<html>
<head>
<meta content="text/html; charset=windows-1252"
http-equiv="Content-Type">
</head>
<body text="#000000" bgcolor="#FFFFFF">
<div class="moz-cite-prefix">Hi Makoto, <br>
<br>
Just to clarify, while the process of developing my method does
use inputs from the ICLabel website, the final product that people
will use will <b><i>only</i></b> look at the EEG measures and
nothing from the website. Just like the other methods, it will not
require people to actively contribute labels to work once
released. The label collection is only for the development of the
classier.<br>
<br>
Luca<br>
<br>
On 06/30/2017 05:52 PM, Makoto Miyakoshi wrote:<br>
</div>
<blockquote
cite="mid:CAEqC+SXVK48QjNJ5KS3z09KLz44XBgosLjOEn=ewTJM7chfs-A@mail.gmail.com"
type="cite">
<meta http-equiv="Content-Type" content="text/html;
charset=windows-1252">
<div dir="ltr">Dear Marius,
<div><br>
</div>
<div>Thank you for your opinion.</div>
<div><br>
</div>
<div>My colleague Luca has been working on automatic IC labeling
using machine learning algorithm: but it still uses <i>user
input </i>as the data to be tranied. So it is NOT a
solution to develop a better or perfect algorithm that judges
what is what based on EEG measures (he tested all the kinds of
algorithms available at the timepoint of last year, and found
nothing was perfect.) See this page for his data collection
scheme (which is also a educational tool)</div>
<div><br>
</div>
<div><a moz-do-not-send="true"
href="http://reaching.ucsd.edu:8000/auth/login">http://reaching.ucsd.edu:8000/auth/login</a><br>
</div>
<div><br>
</div>
<div>I have been collaborating with radiologists and
neurologists. They diagnose patients, particularly
neurologists even determines which brain tissue to remove.
What algorithm do they use? They use eyeballs. After all,
humans are still the best learning machine today (though it is
very tempting to make my own criteria for IC labeling for
non-aggressive data cleaning)</div>
<div><br>
</div>
<div>Makoto</div>
</div>
<div class="gmail_extra"><br>
<div class="gmail_quote">On Fri, Jun 30, 2017 at 12:54 AM,
Marius Klug <span dir="ltr"><<a moz-do-not-send="true"
href="mailto:marius.s.klug@gmail.com" target="_blank">marius.s.klug@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">
<div>
<div>
<div>
<div>Hi Gian, Makoto, list<br>
<br>
</div>
the quest for more reproducibility in EEG is one of
my personal topics of interest, so here are a few
comments from my point of view: I too would consider
an automated method for IC classification a valuable
tool, and dismissing it is not as easy as Makoto
suggests if you ask me. But I agree with Makoto:
there are no perfect solutions and manual inspection
to date is hard to get around since experienced
inspectors are just better than the algorithms.
However the reasoning that it's not reproducible is
valid, and also maybe a trained eye is better than
automated methods, but a novice might not exactly
know where to look at and the quality may suffer...<br>
<br>
You didn't specify if you just want to reject
eye-movement and other artifacts or want to go the
other way round and just keep ICs that are
specifically generated by the brain. In the latter
case you might want to try out a few things, because
there are some that work okay-ish: <br>
First, you can use dipole fitting and check the
residual variance for each IC, and have a threshold
of <0.15 (typically) for brain ICs. This is not a
fail-safe method, however, since eye-ICs and
sometimes also very narrow (not spread around the
whole head) muscle or other artifactual ICs can have
a small residual variance. You can also check if the
located dipoles lie inside the brain of the dipfit
model. <br>
Then there's SASICA, an EEGLAB plug-in also
published (Chaumon, Bishop & Busch, 2015). I
highly recommend the paper also for a deeper
understanding of the IC classification. Playing
around with SASICA I found it to be okay - not
perfect - for an automated method. You can play
around with the different classifiers (it uses
several methods in combination - dipfit being one of
them, but not mandatory) and their respective
thresholds and check if it suits you. <br>
<br>
</div>
Unfortunately, EEG methods do have a high degree of
subjectivity in several steps, and the automated
methods are usually not as good as manual inspection
yet (bad channel detection and time-domain artifacts
as well), but it's a trade-off between clear
reproducibility and best quality, the topic as a whole
needs to be treated with care. I hope you find some
valuable information in my suggestions and wish you a
