<html><head></head><body style="word-wrap: break-word; -webkit-nbsp-mode: space; -webkit-line-break: after-white-space; ">Hi Sara. I haven't systematically looked at the effects of high-pass filters on earlier components with real data. However, there is an extensive discussion of the general issue of filter-induced distortions in chapter 5 of my book on ERP methods (An Introduction to the Event-Related Potential Technique, MIT Press). See Figure 5.10 for examples with artificial data.<div><br></div><div>Here's the issue in a nutshell: Any transient ERP will contain a broad spectrum of frequencies, so even a "high frequency" component like P1 has lots of low frequencies in it. A high-pass filter will tend to induce opposite-polarity artificial peaks at the beginning and end of the waveform, and can also impact the peak amplitude.</div><div><br></div><div>The fundamental principle is that there is an inverse relationship between precision in the time domain and precision in the frequency domain. If you use a filter to restrict the information in the frequency domain (essentially increasing precision in the frequency domain), you will necessarily spread the information out in the time domain (decrease precision in the time domain). Since the most important virtue of ERPs is their temporal resolution, and filters decrease temporal precision, filters actually reduce the very thing that is most compelling about ERPs. Thus, filters should be used sparingly.</div><div><br></div><div>Steve</div><div><br></div><div><br><div><div>On Oct 7, 2011, at 4:10 PM, Sara Jane Webb wrote:</div><br class="Apple-interchange-newline"><blockquote type="cite"><div>Hi Steve et al.,<br><br>Have you looked at amplitude attenuation when using a highpass of 1Hz on earlier signals like the P1?<br><br>Thanks,<br><br>Sara<br><br>Sara Jane Webb, PhD<br>Associate Professor of Psychiatry and Behavioral Sciences<br>Autism Research Program<br><a href="http://depts.washington.edu/pbslab/">http://depts.washington.edu/pbslab/</a><br>Box 357920; CHDD 314C; University of Washington<br>Seattle WA 98195<br>206.221.6461<br>sjwebb@u.washington.edu<br><br>Confidentiality Notice: Because email is not secure, please be aware that we cannot guarantee the confidentiality of information sent by email. If you are not the intended recipient, please notify the sender by reply email, and then destroy all copies of the message and any attachments.<br><br>On Oct 6, 2011, at 8:25 PM, Steve Luck wrote:<br><br><blockquote type="cite">Jason and Sara-<br></blockquote><blockquote type="cite"><br></blockquote><blockquote type="cite">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.<br></blockquote><blockquote type="cite"><br></blockquote><blockquote type="cite">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.<br></blockquote><blockquote type="cite"><br></blockquote><blockquote type="cite">Steve<br></blockquote><blockquote type="cite"><br></blockquote><blockquote type="cite"><blockquote type="cite">From: Jason Palmer <japalmer29@gmail.com><br></blockquote></blockquote><blockquote type="cite"><blockquote type="cite">Date: October 5, 2011 11:56:57 AM PDT<br></blockquote></blockquote><blockquote type="cite"><blockquote type="cite">To: 'Sara Graziadio' <sara.graziadio@newcastle.ac.uk>, <eeglablist@sccn.ucsd.edu><br></blockquote></blockquote><blockquote type="cite"><blockquote type="cite">Subject: Re: [Eeglablist] filters, ICA and erp<br></blockquote></blockquote><blockquote type="cite"><blockquote type="cite">Reply-To: <japalmer@ucsd.edu><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">Hi Sara,<br></blockquote></blockquote><blockquote type="cite"><blockquote type="cite"><br></blockquote></blockquote><blockquote type="cite"><blockquote type="cite">In my experience, using a sharp 1Hz high pass filter is best for ICA, and<br></blockquote></blockquote><blockquote type="cite"><blockquote type="cite">doesn't significantly reduce ERP amplitude--the ERPs I know of are at least<br></blockquote></blockquote><blockquote type="cite"><blockquote type="cite">2 Hz, so the 1Hz high pass shouldn't be a problem. The main issue is to<br></blockquote></blockquote><blockquote type="cite"><blockquote type="cite">eliminate slow drifts in the data which make the mean non-stationary.