You may want to consider running ICA on the 1 Hz high-pass data, but then obtaining the activation time series from the 0.1 Hz high-pass data.<br>act_pt1Hz = unmix_1Hz * data_pt1Hz (where pt1Hz refers to the 0.1 Hz high-passed, and unmix_1Hz=wts_1Hz*sph_1Hz is the unmixing matrix for the 1Hz high-pass).<br>
<br>Rey<br><br><div class="gmail_quote">On Thu, Oct 6, 2011 at 8:25 PM, Steve Luck <span dir="ltr"><<a href="mailto:sjluck@ucdavis.edu">sjluck@ucdavis.edu</a>></span> wrote:<br><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex;">
<div style="word-wrap:break-word"><div>Jason and Sara-</div><div><br></div><div>A 1-Hz high-pass cutoff is very likely to dramatically reduce the amplitude of late components like P3 and N400. To see an example of this, take a look at Figure 7 in Kappenman & Luck (2010, Psychophysiology), which shows the effects of various high-pass cutoffs on P3 amplitude. Not only does a 1-Hz cutoff reduce peak amplitude by over 50%, it also creates a spurious negative-going peak at the beginning of the waveform.</div>
<div><br></div><div>I like David Groppe's suggestion of using epoched data with a fairly long epoch length and doing baseline correction as a type of high-pass filter.</div><div><br></div><div>Steve</div><div><br><blockquote type="cite">
<div style="margin-top:0px;margin-right:0px;margin-bottom:0px;margin-left:0px"><span style="font-family:'Helvetica';font-size:medium"><b>From: </b></span><span style="font-family:'Helvetica';font-size:medium">Jason Palmer <<a href="mailto:japalmer29@gmail.com" target="_blank">japalmer29@gmail.com</a>><br>
</span></div><div style="margin-top:0px;margin-right:0px;margin-bottom:0px;margin-left:0px"><span style="font-family:'Helvetica';font-size:medium"><b>Date: </b></span><span style="font-family:'Helvetica';font-size:medium">October 5, 2011 11:56:57 AM PDT<br>
</span></div><div style="margin-top:0px;margin-right:0px;margin-bottom:0px;margin-left:0px"><span style="font-family:'Helvetica';font-size:medium"><b>To: </b></span><span style="font-family:'Helvetica';font-size:medium">'Sara Graziadio' <<a href="mailto:sara.graziadio@newcastle.ac.uk" target="_blank">sara.graziadio@newcastle.ac.uk</a>>, <<a href="mailto:eeglablist@sccn.ucsd.edu" target="_blank">eeglablist@sccn.ucsd.edu</a>><br>
</span></div><div class="im"><div style="margin-top:0px;margin-right:0px;margin-bottom:0px;margin-left:0px"><span style="font-family:'Helvetica';font-size:medium"><b>Subject: </b></span><span style="font-family:'Helvetica';font-size:medium"><b>Re: [Eeglablist] filters, ICA and erp</b><br>
</span></div></div><div style="margin-top:0px;margin-right:0px;margin-bottom:0px;margin-left:0px"><span style="font-family:'Helvetica';font-size:medium"><b>Reply-To: </b></span><span style="font-family:'Helvetica';font-size:medium"><<a href="mailto:japalmer@ucsd.edu" target="_blank">japalmer@ucsd.edu</a>><br>
</span></div><div class="im"><br><br>Hi Sara,<br><br>In my experience, using a sharp 1Hz high pass filter is best for ICA, and<br>doesn't significantly reduce ERP amplitude--the ERPs I know of are at least<br>2 Hz, so the 1Hz high pass shouldn't be a problem. The main issue is to<br>
eliminate slow drifts in the data which make the mean non-stationary.<br><br>If you want to look at low frequencies specifically, you might do low pass<br>filtering, or band pass between 0.1Hz and say 30 Hz, to try to remove high<br>
frequency sources, leaving only the low frequency sources, but I doubt this<br>would improve ERP results over a ! Hz high-pass filter.<br><br>Average reference is also fine if you are doing ICA after. Spreading muscle<br>
artifacts etc. to other channels is not a problem since ICA will remove the<br>muscle activity etc. and put it in a single source (usually).<br><br>After you do average reference, the data rank goes down by 1, so if you have<br>
94 channels avg referenced, ICA should give you back 93 components/sources.<br><br>Hope this is helpful.<br><br>Jason<br><br></div><div class="im">-----Original Message-----<br>From: <a href="mailto:eeglablist-bounces@sccn.ucsd.edu" target="_blank">eeglablist-bounces@sccn.ucsd.edu</a><br>
[mailto:<a href="mailto:eeglablist-bounces@sccn.ucsd.edu" target="_blank">eeglablist-bounces@sccn.ucsd.edu</a>] On Behalf Of Sara Graziadio<br></div><div class="im">Sent: Wednesday, October 05, 2011 7:46 AM<br>To: <a href="mailto:eeglablist@sccn.ucsd.edu" target="_blank">eeglablist@sccn.ucsd.edu</a><br>
</div>Subject: [Eeglablist] filters, ICA and erp<br><br>Hello,<div class="im"><br>I would like just a suggestion about some data cleaning/analysis I am doing.<br>I am doing an ERP analysis and I want to clean my data first with the ICA.<br>
In theory, though, I should not use an high-pass cutoff higher than 0.1 Hz<br>to not reduce the erp amplitude. On the other side the ICA does not work<br>well if the high-pass cutoff is lower than 0.5 Hz...what is then the best<br>
method to apply? Has anybody tested how robust the ica is with a 0.1Hz<br>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<br>
is quite a large spread of noise on all the electrodes with this method<br>(muscular artefacts for example from the temporal electrodes). But actually<br>almost all the papers are using the average reference so I was surprised, am<br>
I the only one having this problem of noise? Would not be better just to<br>keep the Fz reference and then perhaps to average the erps for every<br>different cortical 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><br><br><br>_______________________________________________<br>Eeglablist page: <a href="http://sccn.ucsd.edu/eeglab/eeglabmail.html" target="_blank">http://sccn.ucsd.edu/eeglab/eeglabmail.html</a><br>
</div><div class="im">To unsubscribe, send an empty email to <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><br><br><br></div><div style="margin-top:0px;margin-right:0px;margin-bottom:0px;margin-left:0px"><span style="font-family:'Helvetica';font-size:medium"><b>From: </b></span><span style="font-family:'Helvetica';font-size:medium">Sara Graziadio <<a href="mailto:sara.graziadio@newcastle.ac.uk" target="_blank">sara.graziadio@newcastle.ac.uk</a>><br>
</span></div><div style="margin-top:0px;margin-right:0px;margin-bottom:0px;margin-left:0px"><span style="font-family:'Helvetica';font-size:medium"><b>Date: </b></span><span style="font-family:'Helvetica';font-size:medium">October 6, 2011 2:50:32 AM PDT<br>
</span></div><div style="margin-top:0px;margin-right:0px;margin-bottom:0px;margin-left:0px"><span style="font-family:'Helvetica';font-size:medium"><b>To: </b></span><span style="font-family:'Helvetica';font-size:medium">'David Groppe' <<a href="mailto:david.m.groppe@gmail.com" target="_blank">david.m.groppe@gmail.com</a>>, "'<a href="mailto:japalmer29@gmail.com" target="_blank">japalmer29@gmail.com</a>'" <<a href="mailto:japalmer29@gmail.com" target="_blank">japalmer29@gmail.com</a>><br>
</span></div><div style="margin-top:0px;margin-right:0px;margin-bottom:0px;margin-left:0px"><span style="font-family:'Helvetica';font-size:medium"><b>Cc: </b></span><span style="font-family:'Helvetica';font-size:medium">"<a href="mailto:eeglablist@sccn.ucsd.edu" target="_blank">eeglablist@sccn.ucsd.edu</a>" <<a href="mailto:eeglablist@sccn.ucsd.edu" target="_blank">eeglablist@sccn.ucsd.edu</a>><br>
</span></div><div><div></div><div class="h5"><div style="margin-top:0px;margin-right:0px;margin-bottom:0px;margin-left:0px"><span style="font-family:'Helvetica';font-size:medium"><b>Subject: </b></span><span style="font-family:'Helvetica';font-size:medium"><b>Re: [Eeglablist] filters, ICA and erp</b><br>
