<div dir="ltr">Dear Devvarta,<div><br></div><div>I agree with Stephen. Signal processing and Statistics is good to have.</div><div>When you perform channel warping, I think it needs optimization toolbox as well.</div><div><br></div><div>Makoto</div><div><br></div><div class="gmail_extra"><br><div class="gmail_quote">On Wed, Feb 8, 2017 at 5:43 PM, Dr Devvarta Kumar <span dir="ltr"><<a href="mailto:devvarta.k@nimhans.ac.in" target="_blank">devvarta.k@nimhans.ac.in</a>></span> wrote:<br><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><p>Hi </p><p>I am buying MATLAB for running EEGLAB. Please let me know whether only basic MATLAB is to be procured or any toolbox is also required to run EEGLAB?</p><p>Best wishes</p><p>Devvarta Kumar<br><br><br>----- Original Message -----<br>From: Makoto Miyakoshi <<a href="mailto:mmiyakoshi@ucsd.edu" target="_blank">mmiyakoshi@ucsd.edu</a>><br>Date: Sunday, January 22, 2017 1:21 pm<br>Subject: Re: [Eeglablist] ICA question<br>To: "Ahmad, Jumana" <<a href="mailto:jumana.ahmad@kcl.ac.uk" target="_blank">jumana.ahmad@kcl.ac.uk</a>><br>Cc: "<a href="mailto:eeglablist@sccn.ucsd.edu" target="_blank">eeglablist@sccn.ucsd.edu</a>" <<a href="mailto:eeglablist@sccn.ucsd.edu" target="_blank">eeglablist@sccn.ucsd.edu</a>><br><br></p><div dir="ltr"><font style="font-size:14px;font-style:normal;font-weight:normal;background-color:rgb(245,248,240)">> </font>Dear Jumana,<br><font style="font-size:14px;font-style:normal;font-weight:normal;background-color:rgb(245,248,240)">> </font><br><font style="font-size:14px;font-style:normal;font-weight:normal;background-color:rgb(245,248,240)">> </font>> For data rank reasons, I interpolated and average referenced after ICA, using runica. When re-referencing I deleted the ICA activation matrix and the ICA weights, because of course I now have interpolated channels which do not have ICA weights associated with them.<br><font style="font-size:14px;font-style:normal;font-weight:normal;background-color:rgb(245,248,240)">> </font><br><font style="font-size:14px;font-style:normal;font-weight:normal;background-color:rgb(245,248,240)">> </font>Average referencing is just to subtract a fixed value from all channels/ICs, so I thought it is harmless. You don't need to delete ICA-calculated items.<br><font style="font-size:14px;font-style:normal;font-weight:normal;background-color:rgb(245,248,240)">> </font><br><font style="font-size:14px;font-style:normal;font-weight:normal;background-color:rgb(245,248,240)">> </font>> I only wanted to do ICA for eye blink rejection, and it worked perfectly and I did not have any ICA corruption (trade offs or rank deficiency). I compared the data before and after ICA and the level of noise was the same. <br><font style="font-size:14px;font-style:normal;font-weight:normal;background-color:rgb(245,248,240)">> </font><br><font style="font-size:14px;font-style:normal;font-weight:normal;background-color:rgb(245,248,240)">> </font>Do you mean that if you don't reject ICA-calculated items, it destroys data? I'm interested in testing it. It's more likely it does not happen. If you say you reject channels after ICA, it's more likely to result in more complicated situation, depending on the path you follow using EEGLAB.<br><font style="font-size:14px;font-style:normal;font-weight:normal;background-color:rgb(245,248,240)">> </font><br><font style="font-size:14px;font-style:normal;font-weight:normal;background-color:rgb(245,248,240)">> </font>By the way what do you mean trade-off?<br><font style="font-size:14px;font-style:normal;font-weight:normal;background-color:rgb(245,248,240)">> </font><br><font style="font-size:14px;font-style:normal;font-weight:normal;background-color:rgb(245,248,240)">> </font>> I am working with an extremely large dataset and do not ideally want to re-do these steps, especially because I am mainly working with raw channel ERP data. <br><font style="font-size:14px;font-style:normal;font-weight:normal;background-color:rgb(245,248,240)">> </font><br><font style="font-size:14px;font-style:normal;font-weight:normal;background-color:rgb(245,248,240)">> </font>It's a good opportunity for you to start using batch code. If you follow my instructions in my wiki pages, 100-200 datasets are nothing. I created a STUDY with nearly 1,000 datasets with no problem, so I can guarantee up to 1,000. If you eventually want to go back to channels, then after ICA selections using std_selectICsByCluster(), perform channel statistics. In this case, I think interpolation will definitely help you because it'll eliminate missing value problems reasonably.