[Mne_analysis] phase locking analysis with MNE

[Matti Hamalainen]

On Sep 16, 2010, at 3:20 PM, Ghuman, Avniel (NIH/NIMH) [F] wrote:

> Hi All,
>
> PLV does in fact depend on the number of trials because it follows  
> the Raleigh distribution.  Think of this like a chi-square  
> distribution.  The mean/peak of the distribution will get closer to  
> 0 as there are more trials, so your result is exactly what you would  
> expect.  It does not have anything to do with the SNR however.
>
> For an example of this, calculate the PLV for random data with  
> different numbers of trials.  You will see the PLV change.

Hi all,

So my guess was wrong. The obvious remedy then to pick N = 50 random  
trials from the N = 100 condition to make the two comparable, right?

- Matti

>
> Avniel
>
>
> On 9/16/10 3:15 PM, "Matti Hamalainen" <msh at nmr.mgh.harvard.edu>  
> wrote:
>
>
>
> On Sep 16, 2010, at 2:53 PM, Elisabeth Fonteneau wrote:
>
>> Hi All,
>>
>> I am running a phase-locking analysis on MEG/EEG combined source
>> data on the single trial level.
>>
>> And I have a quick question for this mailing list
>>
>> I was wondering whether the number of trials used for computing the
>> Phase Lock Value (PLV) will affect the synchrony results?
>>
>> I meant if I am comparing 2 conditions with different numbers of
>> trials in each (let's say N=50 vs N=100) do you think that the
>> synchrony will change because of different signal to noise ratio?
>>
>> I am asking this because I have done such comparison, and strangely
>> this is the condition with the less number of items that is showing
>> increasing synchrony compared to the condition with the larger
>> number (in the gamma band).
>>
>> It will be very helpful if somebody already came across this issue,
>> and/or if you can point me toward a relevant paper discussing this
>> problem.
>>
>> Thx to all for reading!
>
> To my understanding, the PLV should not depend on the number of
> trials. However, here are two tests you could do:
>
> 1. Pick N = 50 trials from the N = 100 condition a few times and check
> whether the PLV is the same as when using all the trials.
>
> 2. Using bootstrapping, you could calculate the variability
> (confidence intervals) of PLV in each condition to see whether this
> difference is true or just due to the variability in the data.
>
> - Matti
>
>
>
>
> ---------
>
> Matti Hamalainen, Ph.D.
> Athinoula A. Martinos Center for Biomedical Imaging
> Massachusetts General Hospital
>
> msh at nmr.mgh.harvard.edu
>
>
>
>
>
>
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---------

Matti Hamalainen, Ph.D.
Athinoula A. Martinos Center for Biomedical Imaging
Massachusetts General Hospital

msh at nmr.mgh.harvard.edu






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