</span></div><br><br>Hello,<br>Thanks for your suggestion. <br><br>As I was planning to do also a PSD analysis on the data I guess that to remove the mean is not the best method if it works as a non-selective high pass filter, am I right?<br>
<br>I am applying the PCA before applying the ICA to reduce the number of components. How the data rank would be modified in this case?<br>I have to admit that it never happened to me that the muscle artefact is put in a single source with the ICA. Usually it spreads on half of the components, is this only my experience? <br>
<br>Thanks again<br><br>Best wishes<br><br>Sara<br><br><br><blockquote type="cite">-----Original Message-----<br></blockquote><blockquote type="cite">From: David Groppe [mailto:<a href="mailto:david.m.groppe@gmail.com" target="_blank">david.m.groppe@gmail.com</a>]<br>
</blockquote><blockquote type="cite">Sent: 05 October 2011 23:10<br></blockquote><blockquote type="cite">To: Sara Graziadio<br></blockquote><blockquote type="cite">Cc: <a href="mailto:eeglablist@sccn.ucsd.edu" target="_blank">eeglablist@sccn.ucsd.edu</a><br>
</blockquote><blockquote type="cite">Subject: Re: [Eeglablist] filters, ICA and erp<br></blockquote><blockquote type="cite"><br></blockquote><blockquote type="cite">Hi Sara,<br></blockquote><blockquote type="cite"> I found that a good way to improve the performance of ICA for ERP<br>
</blockquote><blockquote type="cite">analysis is to<br></blockquote><blockquote type="cite">1) Epoch your data into one or two second chunks time locked to the<br></blockquote><blockquote type="cite">event of interest<br>
</blockquote><blockquote type="cite">2) Remove the mean of each epoch at each channel<br></blockquote><blockquote type="cite">3) Run ICA to remove artifacts<br></blockquote><blockquote type="cite">4) Use a standard pre-event time window to baseline your data<br>
</blockquote><blockquote type="cite">5) Compute your ERPs<br></blockquote><blockquote type="cite"><br></blockquote><blockquote type="cite">Removing the mean of each epoch acts as a crude high-pass filter.<br></blockquote>
<blockquote type="cite">It's not nearly as selective as a "true" high pass filter but it<br></blockquote><blockquote type="cite">doesn't distort the ERP waveforms as much either. Moreover we've<br></blockquote>
<blockquote type="cite">found that the procedure described above massively improves the<br></blockquote><blockquote type="cite">reliability of ICA when compared to standard ERP prestimulus<br></blockquote><blockquote type="cite">
baselines:<br></blockquote><blockquote type="cite"><br></blockquote><blockquote type="cite">Groppe, D.M., Makeig, S., & Kutas, M. (2009) Identifying reliable<br></blockquote><blockquote type="cite">independent components via split-half comparisons. NeuroImage, 45<br>
</blockquote><blockquote type="cite">pp.1199-1211.<br></blockquote><blockquote type="cite"><br></blockquote><blockquote type="cite">Hope this helps,<br></blockquote><blockquote type="cite"> -David<br></blockquote><blockquote type="cite">
<br></blockquote><blockquote type="cite"><br></blockquote><blockquote type="cite"><br></blockquote><blockquote type="cite">On Wed, Oct 5, 2011 at 10:46 AM, Sara Graziadio<br></blockquote><blockquote type="cite"><<a href="mailto:sara.graziadio@newcastle.ac.uk" target="_blank">sara.graziadio@newcastle.ac.uk</a>> wrote:<br>
</blockquote><blockquote type="cite"><blockquote type="cite">Hello,<br></blockquote></blockquote><blockquote type="cite"><blockquote type="cite">I would like just a suggestion about some data cleaning/analysis I am doing. I<br>
</blockquote></blockquote><blockquote type="cite">am doing an ERP analysis and I want to clean my data first with the ICA. In<br></blockquote><blockquote type="cite">theory, though, I should not use an high-pass cutoff higher than 0.1 Hz to not<br>
