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<div class="moz-cite-prefix">Hi Marius,<br>
<br>
No, I have not officially released it yet. I'm still collecting
more labels and updating the working version of the classifier.
For the moment, contributing to reaching.ucsd.edu would be
helpful. I'll definitely post to the list when there is a trial
version of the classifier (likely in the form of an EEGLAB
plugin).<br>
<br>
Thanks,<br>
Luca<br>
<br>
On 07/10/2017 02:47 AM, Marius Klug wrote:<br>
</div>
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<div>Dear Makoto, Luca, all,<br>
<br>
</div>
sorry for the late reply! As a quick explanation from my
side, I know very well that the human eye (or brain) is the
best classifier system we have at the moment and I do the IC
selection manually as well, since I've also tested many
algorithms and found them to be insufficient. I just wanted
to point out that there are _some_ options, however limited
in their performance, that attempt the automatic
classification and if the reviewer insist, one can point
those out. The benefit here would be that for example with
SASICA one can use the pre-made classification and do a
manual inspection in addition based on the measures computed
by SASICA, as the authors have suggested themselves. It's a
helper tool for the not-so-experienced user that also might
help the paper get through reviewing.<br>
<br>
</div>
Also I know of Luca's project but if I'm not mistaken it's not
out there to be used, is it? I would be more than wiling to
test it extensively since, as I've said, increasing the level
of reproducibility is one of my personal goals in my analysis
pipeline! <br>
<br>
</div>
Marius<br>
</div>
<div class="gmail_extra"><br>
<div class="gmail_quote">2017-07-03 19:31 GMT+02:00 Luca
Pion-Tonachini <span dir="ltr"><<a moz-do-not-send="true"
href="mailto:lpionton@ucsd.edu" target="_blank">lpionton@ucsd.edu</a>></span>:<br>
<blockquote class="gmail_quote" style="margin:0 0 0
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<div class="m_4371215099913305928moz-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.<span class="HOEnZb"><font color="#888888"><br>
<br>
Luca</font></span>
<div>
<div class="h5"><br>
<br>
On 06/30/2017 05:52 PM, Makoto Miyakoshi wrote:<br>
</div>
</div>
</div>
<div>
<div class="h5">
<blockquote type="cite">
<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"
target="_blank">http://reaching.ucsd.edu:8000/<wbr>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>
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</div>
<div class="m_4371215099913305928HOEnZb">
<div class="m_4371215099913305928h5">
<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
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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_4371215099913305928m_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>
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-- <br>
<div
class="m_4371215099913305928m_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>
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<br>
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-- <br>
<div class="m_4371215099913305928gmail_signature"
data-smartmail="gmail_signature">
<div dir="ltr">Makoto Miyakoshi<br>
Swartz Center for Computational Neuroscience<br>
Institute for Neural Computation, University
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