<div dir="ltr"><font color="#000000">Thank you very very much for your valuable input! :)</font><div><font color="#000000"><br></font></div><div>
<div style="font-size:12.8px;background-color:rgb(255,255,255);text-decoration-style:initial;text-decoration-color:initial"><font color="#000000">-Fabio<br></font></div><div style="font-size:12.8px;background-color:rgb(255,255,255);text-decoration-style:initial;text-decoration-color:initial"><font color="#000000"><br></font></div><div style="font-size:12.8px;background-color:rgb(255,255,255);text-decoration-style:initial;text-decoration-color:initial"><div style="background-color:rgb(255,255,255);text-decoration-style:initial;text-decoration-color:initial;font-size:small"><font color="#000000">------------------------------<wbr>--</font></div><div style="background-color:rgb(255,255,255);text-decoration-style:initial;text-decoration-color:initial;font-size:small"><i><font color="#000000">Fabio Giatsidis, M.D.</font></i></div><div style="background-color:rgb(255,255,255);text-decoration-style:initial;text-decoration-color:initial;font-size:small"><font color="#000000">Resident in Neurology - University of Rome "Tor Vergata" - Rome, Italy</font></div><div style="background-color:rgb(255,255,255);text-decoration-style:initial;text-decoration-color:initial;font-size:small"><font color="#000000">Post-doctoral research fellow - Brown University - Providence, RI, USA</font></div></div></div><div><br></div></div><div class="gmail_extra"><br><div class="gmail_quote">On Wed, Aug 1, 2018 at 2:02 PM, Makoto Miyakoshi <span dir="ltr"><<a href="mailto:mmiyakoshi@ucsd.edu" target="_blank">mmiyakoshi@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 dir="ltr">Dear Fabio,<div><br></div><div>I will disappear from the list until November (others in the lab will answer instead), so this is my last response to you on this issue.</div><span class=""><div><br></div><div><span style="font-size:small;background-color:rgb(255,255,255);text-decoration-style:initial;text-decoration-color:initial;float:none;display:inline">> Can I run ICA on the 10-minute continuous recording (even if different "states" happen every 2 minutes) so that I have enough data points (sqrt(500 Hz x 600 sec / 30) = 100), and<span> </span></span><i style="font-size:small;background-color:rgb(255,255,255);text-decoration-style:initial;text-decoration-color:initial">then<span> </span></i><span style="font-size:small;background-color:rgb(255,255,255);text-decoration-style:initial;text-decoration-color:initial;float:none;display:inline">do my chunking?</span></div><div><span style="font-size:small;background-color:rgb(255,255,255);text-decoration-style:initial;text-decoration-color:initial;float:none;display:inline"><br></span></div></span><div><span style="font-size:small;background-color:rgb(255,255,255);text-decoration-style:initial;text-decoration-color:initial;float:none;display:inline">Technically yes.</span></div><span class=""><div><span style="font-size:small;background-color:rgb(255,255,255);text-decoration-style:initial;text-decoration-color:initial;float:none;display:inline"><br></span></div><div><span style="font-size:small;background-color:rgb(255,255,255);text-decoration-style:initial;text-decoration-color:initial;float:none;display:inline">> Or is this approach invalid because of the variance in the tasks that are asked every 2 min?</span><br></div><div><br></div></span><div>VERY good point! Such variance difference across task (blocks) is called non-stationarity. ICA's assumption is data be stationary. So theoretically speaking you are violating (better to say 'undermining') the assumption of stationarity. Addressing data non-stationarity is one of the most difficult problem in data processing. As far as I know, there is no established ICA preprorcessing pipeline there, although the fully-functional code of multi-model adaptive-mixture ICA has been there for ten years! We just don't know how to integrate the multiple models in a useful way.</div><div><br></div><div>In most cases, however, using single-model (i.e., standard) ICA across different tasks is fine. My explanation for this is that in EEG occipital alpha is always loud (show high amplitude), so is central mu, etc... these loud member's behaviors are more or less the same across many types of cognitive experiments because they respond to very general cognitive function such as attention and moving/not moving etc. You also want to remember that ICA is biased to high-amplitude activities.</div><div><br></div><div>In practice, if I were you, yes I would concatenate all the blocks to run a single ICA, then make comparison on both across the block-separated conditions and within-block conditions.</div><span class="HOEnZb"><font color="#888888"><div><br></div><div>Makoto</div></font></span><div><div class="h5"><br><div class="gmail_quote"><div dir="ltr">On Tue, Jul 31, 2018 at 9:51 PM Giatsidis, Fabio <<a href="mailto:fabio_giatsidis@brown.edu" target="_blank">fabio_giatsidis@brown.edu</a>> wrote:<br></div><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex"><div dir="ltr">Thank you Tarik and Makoto for your precious suggestions!<div><br></div><div>Actually, I should have clarified that the 2-minutes recordings are segments from a single 10-minute EEG recording, and the patient was asked to do different things every 2 minutes (keep eyes open, keep eyes closed, etc.). I thought that chunking the continuous recording in small, task-specific sub-recording before start cleaning each sub-recording would have made better sense. But given your answers, a new question comes to my mind:</div><div>- let's assume I first cut down