</blockquote><blockquote type="cite">reduce the erp amplitude. On the other side the ICA does not work well if the<br></blockquote><blockquote type="cite">high-pass cutoff is lower than 0.5 Hz...what is then the best method to apply?<br>
</blockquote><blockquote type="cite">Has anybody tested how robust the ica is with a 0.1Hz filter?<br></blockquote><blockquote type="cite"><blockquote type="cite">I have also another question: I am doing the analysis on 94 electrodes<br>
</blockquote></blockquote><blockquote type="cite">referenced to Fz. I planned to average reference the data but actually there is<br></blockquote><blockquote type="cite">quite a large spread of noise on all the electrodes with this method (muscular<br>
</blockquote><blockquote type="cite">artefacts for example from the temporal electrodes). But actually almost all<br></blockquote><blockquote type="cite">the papers are using the average reference so I was surprised, am I the only<br>
</blockquote><blockquote type="cite">one having this problem of noise? Would not be better just to keep the Fz<br></blockquote><blockquote type="cite">reference and then perhaps to average the erps for every different cortical<br>
</blockquote><blockquote type="cite">area and do the analysis on these averaged erps?<br></blockquote><blockquote type="cite"><blockquote type="cite"><br></blockquote></blockquote><blockquote type="cite"><blockquote type="cite">
Thank you very much<br></blockquote></blockquote><blockquote type="cite"><blockquote type="cite"><br></blockquote></blockquote><blockquote type="cite"><blockquote type="cite">Best wishes<br></blockquote></blockquote><blockquote type="cite">
<blockquote type="cite"><br></blockquote></blockquote><blockquote type="cite"><blockquote type="cite">Sara Graziadio<br></blockquote></blockquote><blockquote type="cite"><blockquote type="cite">Research Associate<br></blockquote>
</blockquote><blockquote type="cite"><blockquote type="cite">Newcastle University<br></blockquote></blockquote><blockquote type="cite"><blockquote type="cite"><br></blockquote></blockquote><blockquote type="cite"><blockquote type="cite">
<br></blockquote></blockquote><blockquote type="cite"><blockquote type="cite"><br></blockquote></blockquote><blockquote type="cite"><blockquote type="cite">_______________________________________________<br></blockquote></blockquote>
<blockquote type="cite"><blockquote type="cite">Eeglablist page: <a href="http://sccn.ucsd.edu/eeglab/eeglabmail.html" target="_blank">http://sccn.ucsd.edu/eeglab/eeglabmail.html</a><br></blockquote></blockquote><blockquote type="cite">
<blockquote type="cite">To unsubscribe, send an empty email to eeglablist-<br></blockquote></blockquote><blockquote type="cite"><a href="mailto:unsubscribe@sccn.ucsd.edu" target="_blank">unsubscribe@sccn.ucsd.edu</a><br></blockquote>
<blockquote type="cite"><blockquote type="cite">For digest mode, send an email with the subject "set digest mime" to<br></blockquote></blockquote><blockquote type="cite"><a href="mailto:eeglablist-request@sccn.ucsd.edu" target="_blank">eeglablist-request@sccn.ucsd.edu</a><br>
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--<br></blockquote><blockquote type="cite">David Groppe, Ph.D.<br></blockquote><blockquote type="cite">Postdoctoral Researcher<br></blockquote><blockquote type="cite">North Shore LIJ Health System<br></blockquote><blockquote type="cite">
New Hyde Park, New York<br></blockquote><blockquote type="cite"><a href="http://www.cogsci.ucsd.edu/%7Edgroppe/" target="_blank">http://www.cogsci.ucsd.edu/~dgroppe/</a><br></blockquote><br><br><br><br>_______________________________________________<br>
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