<br></blockquote></blockquote><blockquote type="cite"><blockquote type="cite"><br></blockquote></blockquote><blockquote type="cite"><blockquote type="cite">If you want to look at low frequencies specifically, you might do low pass<br></blockquote></blockquote><blockquote type="cite"><blockquote type="cite">filtering, or band pass between 0.1Hz and say 30 Hz, to try to remove high<br></blockquote></blockquote><blockquote type="cite"><blockquote type="cite">frequency sources, leaving only the low frequency sources, but I doubt this<br></blockquote></blockquote><blockquote type="cite"><blockquote type="cite">would improve ERP results over a ! Hz high-pass filter.<br></blockquote></blockquote><blockquote type="cite"><blockquote type="cite"><br></blockquote></blockquote><blockquote type="cite"><blockquote type="cite">Average reference is also fine if you are doing ICA after. Spreading muscle<br></blockquote></blockquote><blockquote type="cite"><blockquote type="cite">artifacts etc. to other channels is not a problem since ICA will remove the<br></blockquote></blockquote><blockquote type="cite"><blockquote type="cite">muscle activity etc. and put it in a single source (usually).<br></blockquote></blockquote><blockquote type="cite"><blockquote type="cite"><br></blockquote></blockquote><blockquote type="cite"><blockquote type="cite">After you do average reference, the data rank goes down by 1, so if you have<br></blockquote></blockquote><blockquote type="cite"><blockquote type="cite">94 channels avg referenced, ICA should give you back 93 components/sources.<br></blockquote></blockquote><blockquote type="cite"><blockquote type="cite"><br></blockquote></blockquote><blockquote type="cite"><blockquote type="cite">Hope this is helpful.<br></blockquote></blockquote><blockquote type="cite"><blockquote type="cite"><br></blockquote></blockquote><blockquote type="cite"><blockquote type="cite">Jason<br></blockquote></blockquote><blockquote type="cite"><blockquote type="cite"><br></blockquote></blockquote><blockquote type="cite"><blockquote type="cite">-----Original Message-----<br></blockquote></blockquote><blockquote type="cite"><blockquote type="cite">From: eeglablist-bounces@sccn.ucsd.edu<br></blockquote></blockquote><blockquote type="cite"><blockquote type="cite">[mailto:eeglablist-bounces@sccn.ucsd.edu] On Behalf Of Sara Graziadio<br></blockquote></blockquote><blockquote type="cite"><blockquote type="cite">Sent: Wednesday, October 05, 2011 7:46 AM<br></blockquote></blockquote><blockquote type="cite"><blockquote type="cite">To: eeglablist@sccn.ucsd.edu<br></blockquote></blockquote><blockquote type="cite"><blockquote type="cite">Subject: [Eeglablist] filters, ICA and erp<br></blockquote></blockquote><blockquote type="cite"><blockquote type="cite"><br></blockquote></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.<br></blockquote></blockquote><blockquote type="cite"><blockquote type="cite">I am doing an ERP analysis and I want to clean my data first with the ICA.<br></blockquote></blockquote><blockquote type="cite"><blockquote type="cite">In theory, though, I should not use an high-pass cutoff higher than 0.1 Hz<br></blockquote></blockquote><blockquote type="cite"><blockquote type="cite">to not reduce the erp amplitude. On the other side the ICA does not work<br></blockquote></blockquote><blockquote type="cite"><blockquote type="cite">well if the high-pass cutoff is lower than 0.5 Hz...what is then the best<br></blockquote></blockquote><blockquote type="cite"><blockquote type="cite">method to apply? Has anybody tested how robust the ica is with a 0.1Hz<br></blockquote></blockquote><blockquote type="cite"><blockquote type="cite">filter?