successful study and smooth data analysis process! ;-)<br>
<br>
</div>
Best,<br>
</div>
Marius<br>
<div>
<div>
<div>
<div>
<div><br>
</div>
</div>
</div>
</div>
</div>
</div>
<div class="HOEnZb">
<div class="h5">
<div class="gmail_extra"><br>
<div class="gmail_quote">2017-06-30 4:02 GMT+02:00
Makoto Miyakoshi <span dir="ltr"><<a
moz-do-not-send="true"
href="mailto:mmiyakoshi@ucsd.edu"
target="_blank">mmiyakoshi@ucsd.edu</a>></span>:<br>
<blockquote class="gmail_quote" style="margin:0 0 0
.8ex;border-left:1px #ccc solid;padding-left:1ex">
<div dir="ltr">Dear Gian Marco,
<div><br>
</div>
<div>Ah I missed your important email! Sorry for
being so late.</div>
<div>Automated ICA rejection is not more
reliable than manual inspection, because the
programmer implemented his or her own criteria
(or his or her research results on criteria)
to the application. Therefore, for rebuttal
you can say 'Then why do neurologists still
use their eyeballs to identify epileptic
spikes, given the great advance of machine
learning technology today' etc... Trained eyes
are still one of the best solutions.</div>
<div><br>
</div>
<div>> Another question is about bad channel
interpolation. In EEGlab there is the kurtosis
method but it does not work so good. Do you
known any other automatic method that
recognize bad channel?<br>
</div>
<div class="gmail_extra"><br>
</div>
<div class="gmail_extra">I recommend either the
one implemented in clean_rawdata() plugin or
the one in PREP plugin. The former was
developed by Christian Kothe and the other by
Nima Bigdely-Shamlo, both are former SCCN
colleagues (now in Qusp). Let me share a piece
of my recent writing.</div>
<div class="gmail_extra"><br>
</div>
<div class="gmail_extra"><span
id="m_1741620722056554548m_-7186107844428593281gmail-docs-internal-guid-f5720a7e-f6bc-1c2f-80b1-d91b88a0feab"><span style="font-size:11pt;font-family:Arial;color:rgb(0,0,0);background-color:transparent;vertical-align:baseline;white-space:pre-wrap">We first performed outlier channel detection, rejection, and interpolation using the </span><span style="font-size:11pt;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-style:italic;vertical-align:baseline;white-space:pre-wrap">clean_rawdata</span><span style="font-size:11pt;font-family:Arial;color:rgb(0,0,0);background-color:transparent;vertical-align:baseline;white-space:pre-wrap"> plug-in (contributed by Christian Kothe) also available through the EEGLAB Extension Manager. This plug-in calculates each scalp channel signal’s correlation to its random sample consensus (RANSAC) estimate computed from nearby scalp channel signals in successive 5-s segments. Channel signals exhibiting low correlation to signals in neighboring scalp channels (e.g., here r < 0.8 at more than 40% of the data points) were rejected and then replaced with an interpolated channel using the spherical option in </span><span style="font-size:11pt;font-family:Arial;color:rgb(0,0,0);background-color:transparent;font-style:italic;vertical-align:baseline;white-space:pre-wrap">eeg_interp</span><span style="font-size:11pt;font-family:Arial;color:rgb(0,0,0);background-color:transparent;vertical-align:baseline;white-space:pre-wrap"> which makes use of Legendre polynomials up to degree 7 to calculate unbiased expected channel values (see Mullen et al., 2015).</span></span><br>
</div>
<div class="gmail_extra"><br>
</div>
<div class="gmail_extra">Makoto</div>
<div class="gmail_extra"><br>
</div>
<div class="gmail_extra"><br>
</div>
<div class="gmail_extra"><br>
<div class="gmail_quote">On Mon, Mar 27, 2017
at 7:58 AM, Gian Marco Duma <span dir="ltr"><<a
moz-do-not-send="true"
href="mailto:gmduma90@gmail.com"
target="_blank">gmduma90@gmail.com</a>></span>
wrote:<br>
<blockquote class="gmail_quote"
style="margin:0px 0px 0px
0.8ex;border-left:1px solid
rgb(204,204,204);padding-left:1ex">
<div dir="ltr">Dear EEGlab community, I'm
writing because I need a suggestion. ICA
works very well for eye blink and eye
movements correction, and indeed it
works very well too for artifacts
identification as muscle contraction,
electrical noise and so on. Thanks to
the experience it becomes possible to
recognize the specific components by
visual inspection, even if an
experimenter must be very careful in
components rejection. I submitted a
pre-registered reports to Cortex
journal, and they asked me for an
automatized ICA components rejection
method because visual inspection is not
considered as a reproducible method. So
I'm writing to ask for a suggetion about
possible automatized components
rejection methods, specially for eye
blink and eye movements.
