<div dir="ltr"><div class="gmail_extra"><div class="gmail_quote"><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left-width:1px;border-left-color:rgb(204,204,204);border-left-style:solid;padding-left:1ex"><div dir="ltr"><div style="color:rgb(51,51,153)"><br><div class="gmail_default" style="color:rgb(51,51,153);display:inline">Hello Alessandra, </div>To correctly understand how to remove components, <div class="gmail_default" style="color:rgb(51,51,153);display:inline">it's best to </div>look at and consider several things that I've listed below, especially if you haven't had a chance to yet. There are only a few major classes of well-established artifacts with clear patterns, so they are no hard to find. All the best.</div><div style="color:rgb(51,51,153)"><br></div><div style="color:rgb(51,51,153)"><div class="gmail_default" style="color:rgb(51,51,153);display:inline">1</div>. First make sure you are running your ICA correctly on correctly cleaned and prepared data, data that is also of adequate length for ICA's requirements, etc.. <div class="gmail_default" style="color:rgb(51,51,153);display:inline">In short, is it a good ICA decomposition?</div></div></div></blockquote><div> </div><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left-width:1px;border-left-color:rgb(204,204,204);border-left-style:solid;padding-left:1ex"><div dir="ltr"><div style="color:rgb(51,51,153)"><div class="gmail_default" style="color:rgb(51,51,153);display:inline">2. If this your first time with eeglab, please make sure you have opened and worked with the eeglab tutorial data first so you understand how to run all major steps. It's better to cut your teeth with that data first.</div></div><div style="color:rgb(51,51,153)"><br></div><div style="color:rgb(51,51,153)"><div class="gmail_default" style="color:rgb(51,51,153);display:inline">3</div>. See the Onton and Makeig chapter in Luck's Handbook of Event-related COmponents. A copy of this and other important papers are available at Dr.Makeig's site, and also by searching on Google Scholar.</div><div style="color:rgb(51,51,153)"><br></div><div style="color:rgb(51,51,153)"><div class="gmail_default" style="color:rgb(51,51,153);display:inline">4</div>. Review several recent articles using ICA and removing artifactual ICs</div><div style="color:rgb(51,51,153)"><br></div><div style="color:rgb(51,51,153)"><div class="gmail_default" style="color:rgb(51,51,153);display:inline">5</div>. Review past eeglab list messages (recent ones exist) from the last year</div><div style="color:rgb(51,51,153)"><br></div><div style="color:rgb(51,51,153)"><div class="gmail_default" style="color:rgb(51,51,153);display:inline">6</div>. Check out the Artifact/ICA rejection tutorials in the eeglab online tutorial. Try using the tutorial data first.</div><div style="color:rgb(51,51,153)"><br></div><div style="color:rgb(51,51,153)"><div class="gmail_default" style="color:rgb(51,51,153);display:inline">7</div>. Use MARA, ADJUST, SASICA, or IC-MARC (from Frolich) - which are all tools for telling you which ICs are artifactual.These are all plugins for matlab. You will also benefit from reviewing the articles associates with each.</div><div style="color:rgb(51,51,153)"><br></div><div style="color:rgb(51,51,153)"><div class="gmail_default" style="color:rgb(51,51,153);display:inline">8</div>. Note that that "mixed" ICs that contain neural and artifact data, should probably not be removed.</div><div style="color:rgb(51,51,153)"><br></div><div style="color:rgb(51,51,153)"><div class="gmail_default" style="color:rgb(51,51,153);display:inline">9</div>. Remember that some people j<div class="gmail_default" style="color:rgb(51,51,153);display:inline">us</div>t remove a few ICs related to eye or muscle artifacts, and some people remove a lot more ICs, and some just analyze the good ICs and ignore the rest.<div class="gmail_default" style="color:rgb(51,51,153);display:inline"> Just emulate high-quality papers if top journals, if you're not sure about best steps.</div></div><div style="color:rgb(51,51,153)"><br></div><div style="color:rgb(51,51,153)"><div class="gmail_default" style="color:rgb(51,51,153);display:inline">10</div>. See also the "Clean Continuous data with ASR" plugin for eeglab, which can clean data quite well using a different decomposition technique to rebuild bad parts of the data.</div><div style="color:rgb(51,51,153)"><br></div><div style="color:rgb(51,51,153)"><div class="gmail_default" style="color:rgb(51,51,153);display:inline">11</div>. If you have an ERP design, you will likely see the benefits of removing ICs when you plot ERPs.</div><div style="color:rgb(51,51,153)"><br></div><div style="color:rgb(51,51,153)">1<div class="gmail_default" style="color:rgb(51,51,153);display:inline">2.</div> Looking at the data in terms of topomaps across a condition will also give you an idea of how well cleaned your data is.</div><div style="color:rgb(51,51,153)"><br></div><div style="color:rgb(51,51,153)">1<div class="gmail_default" style="color:rgb(51,51,153);display:inline">3</div>. Last examine your normal eegplot with all channels, and your spectopo plots, to determine what the data looks like before and after <div class="gmail_default" style="color:rgb(51,51,153);display:inline">testing various kinds of </div>IC removal.</div></div></blockquote><div><br></div><div><br></div><div> </div><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left-width:1px;border-left-color:rgb(204,204,204);border-left-style:solid;padding-left:1ex"><div dir="ltr"><div style="color:rgb(51,51,153)"><br></div><div style="color:rgb(51,51,153)"><br></div><div style="color:rgb(51,51,153)"><br></div><div style="color:rgb(51,51,153)"><br></div><div style="color:rgb(51,51,153)"><br></div><div style="color:rgb(51,51,153)"><br></div><div style="color:rgb(51,51,153)"><br></div><div style="color:rgb(51,51,153)"><br></div><div style="color:rgb(51,51,153)"><br></div><div style="color:rgb(51,51,153)"><br></div><div style="color:rgb(51,51,153)"><br></div><div style="color:rgb(51,51,153)"><br></div><div style="color:rgb(51,51,153)"><br></div></div><div class="gmail_extra"><br><div class="gmail_quote"><div><div class="h5">On Mon, Jan 11, 2016 at 8:53 AM, Alessandra Di Pietro <span dir="ltr"><<a href="mailto:dipietroalessandra@hotmail.it" target="_blank">dipietroalessandra@hotmail.it</a>></span> wrote:<br></div></div><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div><div class="h5">
<div dir="ltr">
<div style="font-size:12pt;color:#000000;background-color:#ffffff;font-family:Calibri,Arial,Helvetica,sans-serif">
<p>Hi everyone,</p>
<p>I'm working for the first time on EEGLAB, I performed ICA with 35 components and I obtained the first butterfly plot ( <a href="http://it.tinypic.com/r/2eehahc/9" target="_blank">http://it.tinypic.com/r/2eehahc/9</a>), after that I reject the artifacts
components by ICA (<a href="http://it.tinypic.com/r/15ebrqv/9" target="_blank">http://it.tinypic.com/r/15ebrqv/9</a>) but we are not sure if we deleted all the wrong components. I removed this components and plotted the butterfly cleaned ( <a href="http://it.tinypic.com/r/34opy1e/9" title="http://it.tinypic.com/r/34opy1e/9
Ctrl+Fai clic o tocca il collegamento per aprirlo" target="_blank">http://it.tinypic.com/r/34opy1e/9</a>).
Could you help me to understand if I cleaned correctly the signal?</p>
<p>Thank you so much</p>
<p>Alessandra</p>
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