I think some combination of this and sensor-plotting-on-brain would probably be the best. But this at least is maybe an incremental step

https://21939-1301584-gh.circle-artifacts.com/0/dev/auto_tutorials/misc/plot_ecog.html#sphx-glr-auto-tutorials-misc-plot-ecog-py

Wow this is awesome! you are a magician @larsoner.

I think for me I haven't seen these kinds of mne-visualizations yet except for MEG, so the only issue I have is interpreting the cool viz. So correct me where I'm wrong. You are visualizing the gamma band activity mapped to the closest parietal lobe on both hemispheres (in this case it's the left hemisphere), at 0.680 seconds. There is a small window where you are smoothing with a factor of 10.

I might be asking a stupid question, but what is being shown in the bottom time series plot? not sure what GFP stands for and the other things in the legend.

I think for me I haven't seen these kinds of mne-visualizations yet except for MEG,

The real question to me is, is the method used for projecting the data to the brain sensible for ECoG? It's pretty basic, doesn't use forward/inverse at all... But I imagine this is what people mentally try to do when they see electrodes on the brain anyway. Hence why I think this plus an actual sensor overlay would be potentially very useful.

You are visualizing the gamma band activity mapped to the closest parietal lobe

The Notes say this, does it clarify it for you?

This function projects the ECoG sensors to the pial surface, then the
activation at each pial surface vertex is given by the sum of the
activation from each sensor. Activation is weighted by the fractional
``distance`` from the sensor, such that the vertex where the sensor
projects gets weight 1 and a sensor at ``distance`` meters away (or
larger) gets weight 0.

There is a small window where you are smoothing with a factor of 10.

Actually the smoothing here has no effect. It's spatial upsampling which is not used for these data.

but what is being shown in the bottom time series plot? not sure what GFP stands for and the other things in the legend.

It's the time course of the vertex shown by the ball, in this case the time course of the gamma activation. GFP is global field power. You can try running the example locally to play with the options and clicking around, playing with spacebar, etc.

Ah okay makes a bit more sense! So just to clarify is this the projected activity of all the channels on the brain? The time course on the bottom shows that for just one region (i.e. the blue ball plotted onto the brain). If say someone wants to only visualize the effect from a subset of channels, then one simply needs to "pick_channels" right?

Do I think this is useful?

So the heatmap on the pial surface is a weighted (by distance) linear combination of all the channel's gamma activity. I think that is pretty useful because there are many recordings that are ECoG arrays on the brain cortex, and many publications essentially produce (crappier) versions of what you have here. The cool thing I can see is that any "metric" or feature can be visualized as such following the example. I would definitely use it.

The future of iEEG though with epilepsy surgery is moving towards SEEG (so depth electrodes), which obviously then become way harder to visualize. But one step at a time :p. This looks so cool!

So just to clarify is this the projected activity of all the channels on the brain?

Each colorful point on the brain is the result of summing together channels that are (or really, the version of them projected to the nearest point on the pial surface is) within distance of that point, weighted as 1. if they have 0 distance and 0. if they have distance (or greater) distance. So any colorful point is the weighted sum of 1 or more electrodes, but generally it's not going to be more than probably a couple of channels.

The time course on the bottom shows that for just one region (i.e. the blue ball plotted onto the brain). If say someone wants to only visualize the effect from a subset of channels, then one simply needs to "pick_channels" right?

Yes indeed, do this before stc.plot and it should plot the subset. Or just set your distance less than your minimum electrode spacing, then you'll see the individual points on the brain, and can click on them to make their time-courses be plotted, etc.

The future of iEEG though with epilepsy surgery is moving towards SEEG (so depth electrodes), which obviously then become way harder to visualize. But one step at a time :p. This looks so cool!

Hopefully not too bad -- we have translucent volumetric rendering already:

https://mne.tools/dev/auto_tutorials/source-modeling/plot_beamformer_lcmv.html#volumetric-rendering-3d

sEEG should be fairly easy by comparison :) We can for example, when we add the sensor plots, make sEEG electrodes translucent spheres. And/or we figure out some sensible way to interpolate/map the sEEG data into a volumetric space, then the viz is already done actually. Again I think a combination would probably be best...

I would definitely use it.

Okay let's wait to see if @agramfort thinks it makes sense, too -- if so, I'll add tests and we can pick a name then we should be good to go.

Actually also @choldgraf if you have an opinion on this PR/viz method it would be great!

• Pick a better name for the function

For naming, I wonder if it should be explicit that this is for the pial surface of the brain? Also... I might be asking another stupid question, but what is stc stand for? I couldn't find anything from using mne documentation search. It seems to be a file type that contains source reconstructions?

Tests added, ready for review/merge from my end

For naming, I wonder if it should be explicit that this is for the pial surface of the brain?