<br></blockquote></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"><blockquote type="cite">referenced to Fz. I planned to average reference the data but actually there<br></blockquote></blockquote><blockquote type="cite"><blockquote type="cite">is quite a large spread of noise on all the electrodes with this method<br></blockquote></blockquote><blockquote type="cite"><blockquote type="cite">(muscular artefacts for example from the temporal electrodes). But actually<br></blockquote></blockquote><blockquote type="cite"><blockquote type="cite">almost all the papers are using the average reference so I was surprised, am<br></blockquote></blockquote><blockquote type="cite"><blockquote type="cite">I the only one having this problem of noise? Would not be better just to<br></blockquote></blockquote><blockquote type="cite"><blockquote type="cite">keep the Fz reference and then perhaps to average the erps for every<br></blockquote></blockquote><blockquote type="cite"><blockquote type="cite">different cortical area and do the analysis on these averaged erps?<br></blockquote></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-unsubscribe@sccn.ucsd.edu<br></blockquote></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"><blockquote type="cite">eeglablist-request@sccn.ucsd.edu<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"><br></blockquote></blockquote><blockquote type="cite"><blockquote type="cite">From: Sara Graziadio <sara.graziadio@newcastle.ac.uk><br></blockquote></blockquote><blockquote type="cite"><blockquote type="cite">Date: October 6, 2011 2:50:32 AM PDT<br></blockquote></blockquote><blockquote type="cite"><blockquote type="cite">To: 'David Groppe' <david.m.groppe@gmail.com>, "'japalmer29@gmail.com'" <japalmer29@gmail.com><br></blockquote></blockquote><blockquote type="cite"><blockquote type="cite">Cc: "eeglablist@sccn.ucsd.edu" <eeglablist@sccn.ucsd.edu><br></blockquote></blockquote><blockquote type="cite"><blockquote type="cite">Subject: Re: [Eeglablist] filters, ICA and erp<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">Hello,<br></blockquote></blockquote><blockquote type="cite"><blockquote type="cite">Thanks for your suggestion.<br></blockquote></blockquote><blockquote type="cite"><blockquote type="cite"><br></blockquote></blockquote><blockquote type="cite"><blockquote type="cite">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></blockquote></blockquote><blockquote type="cite"><blockquote type="cite"><br></blockquote></blockquote><blockquote type="cite"><blockquote type="cite">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></blockquote></blockquote><blockquote type="cite"><blockquote type="cite">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></blockquote></blockquote><blockquote type="cite"><blockquote type="cite"><br></blockquote></blockquote><blockquote type="cite"><blockquote type="cite">Thanks again<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<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"><blockquote type="cite">-----Original Message-----<br></blockquote></blockquote></blockquote><blockquote type="cite"><blockquote type="cite"><blockquote type="cite">From: David Groppe [mailto:david.m.groppe@gmail.com]<br></blockquote></blockquote></blockquote><blockquote type="cite"><blockquote type="cite"><blockquote type="cite">Sent: 05 October 2011 23:10<br></blockquote></blockquote></blockquote><blockquote type="cite"><blockquote type="cite"><blockquote type="cite">To: Sara Graziadio<br></blockquote></blockquote></blockquote><blockquote type="cite"><blockquote type="cite"><blockquote type="cite">Cc: eeglablist@sccn.ucsd.edu<br></blockquote></blockquote></blockquote><blockquote type="cite"><blockquote type="cite"><blockquote type="cite">Subject: Re: [Eeglablist] filters, ICA and erp<br></blockquote></blockquote></blockquote><blockquote type="cite"><blockquote type="cite"><blockquote type="cite"><br></blockquote></blockquote></blockquote><blockquote type="cite"><blockquote type="cite"><blockquote type="cite">Hi Sara,<br></blockquote></blockquote></blockquote><blockquote type="cite"><blockquote type="cite"><blockquote type="cite"> I found that a good way to improve the performance of ICA for ERP<br></blockquote></blockquote></blockquote><blockquote type="cite"><blockquote type="cite"><blockquote type="cite">analysis is to<br></blockquote></blockquote></blockquote><blockquote type="cite"><blockquote type="cite"><blockquote type="cite">1) Epoch your data into one or two second chunks time locked to