[Mne_analysis] EEG reference
Marijn van Vliet
w.m.vanvliet at gmail.com
Thu Sep 28 03:54:16 EDT 2017
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
>> <mailto: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
>> <mailto: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
>>> <mailto: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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