<span style="font-size:small;background-color:rgb(255,255,255);text-decoration-style:initial;text-decoration-color:initial;float:none;display:inline">arbitrarily<span> </span></span>the number of channels to ~100 or less as per Tarik's suggestion (i.e. excluding those around the ears, etc. that are of little help anyway). Can I run ICA on the 10-minute continuous recording (even if different "states" happen every 2 minutes) so that I have enough data points (sqrt(500 Hz x 600 sec / 30) = 100), and <i>then </i>do my chunking? Or is this approach invalid because of the variance in the tasks that are asked every 2 min?</div><div><br></div><div>Sorry for the confusion, I'm just trying to make the most sense out of what I have and to not make missteps! Thank you immensely for all your help!</div><div>-Fabio<br></div><div><br></div><div>
<div style="background-color:rgb(255,255,255);text-decoration-style:initial;text-decoration-color:initial;font-size:small">------------------------------<wbr>--</div><div style="background-color:rgb(255,255,255);text-decoration-style:initial;text-decoration-color:initial;font-size:small"><i>Fabio Giatsidis, M.D.</i></div><div style="background-color:rgb(255,255,255);text-decoration-style:initial;text-decoration-color:initial;font-size:small">Resident in Neurology - University of Rome "Tor Vergata" - Rome, Italy</div><div style="background-color:rgb(255,255,255);text-decoration-style:initial;text-decoration-color:initial;font-size:small">Post-doctoral research fellow - Brown University - Providence, RI, USA</div>
<br></div><div class="gmail_extra"><br><div class="gmail_quote">On Wed, Jul 25, 2018 at 3:18 PM, Makoto Miyakoshi <span dir="ltr"><<a href="mailto:mmiyakoshi@ucsd.edu" target="_blank">mmiyakoshi@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 dir="ltr">Dear Fabio,<div><br></div><div><span><div style="font-size:small;background-color:rgb(255,255,255);text-decoration-style:initial;text-decoration-color:initial">> I have a (probably naive) question regarding ICA.<br></div><div><br></div></span>I'll give you my naive answers, not intentionally but due to my limitations. Follow them at your own risk.<br class="m_-3219630500608125505m_8901058386006335821m_8533337502848869948gmail-Apple-interchange-newline"><br></div><div><span><div style="font-size:small;background-color:rgb(255,255,255);text-decoration-style:initial;text-decoration-color:initial">> - whether I should or not choose the whole 128 channel set for running ICA</div><div style="font-size:small;background-color:rgb(255,255,255);text-decoration-style:initial;text-decoration-color:initial"><br></div></span><div style="font-size:small;background-color:rgb(255,255,255);text-decoration-style:initial;text-decoration-color:initial">Yes, unless your data is too short for that. Remember, (number of channels)^2 x 30 data points at 256 Hz sampling rate is a rule of thumb for running ICA.</div><span><div style="font-size:small;background-color:rgb(255,255,255);text-decoration-style:initial;text-decoration-color:initial"><br></div><div style="font-size:small;background-color:rgb(255,255,255);text-decoration-style:initial;text-decoration-color:initial">> - consequently, how I should <i>a priori</i> decide which channels to consider for ICA and which not.<br></div><br></span></div><div>You can determine a priori which anatomical regions you are going to analyze. Then, if ICA gives you the '(stationary) effective source locations' that overlap /are close enough to those pre-selected regions, pick them up for the final analysis.</div><div><br></div><div>ICA is a hypothesis-free approach, but that does not mean you cannot have a hypothesis.</div><div>You might enjoy reading the classic discussion between ICA pioneers and Karl Friston about how ICA could be used in neuroscience data analysis.</div><div><div>Friston KJ. <span style="font-size:small;background-color:rgb(255,255,255);text-decoration-style:initial;text-decoration-color:initial;float:none;display:inline">Modes or models: a critique on independent component analysis for fMRI. </span>Trends Cogn Sci. 1998. Oct 01; 2(10) 373-375</div></div><span><div><br></div><div><span style="font-size:small;background-color:rgb(255,255,255);text-decoration-style:initial;text-decoration-color:initial;float:none;display:inline">> The recordings are ~2 minutes long and the sampling rate is 1000 Hz.