the<br></blockquote></blockquote></blockquote><blockquote type="cite"><blockquote type="cite"><blockquote type="cite">event of interest<br></blockquote></blockquote></blockquote><blockquote type="cite"><blockquote type="cite"><blockquote type="cite">2) Remove the mean of each epoch at each channel<br></blockquote></blockquote></blockquote><blockquote type="cite"><blockquote type="cite"><blockquote type="cite">3) Run ICA to remove artifacts<br></blockquote></blockquote></blockquote><blockquote type="cite"><blockquote type="cite"><blockquote type="cite">4) Use a standard pre-event time window to baseline your data<br></blockquote></blockquote></blockquote><blockquote type="cite"><blockquote type="cite"><blockquote type="cite">5) Compute your ERPs<br></blockquote></blockquote></blockquote><blockquote type="cite"><blockquote type="cite"><blockquote type="cite"><br></blockquote></blockquote></blockquote><blockquote type="cite"><blockquote type="cite"><blockquote type="cite">Removing the mean of each epoch acts as a crude high-pass filter.<br></blockquote></blockquote></blockquote><blockquote type="cite"><blockquote type="cite"><blockquote type="cite">It's not nearly as selective as a "true" high pass filter but it<br></blockquote></blockquote></blockquote><blockquote type="cite"><blockquote type="cite"><blockquote type="cite">doesn't distort the ERP waveforms as much either. Moreover we've<br></blockquote></blockquote></blockquote><blockquote type="cite"><blockquote type="cite"><blockquote type="cite">found that the procedure described above massively improves the<br></blockquote></blockquote></blockquote><blockquote type="cite"><blockquote type="cite"><blockquote type="cite">reliability of ICA when compared to standard ERP prestimulus<br></blockquote></blockquote></blockquote><blockquote type="cite"><blockquote type="cite"><blockquote type="cite">baselines:<br></blockquote></blockquote></blockquote><blockquote type="cite"><blockquote type="cite"><blockquote type="cite"><br></blockquote></blockquote></blockquote><blockquote type="cite"><blockquote type="cite"><blockquote type="cite">Groppe, D.M., Makeig, S., & Kutas, M. (2009) Identifying reliable<br></blockquote></blockquote></blockquote><blockquote type="cite"><blockquote type="cite"><blockquote type="cite">independent components via split-half comparisons. NeuroImage, 45<br></blockquote></blockquote></blockquote><blockquote type="cite"><blockquote type="cite"><blockquote type="cite">pp.1199-1211.<br></blockquote></blockquote></blockquote><blockquote type="cite"><blockquote type="cite"><blockquote type="cite"><br></blockquote></blockquote></blockquote><blockquote type="cite"><blockquote type="cite"><blockquote type="cite">Hope this helps,<br></blockquote></blockquote></blockquote><blockquote type="cite"><blockquote type="cite"><blockquote type="cite"> -David<br></blockquote></blockquote></blockquote><blockquote type="cite"><blockquote type="cite"><blockquote type="cite"><br></blockquote></blockquote></blockquote><blockquote type="cite"><blockquote type="cite"><blockquote type="cite"><br></blockquote></blockquote></blockquote><blockquote type="cite"><blockquote type="cite"><blockquote type="cite"><br></blockquote></blockquote></blockquote><blockquote type="cite"><blockquote type="cite"><blockquote type="cite">On Wed, Oct 5, 2011 at 10:46 AM, Sara Graziadio<br></blockquote></blockquote></blockquote><blockquote type="cite"><blockquote type="cite"><blockquote type="cite"><sara.graziadio@newcastle.ac.uk> wrote:<br></blockquote></blockquote></blockquote><blockquote type="cite"><blockquote type="cite"><blockquote type="cite"><blockquote type="cite">Hello,<br></blockquote></blockquote></blockquote></blockquote><blockquote type="cite"><blockquote type="cite"><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></blockquote><blockquote type="cite"><blockquote type="cite"><blockquote type="cite">am doing an ERP analysis and I want to clean my data first with the ICA. In<br></blockquote></blockquote></blockquote><blockquote type="cite"><blockquote type="cite"><blockquote type="cite">theory, though, I should not use an high-pass cutoff higher than 0.1 Hz to not<br></blockquote></blockquote></blockquote><blockquote type="cite"><blockquote type="cite"><blockquote type="cite">reduce the erp amplitude. On the other side the ICA does not work well if the<br></blockquote></blockquote></blockquote><blockquote type="cite"><blockquote type="cite"><blockquote type="cite">high-pass cutoff is lower than 0.5 Hz...what is then the best method to apply?