[Eeglablist] single trial phase locking

[Tim Mullen]

Hi,

I have not implemented any PLV code in SIFT (yet). However, SIFT
implements methods for computing time-varying spectra, coherence,
partial coherence, or multivariate Granger-causal quantities on
multiple or single-trial data using Vector Autoregressive models. You
can obtain these quantities in the frequency-domain for single-trial
data using either overlapping sliding windows and a standard VAR model
(pop_est_fitMVAR.m) or for each time point individually using a Kalman
filter (est_fitMVARKalman.m). If you are interested in driving/driver
or *effective* (causal) connectivity relationships then the GC is
generally more appropriate and interpretable than PLV or coherence
(e.g., dDTF or PDC or Granger-Geweke causality for
bivariate/trivariate systems).

I don't see how the epoching workaround proposed by Michiel for
computing sliding-window PLV using ITC would work since ITC is not the
same as the bivariate PLV. ITC quantifies the phase-locking for a
single channel across event-locked trials, but will not tell you
whether two channels are phase-locked w.r.t. each other.

I have on my TODO list to contribute a PLV function for EEGLAB, and
when I do get to this (perhaps sooner than later), I'll make sure to
include single-trial capabilities...otherwise, if anyone wants to step
to the plate, that's cool too.

In the meantime, you might take a look at SIFT for getting those
time-varying single-trial coherence or GC estimates...

Tim




On Mon, Apr 18, 2011 at 11:11 AM, Arnaud Delorme <arno at ucsd.edu> wrote:
> Dear Michiel,
>
> that's fantastic. Is there any way, you could wrap your code up into an EEGLAB plugin. It is pretty easy when you use the file templates and I am sure a lot of users would appreciate.
>
> http://sccn.ucsd.edu/wiki/A07:_Contributing_to_EEGLAB
>
> Also Scott Makeig pointed out to me that Tim Mullen has recently implemented a similar (if not identical) method to compute coherence on continuous data and the method is already available in the SIFT toolbox and described in an upcoming IEEE paper.
>
> http://sccn.ucsd.edu/wiki/SIFT
>
> Best regards,
>
> Arno
>
>
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