Dear Makeig,<div><br></div><div>I still feel quite confused about your post.Please forgive me for my ignorance as a newbie in ICA.</div><div>My questions can be outlined as follows:</div><div>1)Why should we first remove major artifacts and then filter the data?Can we invert these two procedures?</div>
<div>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.</div>
<div>3)Why should baseline removal be done after ICA?I really don't understand the reason under this practice.</div><div>4)Why longer epochs are more enjoyable for ICA?</div><div>5)Are there some indicators which can used to identify if the result of ICA are good enough to make inference?</div>
<div>Sorry for the long question list and wish for your reply.</div><div><br><div class="gmail_quote">2012/5/13 Matthew Stief <span dir="ltr"><<a href="mailto:ms2272@cornell.edu" target="_blank">ms2272@cornell.edu</a>></span><br>
<blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex">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<div><div class="h5"><br><br><br><div class="gmail_quote">On Fri, May 11, 2012 at 11:09 PM, Scott Makeig <span dir="ltr"><<a href="mailto:smakeig@gmail.com" target="_blank">smakeig@gmail.com</a>></span> wrote:<br>
<blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex">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.<div>
<br></div><div>Scott<br><br><div class="gmail_quote">On Fri, May 11, 2012 at 12:31 PM, Makoto Miyakoshi <span dir="ltr"><<a href="mailto:mmiyakoshi@ucsd.edu" target="_blank">mmiyakoshi@ucsd.edu</a>></span> wrote:<br>
<blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex">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>>:<br>
<div><div>>> > 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>
>> ><br>
</div></div>>> > _______________________________________________<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><font 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><span><font 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>
</font></span></font></span></blockquote></div><span><font color="#888888"><br><br clear="all"><div><br></div>-- <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><br>
</font></span></div>
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