<br></blockquote></blockquote></blockquote><blockquote type="cite"><blockquote type="cite"><blockquote type="cite">Has anybody tested how robust the ica is with a 0.1Hz filter?<br></blockquote></blockquote></blockquote><blockquote type="cite"><blockquote type="cite"><blockquote type="cite"><blockquote type="cite">I have also another question: I am doing the analysis on 94 electrodes<br></blockquote></blockquote></blockquote></blockquote><blockquote type="cite"><blockquote type="cite"><blockquote type="cite">referenced to Fz. I planned to average reference the data but actually there is<br></blockquote></blockquote></blockquote><blockquote type="cite"><blockquote type="cite"><blockquote type="cite">quite a large spread of noise on all the electrodes with this method (muscular<br></blockquote></blockquote></blockquote><blockquote type="cite"><blockquote type="cite"><blockquote type="cite">artefacts for example from the temporal electrodes). But actually almost all<br></blockquote></blockquote></blockquote><blockquote type="cite"><blockquote type="cite"><blockquote type="cite">the papers are using the average reference so I was surprised, am I the only<br></blockquote></blockquote></blockquote><blockquote type="cite"><blockquote type="cite"><blockquote type="cite">one having this problem of noise? Would not be better just to keep the Fz<br></blockquote></blockquote></blockquote><blockquote type="cite"><blockquote type="cite"><blockquote type="cite">reference and then perhaps to average the erps for every different cortical<br></blockquote></blockquote></blockquote><blockquote type="cite"><blockquote type="cite"><blockquote type="cite">area and do the analysis on these averaged erps?<br></blockquote></blockquote></blockquote><blockquote type="cite"><blockquote type="cite"><blockquote type="cite"><blockquote type="cite"><br></blockquote></blockquote></blockquote></blockquote><blockquote type="cite"><blockquote type="cite"><blockquote type="cite"><blockquote type="cite">Thank you very much<br></blockquote></blockquote></blockquote></blockquote><blockquote type="cite"><blockquote type="cite"><blockquote type="cite"><blockquote type="cite"><br></blockquote></blockquote></blockquote></blockquote><blockquote type="cite"><blockquote type="cite"><blockquote type="cite"><blockquote type="cite">Best wishes<br></blockquote></blockquote></blockquote></blockquote><blockquote type="cite"><blockquote type="cite"><blockquote type="cite"><blockquote type="cite"><br></blockquote></blockquote></blockquote></blockquote><blockquote type="cite"><blockquote type="cite"><blockquote type="cite"><blockquote type="cite">Sara Graziadio<br></blockquote></blockquote></blockquote></blockquote><blockquote type="cite"><blockquote type="cite"><blockquote type="cite"><blockquote type="cite">Research Associate<br></blockquote></blockquote></blockquote></blockquote><blockquote type="cite"><blockquote type="cite"><blockquote type="cite"><blockquote type="cite">Newcastle University<br></blockquote></blockquote></blockquote></blockquote><blockquote type="cite"><blockquote type="cite"><blockquote type="cite"><blockquote type="cite"><br></blockquote></blockquote></blockquote></blockquote><blockquote type="cite"><blockquote type="cite"><blockquote type="cite"><blockquote type="cite"><br></blockquote></blockquote></blockquote></blockquote><blockquote type="cite"><blockquote type="cite"><blockquote type="cite"><blockquote type="cite"><br></blockquote></blockquote></blockquote></blockquote><blockquote type="cite"><blockquote type="cite"><blockquote type="cite"><blockquote type="cite">_______________________________________________<br></blockquote></blockquote></blockquote></blockquote><blockquote type="cite"><blockquote type="cite"><blockquote type="cite"><blockquote type="cite">Eeglablist