MRG: Add support for NIRS to plot_topomap

doc/changes/latest.inc

@@ -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
 ~~~
 

mne/channels/channels.py

@@ -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',
+                         'fnirs_raw', 'fnirs_od', 'hbo', 'hbr']
         allowed_types += ['ref_meg'] if allow_ref_meg else []
         for type_ in allowed_types:
             if isinstance(inst, Info):

mne/io/nirx/nirx.py

@@ -198,7 +198,7 @@ def prepend(list, str):
             list = [str.format(i) for i in list]
             return(list)
         snames = prepend(sources[req_ind], 'S')
-        dnames = prepend(detectors[req_ind], '-D')
+        dnames = prepend(detectors[req_ind], '_D')
         sdnames = [m + str(n) for m, n in zip(snames, dnames)]
         sd1 = [s + ' ' + str(fnirs_wavelengths[0]) for s in sdnames]
         sd2 = [s + ' ' + str(fnirs_wavelengths[1]) for s in sdnames]

mne/io/nirx/tests/test_nirx.py

@@ -34,9 +34,9 @@ def test_nirx_15_2_short():
     assert raw.info['sfreq'] == 12.5
 
     # Test channel naming
-    assert raw.info['ch_names'][:4] == ["S1-D1 760", "S1-D1 850",
-                                        "S1-D9 760", "S1-D9 850"]
-    assert raw.info['ch_names'][24:26] == ["S5-D13 760", "S5-D13 850"]
+    assert raw.info['ch_names'][:4] == ["S1_D1 760", "S1_D1 850",
+                                        "S1_D9 760", "S1_D9 850"]
+    assert raw.info['ch_names'][24:26] == ["S5_D13 760", "S5_D13 850"]
 
     # Test frequency encoding
     assert raw.info['chs'][0]['loc'][9] == 760
@@ -123,8 +123,8 @@ def test_nirx_15_2():
     assert raw.info['sfreq'] == 3.90625
 
     # Test channel naming
-    assert raw.info['ch_names'][:4] == ["S1-D1 760", "S1-D1 850",
-                                        "S1-D10 760", "S1-D10 850"]
+    assert raw.info['ch_names'][:4] == ["S1_D1 760", "S1_D1 850",
+                                        "S1_D10 760", "S1_D10 850"]
 
     # Test info import
     assert raw.info['subject_info'] == dict(sex=1, first_name="TestRecording")
@@ -157,12 +157,12 @@ def test_nirx_15_0():
     assert raw.info['sfreq'] == 6.25
 
     # Test channel naming
-    assert raw.info['ch_names'][:12] == ["S1-D1 760", "S1-D1 850",
-                                         "S2-D2 760", "S2-D2 850",
-                                         "S3-D3 760", "S3-D3 850",
-                                         "S4-D4 760", "S4-D4 850",
-                                         "S5-D5 760", "S5-D5 850",
-                                         "S6-D6 760", "S6-D6 850"]
+    assert raw.info['ch_names'][:12] == ["S1_D1 760", "S1_D1 850",
+                                         "S2_D2 760", "S2_D2 850",
+                                         "S3_D3 760", "S3_D3 850",
+                                         "S4_D4 760", "S4_D4 850",
+                                         "S5_D5 760", "S5_D5 850",
+                                         "S6_D6 760", "S6_D6 850"]
 
     # Test info import
     assert raw.info['subject_info'] == {'first_name': 'NIRX',

mne/preprocessing/nirs/_beer_lambert_law.py

@@ -74,9 +74,9 @@ def _check_channels_ordered(raw, freqs):
     # and have the specified light frequencies.
     picks = _picks_to_idx(raw.info, 'fnirs_od')
     for ii in picks[::2]:
-        ch1_name_info = re.match(r'S(\d+)-D(\d+) (\d+)',
+        ch1_name_info = re.match(r'S(\d+)_D(\d+) (\d+)',
                                  raw.info['chs'][ii]['ch_name'])
-        ch2_name_info = re.match(r'S(\d+)-D(\d+) (\d+)',
+        ch2_name_info = re.match(r'S(\d+)_D(\d+) (\d+)',
                                  raw.info['chs'][ii + 1]['ch_name'])
 
         if (ch1_name_info.groups()[0] != ch2_name_info.groups()[0]) or \

mne/preprocessing/tests/test_beer_lambert_law.py

@@ -68,6 +68,6 @@ def test_beer_lambert_v_matlab():
                              raw._data[idx])
         assert mean_error < 0.1
         matlab_name = ("S" + str(int(matlab_data['sources'][idx])) +
-                       "-D" + str(int(matlab_data['detectors'][idx])) +
+                       "_D" + str(int(matlab_data['detectors'][idx])) +
                        " " + matlab_data['type'][idx])
         assert raw.info['ch_names'][idx] == matlab_name

mne/viz/topomap.py

@@ -67,6 +67,9 @@ 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', 'fnirs_raw', 'fnirs_od']:
+            picks = pick_types(info, meg=False, ref_meg=False,
+                               fnirs=ch_type, exclude='bads')
         else:
             picks = pick_types(info, meg=ch_type, ref_meg=False,
                                exclude='bads')

tutorials/preprocessing/plot_70_fnirs_processing.py

@@ -17,6 +17,7 @@
 # sphinx_gallery_thumbnail_number = 3
 
 import os
+import numpy as np
 import matplotlib.pyplot as plt
 
 import mne
@@ -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.
 
@@ -180,3 +181,61 @@
 
 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)
+
+###############################################################################
+# And we can plot the comparison at a single time point for two conditions.
+
+fig, axes = plt.subplots(nrows=2, ncols=4, figsize=(9, 5))
+vmin, vmax, ts = -8, 8, 9.0
+
+evoked_left = epochs['Tapping/Left'].average()
+evoked_right = epochs['Tapping/Right'].average()
+
+evoked_left.plot_topomap(ch_type='hbo', times=ts, axes=axes[0, 0],
+                         vmin=vmin, vmax=vmax, colorbar=False)
+evoked_left.plot_topomap(ch_type='hbr', times=ts, axes=axes[1, 0],
+                         vmin=vmin, vmax=vmax, colorbar=False)
+evoked_right.plot_topomap(ch_type='hbo', times=ts, axes=axes[0, 1],
+                          vmin=vmin, vmax=vmax, colorbar=False)
+evoked_right.plot_topomap(ch_type='hbr', times=ts, axes=axes[1, 1],
+                          vmin=vmin, vmax=vmax, colorbar=False)
+
+evoked_diff = mne.combine_evoked([evoked_left, -evoked_right], weights='equal')
+
+evoked_diff.plot_topomap(ch_type='hbo', times=ts, axes=axes[0, 2],
+                         vmin=vmin, vmax=vmax)
+evoked_diff.plot_topomap(ch_type='hbr', times=ts, axes=axes[1, 2],
+                         vmin=vmin, vmax=vmax, colorbar=True)
+
+for column, condition in enumerate(
+        ['Tapping Left', 'Tapping Right', 'Left-Right']):
+    for row, chroma in enumerate(['HbO', 'HbR']):
+        axes[row, column].set_title('{}: {}'.format(chroma, condition))