<div dir="ltr"><div class="gmail_default" style="color:#333399">Hello Ida,</div><div class="gmail_default" style="color:#333399">Just a few quick points:</div><div class="gmail_default" style="color:#333399">1. I would ask how was the data truly continuous if you did artifact rejection on it ?</div>
<div class="gmail_default" style="color:#333399"><br></div><div class="gmail_default" style="color:#333399">2.Epoching the data won't hurt the components. You should eventually end up with a (potentially) clean(er) ICA decomposition, and then apply that back to a full continuous data set, or a continuous (or epoched) dataset free of artifactual time periods.</div>
<div class="gmail_default" style="color:#333399"><br></div><div class="gmail_default" style="color:#333399">3. At some point you will reach some decisions that no one has made before or only few researchers have explored. I would say your best bet is to try the analysis yourself on a few cases so you can have some first-hand information as to what seems to be working best.</div>
<div class="gmail_default" style="color:#333399"><br></div><div class="gmail_default" style="color:#333399">4. Yes, there are many kinds of analyses and processing you can do with the ICs you have, so you should try to see what you have when you try to interpret your ICs in relation to the task and the relevant eeg literature about the kind of cognition or behavior you are studying.</div>
<div class="gmail_default" style="color:#333399"><br></div><div class="gmail_default" style="color:#333399">5. When you have a chance, consider sharing some of your results with the list, as this would be beneficial for other people just learning about ICA.</div>
<div class="gmail_default" style="color:#333399"><br></div><div class="gmail_default" style="color:#333399">Cheers!</div><div class="gmail_default" style="color:#333399"><br></div><div class="gmail_default" style="color:#333399">
<br></div><div class="gmail_default" style="color:#333399"><br></div><div class="gmail_default" style="color:#333399"><br></div><div class="gmail_default" style="color:#333399">3. </div></div><div class="gmail_extra"><br clear="all">
<div>Tarik Bel-Bahar, Postdoctoral Fellow<br>Perception, Performance & Psychophysiology Lab<br><a href="http://tarik.777@duke.edu/" target="_blank">tarik.777@duke.edu/</a> 919 328 9573<br>Div. of Brain Stimulation and Neurophysiology<br>
Dep. of Psychiatry and Behavioral Sciences<br>Duke University Medical Center, Duke Clinics<br>Red Zone, 5th Floor, Rm. 54236<br>200 Trent Drive, Durham, NC 27710</div>
<br><br><div class="gmail_quote">On Sat, Mar 30, 2013 at 5:40 AM, ida miokovic <span dir="ltr"><<a href="mailto:ida.miokovic@gmail.com" target="_blank">ida.miokovic@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 dir="ltr">Hello everyone,<div><br></div><div>thank you Marco, Tarik, Makoto, Arnaud for your suggestions.</div><div><br></div><div>I will surely consider using regepochs function. As I understood its functioning correctly - it is epoching data set in way that it is possible to merge them together later. Since I already calculated ICA algorithm on continuous data, does it make sense to epoch the data now, before second ICA, what would it "do" to components? Or it is better to do the epoching before first ICA and do it again?</div>
<div><br></div><div>At this moment I see also this option - Since I read that the experience of some researchers is that second ICA does not mean that much better decomposition - I could remove components (calculated on continuous data) recognized as artifacts and prune the dataset and see what I have.</div>
<div><br></div><div>I appreciate your opinion.</div><div><br></div><div>Thanks </div><span class="HOEnZb"><font color="#888888"><div><br></div><div>Ida</div></font></span></div><div class="HOEnZb"><div class="h5"><div class="gmail_extra">
<br><br><div class="gmail_quote">On Thu, Mar 28, 2013 at 10:26 AM, Arnaud Delorme <span dir="ltr"><<a href="mailto:arno@ucsd.edu" target="_blank">arno@ucsd.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">Dear Ida,<div><br></div><div>there is the menu "Tools > Automatic continuous rejection" although this does not use ICA component activities. It would be a good idea to add component to that function as well as channels.</div>
<div><br></div><div>Arno</div><div><div><div><br><div><div>On 27 Mar 2013, at 17:53, Makoto Miyakoshi wrote:</div><br><blockquote type="cite">Dear Ida,<div><br></div><div>>Are there any suggestions how to implement any kind of "bad" parts of the components removal, when having continuous data (before running second ICA)? <br>
<br>When you plot raw EEG, you can choose time period (marked as light blue) to register as bad data. You can reject the marked data portion later.</div>
<div><br></div><div>If you are interested in automated algorithm for that you may want to check BCILAB.</div><div><br></div><div>Makoto<br><br><div class="gmail_quote">2013/3/24 ida miokovic <span dir="ltr"><<a href="mailto:ida.miokovic@gmail.com" target="_blank">ida.miokovic@gmail.com</a>></span><br>
<blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex">Dear Makoto, Mikolaj and Marco (and everyone else =)),<div><br></div><div>thank you very much for your answers. </div>
<div>
<br></div><div>Regarding 1st issue - Yes, I have been informed that there is a bug that causes this channels locations problem, I have seen that others reported the same issue, so I didn't reported it myself since it was said that this will be corrected in the next EEGLAB version. Marco, is it necessary for me to report this in order to get the developers version? How could I get it?</div>
<div><br></div><div>Regarding 2nd issue - I didn't realize that mentioned methods are applicable only on epoched data. Are there any suggestions how to implement any kind of "bad" parts of the components removal, when having continuous data (before running second ICA)? Does EEGLAB support that? If not, any pipeline in MATLAB itself would be appreciated...</div>
<div><br></div><div>Thank you very much again</div><span><font color="#888888"><div><br></div></font></span><div><span><font color="#888888">Ida</font></span><div><div><br><br><div class="gmail_quote">
On Fri, Mar 22, 2013 at 4:32 PM, Marco Montalto <span dir="ltr"><<a href="mailto:montaltomarco@onvol.net" target="_blank">montaltomarco@onvol.net</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>Dear Ida,</div><div><br></div><div>Regarding Problem no. 1, I was having the same problem with the same version of EEGLAB you are using. When I plotted channel locations by pressing plot-2D I used to get a plot with all channels squashed in the bottom left hand corner of the plot. I reported the problem. Arno reproduced the problem and made the necessary changes to the developer's version of EEGLAB. I am using the developer's version now and the problem is indeed solved. So I would recommend that you use the developer's version to overcome this problem.</div>
<div><br></div><div>Regarding Problem no. 2, (corroborating what was said previously by Mikołaj Magnuski), Reject data (all methods) is greyed out because you have not extracted any epochs. Since you are using continuous data you cannot make use of this function, .</div>
<div><br></div><div>Regards,</div><div>Marco</div><div><div><br></div><div><br></div><div><span style="font-family:arial,sans-serif;font-size:13px">2013/3/17 ida miokovic </span><span dir="ltr" style="font-family:arial,sans-serif;font-size:13px"><<a href="mailto:ida.miokovic@gmail.com" target="_blank">ida.miokovic@gmail.com</a>></span><br style="font-family:arial,sans-serif;font-size:13px">
<blockquote class="gmail_quote" style="font-family:arial,sans-serif;font-size:13px;margin:0px 0px 0px 0.8ex;border-left-width:1px;border-left-color:rgb(204,204,204);border-left-style:solid;padding-left:1ex">Dear list,<div>
<br></div><div>I am running ICA decomposition on continuous data. I have several questions regarding this:</div><div><br></div><div>1. After applying lowpass filter and removing line noise (using Cleanline plug-in), I import the channel locations of the dataset. I am using EEGLAB v12.0.1.0b which is having issues with channel locations importing, but when I obtain ICA components, scalp maps seem to be plotted correctly, so I suppose that this bug does not influence this step? After that, I do the average reference.</div>
<div><br></div><div>2. Since I plan to run ICA second time, I should remove bad trials of the components (not entire component/s) using Tools --> Reject data using ICA --> Reject data (all methods). After doing all described above, I do not have an option to click on this tab "Reject data (all methods)" (it is grey). It is possibe to click only on "Reject components by map". Does anyone have an idea what could be the reason for this? </div>
<div><br></div><div>3. There is a suggestion that not more than 10% of the dataset should be rejected after applying all the methods I am interested in. Are some of the methods given it this menu (<span style="text-align:justify">Find abnormal values, Find abnormal trends, Find impropable data, Find abnormal distributions, Find abnormal spectra (slow)) more convenient that others and which? Which way I can calculate what is the percent of the rejected data?</span></div>
<div><p style="text-align:justify">4. Should the mean of the all channels be removed somewhere in this procedure and where is it suggested to be done? As I mentioned, here contiuous data is used, so there are no epochs and no need for baseline removal.</p>
<p style="text-align:justify"><br></p><p style="text-align:justify">Thank you very much for any help.</p><p style="text-align:justify">All the best</p><font color="#888888"><p style="text-align:justify">Ida</p><div><br></div>
</font></div></blockquote></div></div></div></blockquote></div><br></div></div></div>
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-- <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>
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