<div dir="ltr"><div class="gmail_default" style="color:rgb(51,51,153)">Hello Emmanuelle, Hoping all is well. some thoughts below that should help. Best wishes!</div><div class="gmail_default" style="color:rgb(51,51,153)"><br></div><div class="gmail_default" style="color:rgb(51,51,153)">For your purposes, I would suggest sticking with one set of methods for all your participants, as it is not usually acceptable to process some participants one way, and some another. I would recommend doing normal (non-ICA) cleaning of all participants, and showing the results that way. After that, run the analyses within ICA, for only those participants who did have good ICs. It all depends on how many participants total you have, and how many you can "drop" for one reason or another. Finally , you may want to confirm for yourself that statistics actually change for one participant with and without the ICA cleaning.<br></div><div class="gmail_default" style="color:rgb(51,51,153)"><br></div><div class="gmail_default" style="color:rgb(51,51,153)">-if people don't blink or move their eyes during eyes closed rest, then we should not expect ICA to "find" such things. In the future, consider having people give you a series of sample blinks, movements, etc.. This will make it easier for ICA rejection, or similar methods that need some "information about artifacts". </div><div class="gmail_default" style="color:rgb(51,51,153)"><br></div><div class="gmail_default" style="color:rgb(51,51,153)">-some researchers take an IC-artifact template from other studies and "apply it" to new data. This works to some degree because at least eye artifacts are quite stereotypical.</div><div class="gmail_default" style="color:rgb(51,51,153)"><br></div><div class="gmail_default" style="color:rgb(51,51,153)">You're right, ICA methods and their deployment are still in development, and usually only the beginning of the process is well-delineated. The most advanced or current methods can usually be found in articles, or within in-house methods in various labs.</div><div class="gmail_default" style="color:rgb(51,51,153)">You might also want to look at automated processing pipelines discussed on this list, such as the TAPEEG toolbox, and another recent one from Kothe, Bigdely-Shamlo et al. See also the wide range of ICA-rejection toolboxes including ICMARC and SASICA.</div><div class="gmail_default" style="color:rgb(51,51,153)"><br></div><div class="gmail_default" style="color:rgb(51,51,153)">-Some data just does not give great ICA decompositions, for various reasons. </div><div class="gmail_default" style="color:rgb(51,51,153)"><br></div><div class="gmail_default" style="color:rgb(51,51,153)">-The small amount of time you have for ICA is likely impacting the quality of your ICA decompositions. It's better to have closer to a half hour or more, though many people try to use ICA on short periods.</div><div class="gmail_default" style="color:rgb(51,51,153)"><br></div><div class="gmail_default" style="color:rgb(51,51,153)">-It also depends on how many channels you have. 64 chans or better is recommended for ICA, though many people have published ICA and microstate results with 32 channels.</div><div class="gmail_default" style="color:rgb(51,51,153)"><br></div><div class="gmail_default" style="color:rgb(51,51,153)"><br></div><div class="gmail_default" style="color:rgb(51,51,153)"><br></div><div class="gmail_default" style="color:rgb(51,51,153)"><br></div><div class="gmail_default" style="color:rgb(51,51,153)"><br></div><div class="gmail_default" style="color:rgb(51,51,153)"><br></div><div class="gmail_default" style="color:rgb(51,51,153)"><br></div><div class="gmail_default" style="color:rgb(51,51,153)"><br></div><div class="gmail_default" style="color:rgb(51,51,153)"><br></div><div class="gmail_default" style="color:rgb(51,51,153)"><br></div><div class="gmail_default" style="color:rgb(51,51,153)"><br></div><div class="gmail_default" style="color:rgb(51,51,153)"><br></div></div><div class="gmail_extra"><br><div class="gmail_quote">On Mon, Aug 24, 2015 at 4:10 PM, Emmanuelle Renauld <span dir="ltr"><<a href="mailto:emmanuelle.renauld.1@ulaval.ca" target="_blank">emmanuelle.renauld.1@ulaval.ca</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" style="font-size:12pt;color:#000000;background-color:#ffffff;font-family:Calibri,Arial,Helvetica,sans-serif">
<p>Hi,</p>
<p> </p>
<p>There are many discussions on ICA, but I can't find many on what happens next!</p>
<p> </p>
<p>I am now working on data where the subjects had their eyes open and looked at a computer screen.
</p>
<p> </p>
<p>I am trying to use ICA to remove eye blinks and eye movements, but it is very difficult: I have no EOC, only 7 components, and only approximately 3-5 minutes of data.</p>
<p> </p>
<p> For most subjects, it works well, but sometimes data is not well divided, of course (ex: no component has the right topoplot, or their spectrum contain alpha waves). For some subjects, I have tried many different options, but I can never really remove eyeblinks
and eye movements.</p>
<p> </p>
<p>So now if we want to compute statistics (ex, Fourier spectrum), how do I compare subjects where it worked and subjects where these artefacts were not removed!? Sometimes I feel it would be better to forget artefact rejection, so that, at least, all subjects
are more or less the same... Any idea?</p>
<p> </p>
<p> </p>
<p>Thak you very much!</p>
</div>
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