<br><font style="font-size:14px;font-style:normal;font-weight:normal;background-color:rgb(245,248,240)">> </font><br><font style="font-size:14px;font-style:normal;font-weight:normal;background-color:rgb(245,248,240)">> </font>> this time on the Chanel data where ICA blink components have been removed<br><font style="font-size:14px;font-style:normal;font-weight:normal;background-color:rgb(245,248,240)">> </font><br><font style="font-size:14px;font-style:normal;font-weight:normal;background-color:rgb(245,248,240)">> </font>Even if you run ICA on IC-rejected clean data, you won't obtain cleaner data. What happens is that you'll get exactly the same decomposition. Try it with one subject to see it.<br><font style="font-size:14px;font-style:normal;font-weight:normal;background-color:rgb(245,248,240)">> </font><br><font style="font-size:14px;font-style:normal;font-weight:normal;background-color:rgb(245,248,240)">> </font>> Identify noisy channels<br><font style="font-size:14px;font-style:normal;font-weight:normal;background-color:rgb(245,248,240)">> </font>> Run ICA on clean channels<br><font style="font-size:14px;font-style:normal;font-weight:normal;background-color:rgb(245,248,240)">> </font>> Interpolate electrodes to give the same 60 electrodes per person<br><font style="font-size:14px;font-style:normal;font-weight:normal;background-color:rgb(245,248,240)">> </font>> Average reference to the 60 electrodes<br><font style="font-size:14px;font-style:normal;font-weight:normal;background-color:rgb(245,248,240)">> </font>> Compute ERPs on channel data<br><font style="font-size:14px;font-style:normal;font-weight:normal;background-color:rgb(245,248,240)">> </font>> Then re-run ICA, and do any further analysis on component data.<br><div><div class="gmail_extra"><font style="font-size:14px;font-style:normal;font-weight:normal;background-color:rgb(245,248,240)">> </font><br></div><div class="gmail_extra"><font style="font-size:14px;font-style:normal;font-weight:normal;background-color:rgb(245,248,240)">> </font>Yeah it works. Most likely, the last ICA will produce the same results except post average-reference components show zero-mean scalp topos.</div><div class="gmail_extra"><font style="font-size:14px;font-style:normal;font-weight:normal;background-color:rgb(245,248,240)">> </font><br></div><div class="gmail_extra"><font style="font-size:14px;font-style:normal;font-weight:normal;background-color:rgb(245,248,240)">> </font>Makoto</div><div class="gmail_extra"><font style="font-size:14px;font-style:normal;font-weight:normal;background-color:rgb(245,248,240)">> </font><br></div><div class="gmail_extra"><font style="font-size:14px;font-style:normal;font-weight:normal;background-color:rgb(245,248,240)">> </font><br></div><div class="gmail_extra"><font style="font-size:14px;font-style:normal;font-weight:normal;background-color:rgb(245,248,240)">> </font><br></div><div class="gmail_extra"><font style="font-size:14px;font-style:normal;font-weight:normal;background-color:rgb(245,248,240)">> </font><br><div class="gmail_quote"><font style="font-size:14px;font-style:normal;font-weight:normal;background-color:rgb(245,248,240)">> </font>On Fri, Jan 13, 2017 at 6:20 AM, Ahmad, Jumana <span dir="ltr"><<a>jumana.ahmad@kcl.ac.uk</a>></span> wrote:<br><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;padding-left:1ex;border-left-color:rgb(204,204,204);border-left-width:1px;border-left-style:solid"> <div dir="ltr"> <div id="m_-4415322609830842843gmail-m_-169335420982424779divtagdefaultwrapper" style="color:rgb(0,0,0);font-family:calibri,arial,helvetica,sans-serif;font-size:12pt" dir="ltr"> <p><font style="font-size:14px;font-style:normal;font-weight:normal;background-color:rgb(245,248,240)">> </font>Dear EEGlab. </p> <p><font style="font-size:14px;font-style:normal;font-weight:normal;background-color:rgb(245,248,240)">> </font>For data rank reasons, I interpolated and average referenced after ICA, using runica. When re-referencing I deleted the ICA activation matrix and the ICA weights, because of course I now have interpolated channels which do not have ICA weights associated with them.