plot_topomap() with arbitrary values (e.g., t-values, BFs)

Dear all,

I am running a MEG experiment and analysing the data with MVPA (decoding) and I am trying to compare two groups of participants. More precisely, I would like to compare the spatial patterns (contribution of sensors to decoding) across time. I have retrieved the spatial patterns for each participant, and then computed Bayes factors (BFs) to test for groups differences at each time step and sensor.

I have tried visualising these BFs using plot_topomap() with:

x_times = np.arange(0.050, 0.750, 0.100)

    ts_args=dict(time_unit="s", spatial_colors=True, gfp=False),

which works well, except that I am not sure what is actually plotted. From the colorbar, it seems that the scale does not correspond to the BF values I have stored in evoked_time_gen_difference_BF.

Hence my question, is it possible to plot arbitrary values with plot_topomap()? I understand that the β€œpick” argument leads to different underlying computations, but I just want to plot the actual values present in evoked_time_gen_difference_BF, without any transformation.

Thank you for your help!



Just an idea, but by default scalings are applied to the data depending on the channel type when plotting topomaps in Evoked objects, e.g. 1e15 for magnetometers.

What happens if you add scalings=dict(mag=1)) to topomap_args?

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adding scalings=dict(mag/grad=1)) seems to work indeed, thank you very much!


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