<div>Another question is about bad
channel interpolation. In EEGlab there
is the kurtosis method but it does not
work so good. Do you known any other
automatic method that recognize bad
channel?</div>
<div>Thanks for your help</div>
</div>
<br>
______________________________<wbr>_________________<br>
Eeglablist page: <a
moz-do-not-send="true"
href="http://sccn.ucsd.edu/eeglab/eeglabmail.html"
rel="noreferrer" target="_blank">http://sccn.ucsd.edu/eeglab/ee<wbr>glabmail.html</a><br>
To unsubscribe, send an empty email to <a
moz-do-not-send="true"
href="mailto:eeglablist-unsubscribe@sccn.ucsd.edu"
target="_blank">eeglablist-unsubscribe@sccn.uc<wbr>sd.edu</a><br>
For digest mode, send an email with the
subject "set digest mime" to <a
moz-do-not-send="true"
href="mailto:eeglablist-request@sccn.ucsd.edu"
target="_blank">eeglablist-request@sccn.ucsd.e<wbr>du</a><span
class="m_1741620722056554548HOEnZb"><font
color="#888888"><br>
</font></span></blockquote>
</div>
<span class="m_1741620722056554548HOEnZb"><font
color="#888888"><br>
<br clear="all">
<div><br>
</div>
-- <br>
<div
class="m_1741620722056554548m_-7186107844428593281gmail_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>
</font></span></div>
</div>
<br>
______________________________<wbr>_________________<br>
Eeglablist page: <a moz-do-not-send="true"
href="http://sccn.ucsd.edu/eeglab/eeglabmail.html"
rel="noreferrer" target="_blank">http://sccn.ucsd.edu/eeglab/ee<wbr>glabmail.html</a><br>
To unsubscribe, send an empty email to <a
moz-do-not-send="true"
href="mailto:eeglablist-unsubscribe@sccn.ucsd.edu"
target="_blank">eeglablist-unsubscribe@sccn.uc<wbr>sd.edu</a><br>
For digest mode, send an email with the subject
"set digest mime" to <a moz-do-not-send="true"
href="mailto:eeglablist-request@sccn.ucsd.edu"
target="_blank">eeglablist-request@sccn.ucsd.e<wbr>du</a><br>
</blockquote>
</div>
<br>
</div>
</div>
</div>
</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>
</div>
<br>
<fieldset class="mimeAttachmentHeader"></fieldset>
<br>
<pre wrap="">_______________________________________________
Eeglablist page: <a class="moz-txt-link-freetext" href="http://sccn.ucsd.edu/eeglab/eeglabmail.html">http://sccn.ucsd.edu/eeglab/eeglabmail.html</a>
To unsubscribe, send an empty email to <a class="moz-txt-link-abbreviated" href="mailto:eeglablist-unsubscribe@sccn.ucsd.edu">eeglablist-unsubscribe@sccn.ucsd.edu</a>
For digest mode, send an email with the subject "set digest mime" to <a class="moz-txt-link-abbreviated" href="mailto:eeglablist-request@sccn.ucsd.edu">eeglablist-request@sccn.ucsd.edu</a></pre>
</blockquote>
<p><br>
</p>
</body>
</html>