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<p>Hi Mengting,</p>
<p>let me chime in here. Whenever you are doing EEG analysis, the
choice of reference is important. EEG measures voltage, which is
the difference in electric potential between two points. This
means, there's technically no such thing as "the voltage at
electrode Cz", there is only "the difference in electric potential
between electrodes Cz and the one I stuck on the nose" (or
whatever reference electrode you used, could be CMS).</p>
<p>After the recording, it is easy to "re-reference" the data, which
is choosing a difference electrode as a reference. For example, to
move from the nose reference to channel Pz as reference, you can
just do: Cz = Cz - Pz and you have a voltage at the Cz channel
with Pz as reference.</p>
<p>You can imagine your data often looks completely different,
depending on the reference you choose. If you choose Pz as a
reference, the signal at Pz will be 0 (Pz - Pz) and all channels
surrounding Pz only have a very small signal. If Pz contained
oscillatory activity (maybe alpha rhythms), you would see alpha
power at every electrode *except* surrounding Pz. For example you
would see "negative" alpha at Fpz (remember, we're looking at Fpz
- Pz).</p>
<p>So maybe Pz is not a very useful reference. We would like to have
some "neutral" reference that doesn't contain signals of interest.
This is why nose and mastoids are popular locations for the
reference. But there's another option: choose the average of all
electrodes as "virtual reference". With this reference, at any
time, mean(Cz, Pz, ...) = 0. This is the reference assumed by
forward models, which is why when doing anything that involves the
forward model (source localization, connectivity analysis at the
source level, etc.) your data needs to be in this reference.</p>
<p>One thing you need to be aware of when re-reference to an average
reference is that the reference is, you know, the average of all
electrodes. If one of the electrodes is noisy (maybe it got loose
during the experiment), this ruins the reference signal, and since
the reference is subtracted from all sensors, it ruins the signal
at *all* sensors.</p>
<p>So, first use a sensors which you know for sure has a good signal
as a reference (preferably a "neutral" reference like the noise,
mastoids or earlobe), then inspect the data to spot all sensors
that have problems. Either remove them from the data or have
MNE-Python reconstruct the signal at the bad sensor by
interpolating the neighbouring sensors. Then, when the data is
squeaky clean, move to the average reference.<br>
</p>
<p>regards,<br>
Marijn.</p>
<p><br>
</p>
<br>
<div class="moz-cite-prefix">On 09/28/2017 09:58 AM, Christopher
Bailey wrote:<br>
</div>
<blockquote type="cite"
cite="mid:5F613C19-3E8E-4F83-9FC4-2D8D5E338DDA@cfin.au.dk">
<meta http-equiv="Content-Type" content="text/html; charset=utf-8">
Hi Mengting,
<div class=""><br class="">
</div>
<div class="">
<blockquote type="cite" class="">
<div dir="ltr" class="">
<div class="">So as I understand, reference is only a
required step for source localization,</div>
</div>
</blockquote>
</div>
<div class=""><br class="">
</div>
<div class="">The _average_ reference is required for source
localisation</div>
<div class=""><br class="">
</div>
<div class="">
<blockquote type="cite" class="">
<div dir="ltr" class="">
<div class="">but not any other steps like measuring the
connectivity between brain regions. </div>
</div>
</blockquote>
</div>
<div class=""><br class="">
</div>
<div class="">I’m not sure I understand what you mean by “brain
regions” here. If you’re going to measure connectivity in source
space, your need an (inverse) operator of some sort to project
your electrode-data “into the brain”.</div>
<div class=""><br class="">
</div>
<div class="">If you want to do sensor-level connectivity
calculations on EEG data, the effect of the reference might
depend on the connectivity metric you use. For those
time/frequency-domain measures I’m aware of (but have little
first-hand experience with), it doesn’t matter which reference
your data is in.</div>
<div class=""><br class="">
</div>
<div class="">
<blockquote type="cite" class="">
<div dir="ltr" class="">
<div class="">Sounds reference removed the DC componets but
remains the oscillation. If so, does it influence the
power in DC components? </div>
</div>
</blockquote>
<br class="">
</div>
<div class="">Yes and no. Relative differences within and between
channels remain the same in any (proper) reference. Note though
that the time-domain is not involved in (re-)referencing, so
possible ‘DC components’ still remain in the data.</div>
<div class=""><br class="">
</div>
<div class="">
<blockquote type="cite" class="">
<div dir="ltr" class="">
<div class=""> Also, does it matter the conventional source
localization methods such as dipole fitting?</div>
</div>
</blockquote>
<br class="">
</div>
<div class="">All localisation methods require the average
reference, otherwise the recorded and predicted (forward model)
data cannot be compared.</div>
<div class=""><br class="">
</div>
<div class="">Best,</div>
<div class=""><br class="">
</div>
<div class="">Chris</div>
<div class=""><br class="">
<div>
<blockquote type="cite" class="">
<div class="">On 27 Sep 2017, at 20.54, Liu Mengting <<a
href="mailto:bigting84@gmail.com" class=""
moz-do-not-send="true">bigting84@gmail.com</a>>
wrote:</div>
<br class="Apple-interchange-newline">
<div class="">
<div dir="ltr" class="">Hi Chris,
<div class=""><br class="">
</div>
<div class="">Thanks for the info, this really helps in
understanding the whole procedures. So as I
understand, reference is only a required step for
source localization, but not any other steps like
measuring the connectivity between brain regions.