</p> <p><font style="font-size:14px;font-style:normal;font-weight:normal;background-color:rgb(245,248,240)">> </font><br> </p> <p><font style="font-size:14px;font-style:normal;font-weight:normal;background-color:rgb(245,248,240)">> </font>I only wanted to do ICA for eye blink rejection, and it worked perfectly and I did not have any ICA corruption (trade offs or rank deficiency). I compared the data before and after ICA and the level of noise was the same. </p> <p><font style="font-size:14px;font-style:normal;font-weight:normal;background-color:rgb(245,248,240)">> </font><br> </p> <p><font style="font-size:14px;font-style:normal;font-weight:normal;background-color:rgb(245,248,240)">> </font>I am working with an extremely large dataset and do not ideally want to re-do these steps, especially because I am mainly working with raw channel ERP data. </p> <p><font style="font-size:14px;font-style:normal;font-weight:normal;background-color:rgb(245,248,240)">> </font><br> </p> <p><font style="font-size:14px;font-style:normal;font-weight:normal;background-color:rgb(245,248,240)">> </font>However, for future analysis, if I did want to work with component data, could I re-run ICA for a second time, this time on the Chanel data where ICA blink components have been removed, and where the interpolated electrodes and the new reference implemented (this data should already be clean from blinks etc).</p> <p><font style="font-size:14px;font-style:normal;font-weight:normal;background-color:rgb(245,248,240)">> </font><br> </p> <p><font style="font-size:14px;font-style:normal;font-weight:normal;background-color:rgb(245,248,240)">> </font>Overal this would look like:</p> <p><font style="font-size:14px;font-style:normal;font-weight:normal;background-color:rgb(245,248,240)">> </font><br> </p> <p><font style="font-size:14px;font-style:normal;font-weight:normal;background-color:rgb(245,248,240)">> </font>Identify noisy channels</p> <p><font style="font-size:14px;font-style:normal;font-weight:normal;background-color:rgb(245,248,240)">> </font>Run ICA on clean channels </p> <p><font style="font-size:14px;font-style:normal;font-weight:normal;background-color:rgb(245,248,240)">> </font>Interpolate electrodes to give the same 60 electrodes per person</p> <p><font style="font-size:14px;font-style:normal;font-weight:normal;background-color:rgb(245,248,240)">> </font>Average reference to the 60 electrodes</p> <p><font style="font-size:14px;font-style:normal;font-weight:normal;background-color:rgb(245,248,240)">> </font>Compute ERPs on channel data</p> <p><font style="font-size:14px;font-style:normal;font-weight:normal;background-color:rgb(245,248,240)">> </font>Then re-run ICA, and do any further analysis on component data. </p> <p><font style="font-size:14px;font-style:normal;font-weight:normal;background-color:rgb(245,248,240)">> </font><br> </p> <p><font style="font-size:14px;font-style:normal;font-weight:normal;background-color:rgb(245,248,240)">> </font><br> </p> <p><font style="font-size:14px;font-style:normal;font-weight:normal;background-color:rgb(245,248,240)">> </font>Please let me know if you can see any issues with this. I very much appreciate any advice. </p> <p><font style="font-size:14px;font-style:normal;font-weight:normal;background-color:rgb(245,248,240)">> </font>Best wishes,</p> <p><font style="font-size:14px;font-style:normal;font-weight:normal;background-color:rgb(245,248,240)">> </font>Jumana </p> <p><font style="font-size:14px;font-style:normal;font-weight:normal;background-color:rgb(245,248,240)">> </font><br> </p> <div id="m_-4415322609830842843gmail-m_-169335420982424779Signature"> <div id="m_-4415322609830842843gmail-m_-169335420982424779divtagdefaultwrapper" style="color:rgb(0,0,0);font-family:calibri,arial,helvetica,sans-serif;font-size:12pt;background-color:rgb(255,255,255)"> <p></p> <div