I recommend that you should start with the empty room noise rather than
the participant noise.
Giorgos
On 10/25/2016 12:56 AM, mehdy dousty wrote:
Thanks for your answer. as it is resting state I can have 1 trial or
keep the trial existed in the pre-processed signals,147trails, Based
on the paper by Sylvain Baillet with title of "The brain resting-state
activity is shaped by synchronized cross-frequency coupling of neural
oscillations" the covariance matrix is empirically estimated by
empty-room recording, therefore as the room noise and participant
noise is provided by HCP I don't know which level of noise do I need
to take, or I don't need to consider that and just simply did what you
have proposed.
Thanks
On Mon, Oct 24, 2016 at 4:40 PM Georgios Michalareas
<giorgos.michalar...@esi-frankfurt.de
<mailto:giorgos.michalar...@esi-frankfurt.de>> wrote:
Hi ,
I think you can still do it with ft_timelockanalysis.
I assume that the resting state data you mention has only one trial.
then if you just do:
cfg=[];
cfg.covariance='yes';
pseudoavg=ft_timelockanalysis(cfg, data);
then it will just compute the average across trials but as there
is only
one trial it will basically produce the exact same data.
But in the output of timelockanalysis you ll find a field .cov whith
dimension Nchannels x Nchannels which contains the covariance
matrix of
the MEG sensors .
This covariance has been computed from the entire length of the single
resting state trial , so I think this is what you are interested in.
I hope this helps.
Best
Giorgos
On 10/24/2016 10:42 PM, mehdy dousty wrote:
> Hello,
> I am using HCP MEG resting state to compute source localization.
As it
> is resting state signal and I am using Fieldtirp I have no idea
how to
> compute the noise covariance matrix. Mostly this matrix is computed
> with ft_timelockanalysis with the window of [-inf 0] in
task-based MEG
> I really appreciate if anybody can help me.
> Thanks
> Mehdy
>
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