<html><head></head><body style="word-wrap: break-word; -webkit-nbsp-mode: space; -webkit-line-break: after-white-space; "><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; color:rgba(127, 127, 127, 1.0);"><b>From: </b></span><span style="font-family:'Helvetica'; font-size:medium;">Jason Palmer <<a href="mailto:japalmer29@gmail.com">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; color:rgba(127, 127, 127, 1.0);"><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; color:rgba(127, 127, 127, 1.0);"><b>To: </b></span><span style="font-family:'Helvetica'; font-size:medium;">'Sara Graziadio' <<a href="mailto:sara.graziadio@newcastle.ac.uk">sara.graziadio@newcastle.ac.uk</a>>, <<a href="mailto:eeglablist@sccn.ucsd.edu">eeglablist@sccn.ucsd.edu</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; color:rgba(127, 127, 127, 1.0);"><b>Subject: </b></span><span style="font-family:'Helvetica'; font-size:medium;"><b>Re: [Eeglablist] filters, ICA and erp</b><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; color:rgba(127, 127, 127, 1.0);"><b>Reply-To: </b></span><span style="font-family:'Helvetica'; font-size:medium;"><<a href="mailto:japalmer@ucsd.edu">japalmer@ucsd.edu</a>><br></span></div><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>-----Original Message-----<br>From: <a href="mailto:eeglablist-bounces@sccn.ucsd.edu">eeglablist-bounces@sccn.ucsd.edu</a><br>[mailto:eeglablist-bounces@sccn.ucsd.edu] On Behalf Of Sara Graziadio<br>Sent: Wednesday, October 05, 2011 7:46 AM<br>To: <a href="mailto:eeglablist@sccn.ucsd.edu">eeglablist@sccn.ucsd.edu</a><br>Subject: [Eeglablist] filters, ICA and erp<br><br>Hello,<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">http://sccn.ucsd.edu/eeglab/eeglabmail.html</a><br>To unsubscribe, send an empty email to <a href="mailto:eeglablist-unsubscribe@sccn.ucsd.edu">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">eeglablist-request@sccn.ucsd.edu</a><br><br><br><br><br><br><div style="margin-top: 0px; margin-right: 0px; margin-bottom: 0px; margin-left: 0px;"><span style="font-family:'Helvetica'; font-size:medium; color:rgba(127, 127, 127, 1.0);"><b>From: </b></span><span style="font-family:'Helvetica'; font-size:medium;">Sara Graziadio <sara.graziadio@newcastle.ac.uk><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; color:rgba(127, 127, 127, 1.0);"><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; color:rgba(127, 127, 127, 1.0);"><b>To: </b></span><span style="font-family:'Helvetica'; font-size:medium;">'David Groppe' <david.m.groppe@gmail.com>, "'japalmer29@gmail.com'" <japalmer29@gmail.com><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; color:rgba(127, 127, 127, 1.0);"><b>Cc: </b></span><span style="font-family:'Helvetica'; font-size:medium;">"eeglablist@sccn.ucsd.edu" <eeglablist@sccn.ucsd.edu><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; color:rgba(127, 127, 127, 1.0);"><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:david.m.groppe@gmail.com]<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: eeglablist@sccn.ucsd.edu<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"><sara.graziadio@newcastle.ac.uk> 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: http://sccn.ucsd.edu/eeglab/eeglabmail.html<br></blockquote></blockquote><blockquote type="cite"><blockquote type="cite">To unsubscribe, send an empty email to eeglablist-<br></blockquote></blockquote><blockquote type="cite">unsubscribe@sccn.ucsd.edu<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">eeglablist-request@sccn.ucsd.edu<br></blockquote><blockquote type="cite"><blockquote type="cite"><br></blockquote></blockquote><blockquote type="cite"><br></blockquote><blockquote type="cite"><br></blockquote><blockquote type="cite"><br></blockquote><blockquote type="cite">--<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">http://www.cogsci.ucsd.edu/~dgroppe/<br></blockquote><br><br><br><br>_______________________________________________<br>eeglablist mailing list eeglablist@sccn.ucsd.edu<br>Eeglablist page: http://www.sccn.ucsd.edu/eeglab/eeglabmail.html<br>To unsubscribe, send an empty email to eeglablist-unsub@sccn.ucsd.edu<br>To switch to non-digest mode, send an empty email to eeglablist-nodigest@sccn.ucsd.edu</blockquote></div><br><div>
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