page: http://sccn.ucsd.edu/eeglab/eeglabmail.html<br></blockquote></blockquote></blockquote></blockquote><blockquote type="cite"><blockquote type="cite"><blockquote type="cite"><blockquote type="cite">To unsubscribe, send an empty email to eeglablist-<br></blockquote></blockquote></blockquote></blockquote><blockquote type="cite"><blockquote type="cite"><blockquote type="cite">unsubscribe@sccn.ucsd.edu<br></blockquote></blockquote></blockquote><blockquote type="cite"><blockquote type="cite"><blockquote type="cite"><blockquote type="cite">For digest mode, send an email with the subject "set digest mime" to<br></blockquote></blockquote></blockquote></blockquote><blockquote type="cite"><blockquote type="cite"><blockquote type="cite">eeglablist-request@sccn.ucsd.edu<br></blockquote></blockquote></blockquote><blockquote type="cite"><blockquote type="cite"><blockquote type="cite"><blockquote type="cite"><br></blockquote></blockquote></blockquote></blockquote><blockquote type="cite"><blockquote type="cite"><blockquote type="cite"><br></blockquote></blockquote></blockquote><blockquote type="cite"><blockquote type="cite"><blockquote type="cite"><br></blockquote></blockquote></blockquote><blockquote type="cite"><blockquote type="cite"><blockquote type="cite"><br></blockquote></blockquote></blockquote><blockquote type="cite"><blockquote type="cite"><blockquote type="cite">--<br></blockquote></blockquote></blockquote><blockquote type="cite"><blockquote type="cite"><blockquote type="cite">David Groppe, Ph.D.<br></blockquote></blockquote></blockquote><blockquote type="cite"><blockquote type="cite"><blockquote type="cite">Postdoctoral Researcher<br></blockquote></blockquote></blockquote><blockquote type="cite"><blockquote type="cite"><blockquote type="cite">North Shore LIJ Health System<br></blockquote></blockquote></blockquote><blockquote type="cite"><blockquote type="cite"><blockquote type="cite">New Hyde Park, New York<br></blockquote></blockquote></blockquote><blockquote type="cite"><blockquote type="cite"><blockquote type="cite">http://www.cogsci.ucsd.edu/~dgroppe/<br></blockquote></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">_______________________________________________<br></blockquote></blockquote><blockquote type="cite"><blockquote type="cite">eeglablist mailing list eeglablist@sccn.ucsd.edu<br></blockquote></blockquote><blockquote type="cite"><blockquote type="cite">Eeglablist page: http://www.sccn.ucsd.edu/eeglab/eeglabmail.html<br></blockquote></blockquote><blockquote type="cite"><blockquote type="cite">To unsubscribe, send an empty email to eeglablist-unsub@sccn.ucsd.edu<br></blockquote></blockquote><blockquote type="cite"><blockquote type="cite">To switch to non-digest mode, send an empty email to eeglablist-nodigest@sccn.ucsd.edu<br></blockquote></blockquote><blockquote type="cite"><br></blockquote><blockquote type="cite">--------------------------------------------------------------------<br></blockquote><blockquote type="cite">Steven J. Luck, Ph.D.<br></blockquote><blockquote type="cite">Director, Center for Mind & Brain<br></blockquote><blockquote type="cite">Professor, Department of Psychology<br></blockquote><blockquote type="cite">University of California, Davis<br></blockquote><blockquote type="cite">Room 109<br></blockquote><blockquote type="cite">267 Cousteau Place<br></blockquote><blockquote type="cite">Davis, CA 95618<br></blockquote><blockquote type="cite">(530) 297-4424<br></blockquote><blockquote type="cite">E-Mail: sjluck@ucdavis.edu<br></blockquote><blockquote type="cite">Web: http://mindbrain.ucdavis.edu/people/sjluck<br></blockquote><blockquote type="cite">Calendar: http://www.google.com/calendar/embed?src=stevenjluck%40gmail.com&ctz=America/Los_Angeles<br></blockquote><blockquote type="cite">--------------------------------------------------------------------<br></blockquote><blockquote type="cite"><br></blockquote><blockquote type="cite"><br></blockquote><blockquote type="cite"><br></blockquote><blockquote type="cite"><br></blockquote><blockquote type="cite"><br></blockquote><blockquote type="cite"><br></blockquote><blockquote type="cite"><br></blockquote><blockquote type="cite"><br></blockquote><blockquote type="cite"><br></blockquote><blockquote type="cite"><br></blockquote><blockquote type="cite">_______________________________________________<br></blockquote><blockquote type="cite">Eeglablist page: http://sccn.ucsd.edu/eeglab/eeglabmail.html<br></blockquote><blockquote type="cite">To unsubscribe, send an empty email to eeglablist-unsubscribe@sccn.ucsd.edu<br></blockquote><blockquote type="cite">For digest mode, send an email with the subject "set digest mime" to eeglablist-request@sccn.ucsd.edu<br></blockquote><br></div></blockquote></div><br><div>
