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MRG: Add support for NIRS to plot_topomap #7063

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merged 16 commits into from
Nov 18, 2019
Merged

MRG: Add support for NIRS to plot_topomap #7063

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d0ec2b2
Add support for NIRS to plot_topomap
rob-luke Nov 14, 2019
69158a5
Add plot_topomap to NIRS tutorial
rob-luke Nov 14, 2019
b382e8a
Add newline to tutorial
rob-luke Nov 14, 2019
b71f10c
Do not modify nirs channel names
rob-luke Nov 15, 2019
6d6322a
Update mne/viz/topomap.py
rob-luke Nov 15, 2019
0c4421c
Add another example to nirs tutorial
rob-luke Nov 16, 2019
8590af8
Add fnirs_raw and fnirs_od to topomap
rob-luke Nov 16, 2019
bdf2916
Change nirs channel naming. Instead of modifying clean_names
rob-luke Nov 16, 2019
7174e66
Fix new nirs name in beerlambert test
rob-luke Nov 17, 2019
f75d74e
Add combine evoked to tutorial
rob-luke Nov 17, 2019
add05f3
Remove repeated code in nirs tutorial
rob-luke Nov 17, 2019
a9e7a4d
Add back removed whitespace
rob-luke Nov 17, 2019
5f0ebce
Fix whitespace error
rob-luke Nov 17, 2019
509f30d
Extract only choma of choice in ch_type
rob-luke Nov 17, 2019
2d1da2c
Reuse evoked objects to save memory
rob-luke Nov 17, 2019
4f673bd
Merge branch 'master' into fnirs-topomap
larsoner Nov 18, 2019
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2 changes: 2 additions & 0 deletions doc/changes/latest.inc
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Expand Up @@ -67,6 +67,8 @@ Changelog

- Allow returning vector source estimates from sparse inverse solvers through ``pick_ori='vector'`` by `Christian Brodbeck`_

- Add NIRS support to :func:`mne.viz.plot_topomap` by `Robert Luke`_

Bug
~~~

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3 changes: 2 additions & 1 deletion mne/channels/channels.py
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Expand Up @@ -73,7 +73,8 @@ def _get_ch_type(inst, ch_type, allow_ref_meg=False):
then grads, then ... to plot.
"""
if ch_type is None:
allowed_types = ['mag', 'grad', 'planar1', 'planar2', 'eeg', 'csd']
allowed_types = ['mag', 'grad', 'planar1', 'planar2', 'eeg', 'csd',
'hbo', 'hbr']
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allowed_types += ['ref_meg'] if allow_ref_meg else []
for type_ in allowed_types:
if isinstance(inst, Info):
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11 changes: 8 additions & 3 deletions mne/viz/topomap.py
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Expand Up @@ -41,8 +41,11 @@ def _prepare_topo_plot(inst, ch_type, layout):
layout = find_layout(info) # XXX : why not passing ch_type???
elif layout == 'auto':
layout = None

clean_ch_names = _clean_names(info['ch_names'])
if ch_type in ['hbo', 'hbr']:
# The naming for nirs is very specific and should not be modified
clean_ch_names = info['ch_names']
else:
clean_ch_names = _clean_names(info['ch_names'])
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_clean_names is used in a number of places. I would rather avoid the branching here to make sure it's consistent with other places. How specific are NIRS channel names? What ch name breaks it?

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@rob-luke rob-luke Nov 16, 2019 •
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The name given by the device manufacturer is in the format of source number then detector number, so for example “S1 D2” is the channel between source one and detector two. We then appended the light frequency or chomaphore. So currently the MNE channel names are in the format S1 D2 760.

The problem was that the spaces were getting removed and the first characters were being kept, so we would have several channel names that were modified to be “S1” for example (because source one may have channels to several detectors). And that caused the error listed in the first comment of this PR.

But we can change the way channel names are stored. We could make it S1-D2-760 Or S1D2-760 etc. I think this would solve the problem, but not sure if it will break other things. What do you think?

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@rob-luke rob-luke Nov 16, 2019 •
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Sorry that’s wrong, the format is currently S1-D2 760. Another example is S12-D3 850

https://github.com/mne-tools/mne-python/blob/master/mne/io/nirx/tests/test_nirx.py#L37

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Ok I have push an attempt to change the naming to S1_D3 760. This way the source-detector pair is read as a single word, so stays unique without modifying clean_names. The raw.plot doesnt look as good, but I think thats ok. Thoughts?

for ii, this_ch in enumerate(info['chs']):
this_ch['ch_name'] = clean_ch_names[ii]
info['bads'] = _clean_names(info['bads'])
Expand All @@ -67,6 +70,9 @@ def _prepare_topo_plot(inst, ch_type, layout):
elif ch_type == 'csd':
picks = pick_types(info, meg=False, csd=True, ref_meg=False,
exclude='bads')
elif ch_type in ['hbo', 'hbr']:
picks = pick_types(info, meg=False, csd=True, ref_meg=False,
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fnirs=True, exclude='bads')
else:
picks = pick_types(info, meg=ch_type, ref_meg=False,
exclude='bads')
Expand Down Expand Up @@ -1637,7 +1643,6 @@ def plot_evoked_topomap(evoked, times="auto", ch_type=None, layout=None,
mask_params = _handle_default('mask_params', mask_params)
mask_params['markersize'] *= size / 2.
mask_params['markeredgewidth'] *= size / 2.

picks, pos, merge_grads, names, ch_type = _prepare_topo_plot(
evoked, ch_type, layout)

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31 changes: 30 additions & 1 deletion tutorials/preprocessing/plot_70_fnirs_processing.py
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Expand Up @@ -17,6 +17,7 @@
# sphinx_gallery_thumbnail_number = 3

import os
import numpy as np
import matplotlib.pyplot as plt

import mne
Expand Down Expand Up @@ -162,7 +163,7 @@
# Plot standard fNIRS response image
# ----------------------------------
#
# Finally we generate the most common visualisation of fNIRS data, plotting
# Next we generate the most common visualisation of fNIRS data, plotting
# both the HbO and HbR on the same figure to illustrate the relation between
# the two signals.

Expand All @@ -180,3 +181,31 @@

mne.viz.plot_compare_evokeds(evoked_dict, combine="mean", ci=0.95,
colors=color_dict, styles=styles_dict)


###############################################################################
# View topographic representation of activity
# -------------------------------------------
#
# Next we view how the topographic activity changes throughout the response.

times = np.arange(-3.5, 13.2, 3.0)
epochs['Tapping'].average(picks='hbo').plot_joint(times=times)


###############################################################################
# Compare tapping of left and right hands
# ---------------------------------------
#
# Finally we generate topo maps for the left and right conditions to view
# the location of activity. First we visualise the HbO activity.

times = np.arange(4.0, 11.0, 1.0)
epochs['Tapping/Left'].average(picks='hbo').plot_topomap(times=times)
epochs['Tapping/Right'].average(picks='hbo').plot_topomap(times=times)

###############################################################################
# And we also view the HbR activity for the two conditions.

epochs['Tapping/Left'].average(picks='hbr').plot_topomap(times=times)
epochs['Tapping/Right'].average(picks='hbr').plot_topomap(times=times)