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)
evoked_time_gen_difference_BF.plot_joint(
times=x_times,
title="TTS-Control",
picks="mag",
ts_args=dict(time_unit="s", spatial_colors=True, gfp=False),
topomap_args=dict(time_unit="s")
)
```

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 βpickβ 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!

Ladislas