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"><span class="Apple-style-span" style="border-collapse: separate; -webkit-border-horizontal-spacing: 0px; -webkit-border-vertical-spacing: 0px; color: rgb(0, 0, 0); font-family: Helvetica; font-size: 12px; font-style: normal; font-variant: normal; font-weight: normal; letter-spacing: normal; line-height: normal; -webkit-text-decorations-in-effect: none; text-indent: 0px; -webkit-text-size-adjust: auto; text-transform: none; orphans: 2; white-space: normal; widows: 2; word-spacing: 0px; "><div style="word-wrap: break-word; -webkit-nbsp-mode: space; -webkit-line-break: after-white-space; "><span class="Apple-style-span" style="border-collapse: separate; -webkit-border-horizontal-spacing: 0px; -webkit-border-vertical-spacing: 0px; color: rgb(0, 0, 0); font-family: Helvetica; font-size: 12px; font-style: normal; font-variant: normal; font-weight: normal; letter-spacing: normal; line-height: normal; -webkit-text-decorations-in-effect: none; text-indent: 0px; -webkit-text-size-adjust: auto; text-transform: none; orphans: 2; white-space: normal; widows: 2; word-spacing: 0px; "><div style="margin-top: 0px; margin-right: 0px; margin-bottom: 0px; margin-left: 0px; ">--------------------------------------------------------------------</div><div style="margin-top: 0px; margin-right: 0px; margin-bottom: 0px; margin-left: 0px; ">Steven J. Luck, Ph.D.</div><div style="margin-top: 0px; margin-right: 0px; margin-bottom: 0px; margin-left: 0px; ">Director, Center for Mind & Brain</div><div style="margin-top: 0px; margin-right: 0px; margin-bottom: 0px; margin-left: 0px; ">Professor, Department of Psychology</div><div style="margin-top: 0px; margin-right: 0px; margin-bottom: 0px; margin-left: 0px; ">University of California, Davis</div><div style="margin-top: 0px; margin-right: 0px; margin-bottom: 0px; margin-left: 0px; ">Room 109</div><div style="margin-top: 0px; margin-right: 0px; margin-bottom: 0px; margin-left: 0px; ">267 Cousteau Place</div><div style="margin-top: 0px; margin-right: 0px; margin-bottom: 0px; margin-left: 0px; ">Davis, CA 95618</div><div style="margin-top: 0px; margin-right: 0px; margin-bottom: 0px; margin-left: 0px; ">(530) 297-4424</div><div style="margin-top: 0px; margin-right: 0px; margin-bottom: 0px; margin-left: 0px; ">E-Mail: <a href="mailto:sjluck@ucdavis.edu">sjluck@ucdavis.edu</a></div><div style="margin-top: 0px; margin-right: 0px; margin-bottom: 0px; margin-left: 0px; ">Web: <a href="http://mindbrain.ucdavis.edu/people/sjluck">http://mindbrain.ucdavis.edu/people/sjluck</a></div><div style="margin-top: 0px; margin-right: 0px; margin-bottom: 0px; margin-left: 0px; ">Calendar: <a href="http://www.google.com/calendar/embed?src=stevenjluck@gmail.com&ctz=America/Los_Angeles">http://www.google.com/calendar/embed?src=stevenjluck%40gmail.com&ctz=America/Los_Angeles</a></div><div style="margin-top: 0px; margin-right: 0px; margin-bottom: 0px; margin-left: 0px; ">--------------------------------------------------------------------</div><br class="Apple-interchange-newline"></span></div></span></div></span><br class="Apple-interchange-newline"></div></span><br class="Apple-interchange-newline"></div></span><br class="Apple-interchange-newline"></div></span><br class="Apple-interchange-newline"></div></span><br class="Apple-interchange-newline"></div></span><br class="Apple-interchange-newline"></div><br class="Apple-interchange-newline"></div></div></span></span><br class="Apple-interchange-newline">
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