[Mne_analysis] EEG reference

[Liu Mengting]

Hey Marijn,

Many thanks for this very basic but really important message, I think it
makes me feel much better why select average reference.
Thanks,

Mengting

2017-09-28 3:54 GMT-04:00 Marijn van Vliet <w.m.vanvliet at gmail.com>:

> 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.
>
>
>
> On 09/28/2017 09:58 AM, Christopher Bailey wrote:
>
> 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
>
> On 27 Sep 2017, at 20.54, Liu Mengting <bigting84 at gmail.com> wrote:
>
> 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
>>
>> On 24 Sep 2017, at 01.28, Liu Mengting <bigting84 at gmail.com> wrote:
>>
>> 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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