</span></div><div><span style="font-size:small;background-color:rgb(255,255,255);text-decoration-style:initial;text-decoration-color:initial;float:none;display:inline"><br></span></div></span><div><span style="font-size:small;background-color:rgb(255,255,255);text-decoration-style:initial;text-decoration-color:initial;float:none;display:inline">If you have only 2 min, you definitely cannot perform >100ch ICA.</span></div><div><span style="font-size:small;background-color:rgb(255,255,255);text-decoration-style:initial;text-decoration-color:initial;float:none;display:inline">sqrt(250 Hz x 120 sec / 30) is about 31, so you want to use 'pca' option to perform dimension reduction to obtain 31 ICs.</span></div><span><div><span style="font-size:small;background-color:rgb(255,255,255);text-decoration-style:initial;text-decoration-color:initial;float:none;display:inline"><br></span></div><div><span style="font-size:small;background-color:rgb(255,255,255);text-decoration-style:initial;text-decoration-color:initial;float:none;display:inline">> I would like to keep as many channels as possible during pre-processing, and afterwards discard the ones I realize are not useful to my analysis - if this approach seems reasonable.</span><br></div><div><br></div></span><div>Record longer. 2-min EEG is too short if you want to use ICA.</div><div><br></div><div>Makoto</div><br><div class="gmail_quote"><div><div class="m_-3219630500608125505m_8901058386006335821h5"><div dir="ltr">On Thu, Jul 19, 2018 at 3:26 AM Giatsidis, Fabio <<a href="mailto:fabio_giatsidis@brown.edu" target="_blank">fabio_giatsidis@brown.edu</a>> wrote:<br></div></div></div><blockquote class="gmail_quote" style="margin:0px 0px 0px 0.8ex;border-left:1px solid rgb(204,204,204);padding-left:1ex"><div><div class="m_-3219630500608125505m_8901058386006335821h5"><div dir="ltr">
<div style="font-size:small;text-decoration-style:initial;text-decoration-color:initial">Hello EEGLAB list,</div><div style="font-size:small;text-decoration-style:initial;text-decoration-color:initial"><br></div><div style="font-size:small;text-decoration-style:initial;text-decoration-color:initial">I have a (probably naive) question regarding ICA.<br></div><div style="font-size:small;text-decoration-style:initial;text-decoration-color:initial">I have been using an <span style="font-size:12.8px;background-color:rgb(255,255,255);text-decoration-style:initial;text-decoration-color:initial;float:none;display:inline">EGI 128-channel system to record resting states. I have been reading a bit about ICA, but </span>it is still not clear to me:</div><div style="font-size:small;text-decoration-style:initial;text-decoration-color:initial">- whether I should or not choose the whole 128 channel set for running ICA, and</div><div style="font-size:small;text-decoration-style:initial;text-decoration-color:initial">- consequently, how I should <i>a priori</i> decide which channels to consider for ICA and which not.</div><div style="font-size:small;text-decoration-style:initial;text-decoration-color:initial"><br></div><div style="font-size:small;text-decoration-style:initial;text-decoration-color:initial">The recordings are ~2 minutes long and the sampling rate is 1000 Hz. I would like to keep as many channels as possible during pre-processing, and afterwards discard the ones I realize are not useful to my analysis - if this approach seems reasonable.</div><div style="font-size:small;text-decoration-style:initial;text-decoration-color:initial"><br></div><div style="font-size:small;text-decoration-style:initial;text-decoration-color:initial">Also:</div><div style="font-size:small;text-decoration-style:initial;text-decoration-color:initial">- if as a very first step I delete some clearly bad channels and then interpolate them to repopulate the original channel set, is it legit to include such interpolated channels during ICA?</div><div style="font-size:small;text-decoration-style:initial;text-decoration-color:initial"><br></div><div style="font-size:small;text-decoration-style:initial;text-decoration-color:initial">Thank you very much!</div><div style="font-size:small;text-decoration-style:initial;text-decoration-color:initial">Best,</div><div style="font-size:small;text-decoration-style:initial;text-decoration-color:initial">-Fabio</div><div style="font-size:small;text-decoration-style:initial;text-decoration-color:initial"><br></div><div style="font-size:small;text-decoration-style:initial;text-decoration-color:initial">------------------------------<wbr>--</div><div style="font-size:small;text-decoration-style:initial;text-decoration-color:initial"><i>Fabio Giatsidis, M.D.</i></div><div style="font-size:small;text-decoration-style:initial;text-decoration-color:initial">Resident in Neurology - University of Rome "Tor Vergata" - Rome, Italy</div><div style="font-size:small;text-decoration-style:initial;text-decoration-color:initial">Post-doctoral research fellow - Brown University - Providence, RI, USA</div></div></div></div><span>
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</blockquote></div><br clear="all"><div><br></div>-- <br><div dir="ltr" class="m_-3219630500608125505gmail_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></div></div></div>
</blockquote></div><br></div>