Time-frequency analysis, questions

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Dear MNE users,
I am new to this field so I am sorry if the answer to my question is
obvious.
I need to do a time-frequency analysis and I am following this tutorial
https://mne.tools/dev/auto_examples/time_frequency/plot_source_label_time_frequency.html#sphx-glr-auto-examples-time-frequency-plot-source-label-time-frequency-py
as I am interested in obtaining this sort of maps in certain labels.
However, the maps I am getting are definetely not normalized compared to
the examples (see my examples attached - lower than 10Hz).
1. What might be the reason for this?Is there something special I need to
do when computing inverse solution compared to typical evoked response
analysis? (I include only noise covariance there)
2. What is the recommend parameter here for baseline_mode (percent,
logration mean - is there any suggestion on what to use and when?)
3. In this tutorial and command source_induced_power, what is the method
used? Is it Morlet / Multitaper or something else? I found no information
on this in contrast to e.g. tfr_morlet commands used elsewhere?

Thank you in advance.
Sincerely yours,
Kirill Elin, PhD

<https://mne.tools/dev/generated/mne.minimum_norm.source_induced_power.html#mne.minimum_norm.source_induced_power>
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Dear Kirill,

looking at your plots you have clear edge artifacts. The function you use
to do not try to correct for them automatically. You should crop your outputs
in time and baseline afterwards.

For pow I tend to baseline with a ratio between power during stim
with power during baseline.

Now to be honest this code is fairly old in MNE so maybe someone
can suggest you a simpler route.

I like this example if you are especially interested in power:

https://mne.tools/stable/auto_examples/inverse/plot_evoked_ers_source_power.html?highlight=power

HTH
Alex

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Dear Dr. Gramfort,
So, what is the recommended MNE code / functions to get rid of the edge
artifacts and correct for other issues after I have obtained time-frequency
plots? (I am interested in the source level data)
So if source_induced_power() function does not automatically correct edge
artificats, why it is achieved by parameters baseline, baseline_mode there?
It is already some correction, I would assume.
Thank you in advance.
Sincerely yours,
Kirill Elin, PhD

??, 21 ??? 2020 ?. ? 22:42, Alexandre Gramfort <alexandre.gramfort at inria.fr

:

        External Email - Use Caution

Dear Kirill,

looking at your plots you have clear edge artifacts. The function you use
to do not try to correct for them automatically. You should crop your
outputs
in time and baseline afterwards.

For pow I tend to baseline with a ratio between power during stim
with power during baseline.

Now to be honest this code is fairly old in MNE so maybe someone
can suggest you a simpler route.

I like this example if you are especially interested in power:

Page Redirection

HTH
Alex

> I need to do a time-frequency analysis and I am following this tutorial
Page Redirection
as I am interested in obtaining this sort of maps in certain labels.
However, the maps I am getting are definetely not normalized compared to
the examples (see my examples attached - lower than 10Hz).
> 1. What might be the reason for this?Is there something special I need
to do when computing inverse solution compared to typical evoked response
analysis? (I include only noise covariance there)
> 2. What is the recommend parameter here for baseline_mode (percent,
logration mean - is there any suggestion on what to use and when?)
> 3. In this tutorial and command source_induced_power, what is the method
used? Is it Morlet / Multitaper or something else? I found no information
on this in contrast to e.g. tfr_morlet commands used elsewhere?
>
> Thank you in advance.
> Sincerely yours,
> Kirill Elin, PhD
>
>
> _______________________________________________
> Mne_analysis mailing list
> Mne_analysis at nmr.mgh.harvard.edu
> Mne_analysis Info Page

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hi Kirill,

I would suggest to try first the other approach I proposed to get power maps.

https://mne.tools/stable/auto_examples/inverse/plot_evoked_ers_source_power.html?highlight=power

using source_induced_power requires you to manipulate quite low level
code. You would need to slice the numpy array and a time array accordingly.

Alex