In theory at some point we might want to make the surface to project to optional, so I'd rather leave it. I actually can live with stc_near_sensors I guess.

what is stc stand for? I couldn't find anything from using mne documentation search

Argh, our search needs to work a bit better! See:

https://mne.tools/stable/glossary.html#term-source-estimates-abbr.-stc

For naming, I wonder if it should be explicit that this is for the pial surface of the brain?

In theory at some point we might want to make the surface to project to optional, so I'd rather leave it. I actually can live with stc_near_sensors I guess.

What about something to inform ECoG vs SEEG? Because to allude to what you said about SEEG: would we have an analagous function for doing the volumetric rendering of SEEG?

Hopefully not too bad -- we have translucent volumetric rendering already:

https://mne.tools/dev/auto_tutorials/source-modeling/plot_beamformer_lcmv.html#volumetric-rendering-3d

sEEG should be fairly easy by comparison :) We can for example, when we add the sensor plots, make sEEG electrodes translucent spheres. And/or we figure out some sensible way to interpolate/map the sEEG data into a volumetric space, then the viz is already done actually. Again I think a combination would probably be best...

Whoa, I wasn't aware of this! This looks pretty awesome too... I'm assuming there should be some way of getting the data structures to work w/ arbitrary SEEG feature signals (e.g. either the raw LFP, or some say spectral power, etc.), so that it can plot things in this volumetric rendering? If not, I can open another issue to discuss? Sorry if it's obvious and can :p. I'm just not as familiar w/ the data structures outside Raw and Epochs because the other things always seem to be in the realm of source loc.

If it can do so, perhaps I'll try it on a future patient and see if I run into any troubles.

SEEG: would we have an analagous function for doing the volumetric rendering of SEEG?

Yeah we should be able to project sEEG electrode activation into a volume without too much work. Probably about the same number of lines as here.

I'm assuming there should be some way of getting the data structures to work w/ arbitrary SEEG feature signals (e.g. either the raw LFP, or some say spectral power, etc.), so that it can plot things in this volumetric rendering

Yep, just get those signals then put them into an EvokedArray (if they aren't already in an Evoked) with a corresponding info as done in the example here.

My 2 cents is that this is a super common visualization in ECoG / sEEG, although it has sort-of dubious physiological truth (most folks just assume there's a spherical activation around each electrode, some other kind of heuristic like that, rather than actual physiological modeling etc). However since this is so common I think it'd be a good feature for the ECoG/sEEG community!

In that case this is ready for review/merge @agramfort . Failure is just 3.8 pre segfault that's happening on master

this is a super common visualization in ECoG / sEEG

@choldgraf any reference papers for "each electrode just projects to a sphere of radius R"? @agramfort won't accept this viz without seeing it in use in the community

this is a super common visualization in ECoG / sEEG

@choldgraf any reference papers for "each electrode just projects to a sphere of radius R"? @agramfort won't accept this viz without seeing it in use in the community

See figures:
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5580037/

See EcoG:
http://www.fieldtriptoolbox.org/tutorial/human_ecog/

The field trip ecog page has some visualizations like this I think, maybe they have a reference http://www.fieldtriptoolbox.org/tutorial/human_ecog/

@agramfort you are parisian we know you are never truly happy

(sorry, I couldn't resist, I haven't had as much chance to troll you these days)

Let's push this to 0.22 and hopefully we can tackle ECoG + sEEG in that release cycle

@larsoner This addition looks very useful for my current project. We are trying to show heat maps based on observed change from electrical stimulation. Is 'stc_near_sensors' is not currently available?

@jeschj01 it's not in the master branch but it's part of my branch shown at the top of this page, larsoner:ecog. If you set up a dev/editable install you can try it out. Or you can overwrite your existing 0.20 install with something like:

pip install --upgrade --no-deps https://github.com/larsoner/mne-python/archive/ecog.zip

@larsoner jw if you were planning on also adding the SEEG capability sometime (what you linked for source viz looks pretty neat)? No rush of course, I know you're a busy man :).

What are the steps needed to get it working on the volumetric viz? Just wondering to see if it's in my ability to even come close to implementing. I'm completely lost in mne-python's viz functions.

For sEEG, if we improve stc_near_sensors to allow returning a VolSourceEstimate object then nothing new needs to be done at the viz end. In other words, we "just" need to come up with a suitable way to interpolate the sEEG sensor activity into a volume, and once that's done, stc.plot and stc.plot_3d should just work.

Let me rebase this then we can merge it, and then @adam2392 it would be great if you could make a PR to allow interpolating into a volume!

@agramfort feel free to merge if you're happy

@larsoner it needs a what's new entry then +1 for MRG