style="margin:0px;color:rgb(0,0,0);font-family:helvetica"><font face="Calibri,sans-serif" size="2"><span style="font-size:11pt"><font color="#1f497d" size="2"><span lang="en-GB" style="font-size:10pt"><b><font 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style="font-size:14px;font-style:normal;font-weight:normal;background-color:rgb(245,248,240)">> </font><br><font style="font-size:14px;font-style:normal;font-weight:normal;background-color:rgb(245,248,240)">> </font>______________________________<font style="font-size:14px;font-style:normal;font-weight:normal;background-color:rgb(245,248,240)"><wbr>> </font>_________________<br><font style="font-size:14px;font-style:normal;font-weight:normal;background-color:rgb(245,248,240)">> </font> Eeglablist page: <a href="http://sccn.ucsd.edu/eeglab/eeglabmail.html" rel="noreferrer" target="_blank">http://sccn.ucsd.edu/eeglab/<font style="font-size:14px;font-style:normal;font-weight:normal;background-color:rgb(245,248,240)">> </font>eeglabmail.html</a><br><font style="font-size:14px;font-style:normal;font-weight:normal;background-color:rgb(245,248,240)">> </font> To unsubscribe, send an empty email to <a>eeglablist-unsubscribe@sccn.<font style="font-size:14px;font-style:normal;font-weight:normal;background-color:rgb(245,248,240)">> </font>ucsd.edu</a><br><font style="font-size:14px;font-style:normal;font-weight:normal;background-color:rgb(245,248,240)">> </font> For digest mode, send an email with the subject "set digest mime" to <a>eeglablist-request@sccn.ucsd.<font style="font-size:14px;font-style:normal;font-weight:normal;background-color:rgb(245,248,240)">> </font>edu</a><br></blockquote></div><font style="font-size:14px;font-style:normal;font-weight:normal;background-color:rgb(245,248,240)">> </font><br><font style="font-size:14px;font-style:normal;font-weight:normal;background-color:rgb(245,248,240)">> </font><br clear="all"><div><font style="font-size:14px;font-style:normal;font-weight:normal;background-color:rgb(245,248,240)">> </font><br></div><font style="font-size:14px;font-style:normal;font-weight:normal;background-color:rgb(245,248,240)">> </font>-- <br><div class="m_-4415322609830842843gmail_signature"><div dir="ltr"><font style="font-size:14px;font-style:normal;font-weight:normal;background-color:rgb(245,248,240)">> </font>Makoto Miyakoshi<br><font style="font-size:14px;font-style:normal;font-weight:normal;background-color:rgb(245,248,240)">> </font>Swartz Center for Computational Neuroscience<br><font style="font-size:14px;font-style:normal;font-weight:normal;background-color:rgb(245,248,240)">> </font>Institute for Neural Computation, University of California San Diego<br></div></div> </div></div></div> > ______________________________<wbr>_________________<br>> Eeglablist page: <a href="http://sccn.ucsd.edu/eeglab/eeglabmail.html" target="_blank">http://sccn.ucsd.edu/eeglab/<wbr>eeglabmail.html</a><br>> To unsubscribe, send an empty email to eeglablist-<br>> unsubscribe@sccn.ucsd.eduFor digest mode, send an email with the <br>> subject "set digest mime" to <a href="mailto:eeglablist-request@sccn.ucsd.edu" target="_blank">eeglablist-request@sccn.ucsd.<wbr>edu</a><br><br>==============================<wbr>===============<br>Devvarta Kumar, Ph.D.<br><br>Additional Professor<br>Department of Clinical Psychology<br>M. V. Govindaswamy Building<br>National Institute of Mental Health and Neurosciences,<br>Hosur Road,<br>Bangalore,<br>Karnataka-560029<br>India<br><br>Ph: <a href="tel:+91%2080%202699%205188" value="+918026995188" target="_blank">+91-80-26995188</a><br><a href="http://www.nimhans.ac.in/users/dr-devvarta-kumar" target="_blank">http://www.nimhans.ac.in/<wbr>users/dr-devvarta-kumar</a><br><br>
</blockquote></div><br><br clear="all"><div><br></div>-- <br><div class="gmail_signature" data-smartmail="gmail_signature"><div dir="ltr">Makoto Miyakoshi<br>Swartz Center for Computational Neuroscience<br>Institute for Neural Computation, University of California San Diego<br></div></div>
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