Repository: mne-tools/mne-python

Issue:
Possible (small) errors in ERDS maps example
I just noticed the following two issues in our [ERDS maps example](https://mne.tools/stable/auto_examples/time_frequency/time_frequency_erds.html):

1. The last two plots include "ERDS (%)" as their y axes labels. I think this is not correct, since the ERDS values are not in %. There are two options: we can either convert ERDS values to %, or drop the "(%)" from the labels.
2. I am not sure why the last plot shows negative values up to about -2, because the minimum possible ERD value is -1 (-100%, meaning power during activity completely disappeared as compared to baseline). Is this a visualization artifact of the violin plots?

@eioe do you have time to take a look?


Discussion:
Hi @cbrnr , 
thanks for spotting these. I will try to take a look tonight or tomorrow. 
If I'm too slow to get to a satisfying solution, I'd write here again as I'm then off for 4 weeks of vacation (starting tomorrow evening). 
Qucik question though:

I think my reasoning/labelling was based on this line:
https://github.com/mne-tools/mne-python/blob/6fe2e1322afb6ae5f6816f05d8f85dd78678e1aa/examples/time_frequency/time_frequency_erds.py#L105

Doesn't that express the values in %-change re. the baseline? 
Good point! It seems like this is a bit of a misnomer, because according to the [docs](https://mne.tools/stable/generated/mne.time_frequency.EpochsTFR.html#mne.time_frequency.EpochsTFR.apply_baseline), this mode performs "subtracting the mean of baseline values followed by dividing by the mean of baseline values (‘percent’)". It doesn't say that values are multiplied by 100%, which you can also see [in the source](https://github.com/mne-tools/mne-python/blob/maint/1.5/mne/baseline.py#L118-L122).
@cbrnr , thanks a lot for the clarification. I read that in the docs, but did not pay enough attention to realize that we're missing the multiplication with 100. Indeed, a bit misleading naming. 
Re. your first point: any of the two mentioned options sounds reasonable to me; with a slight preference for multiplying the values with 100 and keeping the "%" in the label, so that we have some kind of a unit on the axis label. What do you think?

Re. 2: Setting the [parameter `cut=0` in the call  of `sns.violinplot`](https://seaborn.pydata.org/generated/seaborn.violinplot.html) should yield the wanted behavior (e.g., in [line 210 here](https://github.com/mne-tools/mne-python/blob/6fe2e1322afb6ae5f6816f05d8f85dd78678e1aa/examples/time_frequency/time_frequency_erds.py#L210)). This is how it looks:

![image](https://github.com/mne-tools/mne-python/assets/20844835/da3e7e1d-78c5-4cc8-a933-b5a37ac344ff)


unfortunately, I did not find enough time today to set up a mne-dev environment on this machine here, so i could create a PR. And we are about to leave for our vacation. 
If it is ok for yout, I'm thankful if you go ahead and fix/adapt it. Or – if you prefer – I am happy to do it once I'm back, but that will only be mid september. Sorry, unfortunate timing.

Thanks anyways & very cool that you spotted this. 🙌 

Resolving patch:
diff --git a/examples/time_frequency/time_frequency_erds.py b/examples/time_frequency/time_frequency_erds.py
--- a/examples/time_frequency/time_frequency_erds.py
+++ b/examples/time_frequency/time_frequency_erds.py
@@ -181,7 +181,7 @@
 g.map(plt.axhline, y=0, **axline_kw)
 g.map(plt.axvline, x=0, **axline_kw)
 g.set(ylim=(None, 1.5))
-g.set_axis_labels("Time (s)", "ERDS (%)")
+g.set_axis_labels("Time (s)", "ERDS")
 g.set_titles(col_template="{col_name}", row_template="{row_name}")
 g.add_legend(ncol=2, loc="lower center")
 g.fig.subplots_adjust(left=0.1, right=0.9, top=0.9, bottom=0.08)
@@ -208,6 +208,7 @@
     "value",
     "band",
     n_boot=10,
+    cut=0,
     palette="deep",
     order=["C3", "Cz", "C4"],
     hue_order=freq_bands_of_interest,
@@ -215,7 +216,7 @@
 ).add_legend(ncol=4, loc="lower center")
 
 g.map(plt.axhline, **axline_kw)
-g.set_axis_labels("", "ERDS (%)")
+g.set_axis_labels("", "ERDS")
 g.set_titles(col_template="{col_name}", row_template="{row_name}")
 g.fig.subplots_adjust(left=0.1, right=0.9, top=0.9, bottom=0.3)
 


Test patch: