>onset during which brain activity is supposed to be neutral, and<br>
whatever ERP can be compared against it.<br><br>> I have one more question regarding this - does it matter if I Remove<br>> Baseline for example (-1000ms to 0ms) if I have epoch that is longer (-4<br>> secs to 4 secs)? I read in Q&A list Arno's answer regarding similar question<br>
> where he said that ICA can be unstable if the epochs baseline is too short,<br>> so he suggests longer baselines (i.e 1 sec).<br><br>Although I don't know what Arno meant in that specific context, I<br>guess he was probably referring to the finding reported by Groppe,<br>
Makeig, and Kutas (2009). In the paper, the authors reports<br>whole-epoch baseline produced better ICA results compared to short<br>pre-stimulus baseline. Therefore, for ICA purpose, it's even better to<br>use an entire epoch for a baseline. The authors says 'It is not clear<br>
what causes this difference.' in the paper (pp.1208), though I heard<br>Scott say a brief explanation. What do you think, Scott?<br><br>Makoto<br><br><br><br>2012/5/10 ida miokovic <<a href="mailto:ida.miokovic@gmail.com" target="_blank">ida.miokovic@gmail.com</a>>:<br>
> Dear Makoto,<br>><br>> thank you for your answer, it cleared the doubts in my head regarding this<br>> =). As I understood, the purpose of Baseline Removal is for me/us to have<br>> better insight when event in observed epoch happened, so the value around<br>
> corresponding marker is expected to be zero. Right?<br>><br>> I have one more question regarding this - does it matter if I Remove<br>> Baseline for example (-1000ms to 0ms) if I have epoch that is longer (-4<br>
> secs to 4 secs)? I read in Q&A list Arno's answer regarding similar question<br>> where he said that ICA can be unstable if the epochs baseline is too short,<br>> so he suggests longer baselines (i.e 1 sec).<br>
><br>> Thanks,<br>><br>> Ida<br>><br>><br>> On Thu, May 10, 2012 at 9:45 PM, Makoto Miyakoshi <<a href="mailto:mmiyakoshi@ucsd.edu" target="_blank">mmiyakoshi@ucsd.edu</a>><br>> wrote:<br>>><br>
>> Dear Ida,<br>>><br>>> The consequence would be that you may not have near-zero potential<br>>> at/around time zero (and this time zero which should be an onset of<br>>> whatever event). Usually people want to reset their data to zero<br>
>> microvolt at/around time zero, so they subtract mean of short time<br>>> period immediately before it (for example, -200 ms to 0 ms as a<br>>> baseline period). Am I answering to your question? If not, let me<br>
>> know.<br>>><br>>> Makoto<br>>><br>>> 2012/5/10 ida miokovic <<a href="mailto:ida.miokovic@gmail.com" target="_blank">ida.miokovic@gmail.com</a>>:<u></u><u></u></p><div><div><p class="MsoNormal">
>> > Hello everyone,<br>>> ><br>>> > Since I do not have experience in eeg signal processing, I am asking you<br>>> > for<br>>> > the opinion regarding epoch baseline removal (a window for this pops up<br>
>> > after I do the data epoching). Epochs I am extracting are quite long: -4<br>>> > secs before and 4 secs after Marker of my interest.<br>>> ><br>>> > Why is following suggested in tutorial:<br>
>> ><br>>> > "Using the mean value in the pre-stimulus period (the pop_rmbase()<br>>> > default)<br>>> > is effective for many datasets, if the goal of the analysis is to define<br>
>> > transformations that occur in the data following the time-locking<br>>> > events."<br>>> ><br>>> > What are the consequences if I leave the fields in pop up window (Epoch<br>>> > Baseline Removal) empty and therefore have the whole epoch used as a<br>
>> > baseline?<br>>> ><br>>> > Thank you in advance,<br>>> ><br>>> > All the best,<br>>> ><br>>> > Ida<br>>> ><br>>> ><br>>> ><br>>> ><br>
>> ><br>>> ><u></u><u></u></p></div></div><p class="MsoNormal">>> > _______________________________________________<br>>> > Eeglablist page: <a href="http://sccn.ucsd.edu/eeglab/eeglabmail.html" target="_blank">http://sccn.ucsd.edu/eeglab/eeglabmail.html</a><br>
>> > To unsubscribe, send an empty email to<br>>> > <a href="mailto:eeglablist-unsubscribe@sccn.ucsd.edu" target="_blank">eeglablist-unsubscribe@sccn.ucsd.edu</a><br>>> > For digest mode, send an email with the subject "set digest mime" to<br>
>> > <a href="mailto:eeglablist-request@sccn.ucsd.edu" target="_blank">eeglablist-request@sccn.ucsd.edu</a><br>>><br>>><br><span style="color:#888888">>><br>>> --<br>>> Makoto Miyakoshi<br>
>> JSPS Postdoctral Fellow for Research Abroad<br>>> Swartz Center for Computational Neuroscience<br>>> Institute for Neural Computation, University of California San Diego<br>><br>><br><br><br><br>
--<br>Makoto Miyakoshi<br>JSPS Postdoctral Fellow for Research Abroad<br>Swartz Center for Computational Neuroscience<br>Institute for Neural Computation, University of California San Diego</span><u></u><u></u></p></div><p class="MsoNormal">
<span style="color:#888888"><br><br clear="all"><u></u><u></u></span></p><div><p class="MsoNormal"><span style="color:#888888"><u></u> <u></u></span></p></div><p class="MsoNormal"><span style="color:#888888">-- <br>Scott Makeig, Research Scientist and Director, Swartz Center for Computational Neuroscience, Institute for Neural Computation; Prof. of Neurosciences (Adj.), University of California San Diego, La Jolla CA 92093-0559, <a href="http://sccn.ucsd.edu/%7Escott" target="_blank">http://sccn.ucsd.edu/~scott</a></span><u></u><u></u></p>
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<br><br clear="all"><br>-- <u></u><u></u></p></div></div><p class="MsoNormal">_________________________________________________________________<br>Matthew Stief<br>Human Development | Sex & Gender Lab | Cornell University<br>
<a href="http://www.human.cornell.edu/HD/sexgender" target="_blank">http://www.human.cornell.edu/HD/sexgender</a><br><br><br>Heterosexuality isn't normal, it's just common.<br>-Dorothy Parker<br><br>_______________________________________________<br>
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<br><br clear="all"><u></u><u></u></p><div><p class="MsoNormal"><u></u> <u></u></p></div><p class="MsoNormal" style="margin-bottom:12.0pt">-- <br>Yuan-Fang Chao <br>School of Psychology<br>SouthWest University<br>Beibei,Chongqing,China<br>
<br><br><u></u><u></u></p></div></div></div></div></div><br>_______________________________________________<br>
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