Hi Mengting,
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).
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.
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).
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.
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.
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.
regards,
Marijn.
Hi Mengting,
So as I understand, reference is only a required step for source
localization,
The _average_ reference is required for source localisation
but not any other steps like measuring the connectivity between brain
regions.
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?.
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.
Sounds reference removed the DC componets but remains the oscillation. If
so, does it influence the power in DC components?
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.
Also, does it matter the conventional source localization methods such as
dipole fitting?
All localisation methods require the average reference, otherwise the
recorded and predicted (forward model) data cannot be compared.
Best,
Chris
Hi Chris,
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?
Really appreciate for help,
Mengting
2017-09-25 4:55 GMT-04:00 Christopher Bailey <cjb at cfin.au.dk>:
Hi Mengting,
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
- have measured (EEG) data
- build forward model that computes sensor-level readings for know sources
- compare measured and predicted (forward-projected) data to each other
- minimise prediction error under chosen prior/model
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 *does not depend on the location of the on-line reference
electrode* (as long as it was functioning properly). After this
re-scaling, the values can be compared directly.
Note that like any (proper) re-referencing procedure, taking the common
average only shifts the zero-point; relative differences between electrode
readings remain unaltered.
/Chris
Hello MNE users,
I noticed that in MNE inverse operations, all EEG data were forced by an
average referencing.
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)?
Thanks,
Mengting
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