Mask-enabled plot_topomap for TFR's and plot_compare_evoked for TFR's

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Hi Alex,

I am expecting to see something like the picture shown below, while we
use the plot_topomap for power (TFR). I want to see the significant sensors
highlighted. As per my knowledge, this is possible when we pass the
parameter "mask" which is not possible when we use plot_topomap on Power
Is there any way to plot only the significant sensors when we use
plot_topomap on TFR's?
[image: image.png]

Thank you for providing the alternate solution for the second question. It
would be nice, if there was a API to plot TFR's like we have for evoked

Thank You

hi Pooja,

I am trying to apply spatio-temporal clustering to the averaged TFR values.

* I am following the same steps as shown in the example:

*>* only difference is i am trying the steps on TFR's instead of evoked.
*>* I faced two difficulties during execution:
*>* 1. When we do the plot_topomap for TFR's, there is no "mask" parameter,
*>* because of which I was unable to display the significant sensors only (like
*>* shown in the MNE example). Is there any way to plot only the significant
*>* sensors using plot_topomap when used on TFR's?
I am not sure what you expect to see.

* 2. To plot the power (TFR's) pertaining to different conditions I used

*>* plot_compare-evoked, for which I created a dummy EvokedArray (using
*>* mne.EvokedArray) and placed the power data for each condition. Plot showed
*>* up the values in 1e15 range whereas the actual power values are in 1e-1
*>* range. Is there any way we can plot the power like we plot the evoked using
*>* the single function? Or am I doing something wrong?
mne plot functions use the channel types to scale the data during plotting.

maybe you can write your own plotting function using just matplotlib?


* It will be of great help, if you can suggest to me the solution.

*>* Thank You
*>>* --
*>* Thank You
*>* Pooja Prabhu