Sounds reference removed the DC componets but remains
the oscillation. If so, does it influence the power in
DC components? Also, does it matter the conventional
source localization methods such as dipole fitting?</div>
<div class=""><br class="">
</div>
<div class="">Really appreciate for help,</div>
<div class=""><br class="">
</div>
<div class="">Mengting</div>
</div>
<div class="gmail_extra"><br class="">
<div class="gmail_quote">2017-09-25 4:55 GMT-04:00
Christopher Bailey <span dir="ltr" class="">
<<a href="mailto:cjb@cfin.au.dk" target="_blank"
class="" moz-do-not-send="true">cjb@cfin.au.dk</a>></span>:<br
class="">
<blockquote class="gmail_quote" style="margin:0 0 0
.8ex;border-left:1px #ccc solid;padding-left:1ex">
<div style="word-wrap:break-word" class="">Hi
Mengting,
<div class=""><br class="">
</div>
<div class="">The average reference is the only
valid scheme when performing ‘source
localisation’, no matter what you do afterwards.
To first order, the localisation procedure is</div>
<div class=""><br class="">
</div>
<div class="">- have measured (EEG) data</div>
<div class="">- build forward model that computes
sensor-level readings for know sources</div>
<div class="">- compare measured and predicted
(forward-projected) data to each other</div>
<div class="">- minimise prediction error under
chosen prior/model</div>
<div class=""><br class="">
</div>
<div class="">The predicted data are calculated
relative to a hypothetical absolute reference
potential of zero at infinity, whereas a real
dataset could be referenced to a number of
points on the head. To be able to compare the
two datasets, both are re-referenced to their
respective average: the average reference <i
class="">does not depend on the location of
the on-line reference electrode</i> (as long
as it was functioning properly). After this
re-scaling, the values can be compared directly.</div>
<div class=""><br class="">
</div>
<div class="">Note that like any (proper)
re-referencing procedure, taking the common
average only shifts the zero-point; relative
differences between electrode readings remain
unaltered.</div>
<div class=""><br class="">
</div>
<div class="">/Chris</div>
<div class=""><br class="">
</div>
<div class="">
<div class="">
<blockquote type="cite" class="">
<div class="">
<div class="h5">
<div class="">On 24 Sep 2017, at 01.28,
Liu Mengting <<a
href="mailto:bigting84@gmail.com"
target="_blank" class=""
moz-do-not-send="true">bigting84@gmail.com</a>>
wrote:</div>
<br
class="m_7606626925763248917Apple-interchange-newline">
</div>
</div>
<div class="">
<div class="">
<div class="h5">
<div dir="ltr" class="">Hello MNE
users,
<div class=""><br class="">
</div>
<div class="">I noticed that in MNE
inverse operations, all EEG data
were forced by an average
referencing. </div>
<div class="">Does anyone has
insight about how does the average
reference influence the inverse
operations in MNE (I mean use dSPM
or sLoreta)? Especially would this
average referencing influence
functional connectivity measure in
source space (e.g. using phase
locking)?</div>
<div class=""><br class="">
</div>
<div class="">Thanks,</div>
<div class="">Mengting</div>
</div>
</div>
</div>
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