MRG: Add interpolation for NIRS signals

doc/changes/latest.inc

@@ -21,6 +21,7 @@ Changelog
 
 - Added temporal derivative distribution repair :func:`mne.preprocessing.nirs.temporal_derivative_distribution_repair` by `Robert Luke`_
 
+- Add functionality to interpolate bad NIRS channels by `Robert Luke`_
 
 Bug
 ~~~

mne/channels/channels.py

@@ -993,7 +993,8 @@ def interpolate_bads(self, reset_bads=True, mode='accurate',
         .. versionadded:: 0.9.0
         """
         from ..bem import _check_origin
-        from .interpolation import _interpolate_bads_eeg, _interpolate_bads_meg
+        from .interpolation import _interpolate_bads_eeg,\
+            _interpolate_bads_meg, _interpolate_bads_nirs
 
         _check_preload(self, "interpolation")
 
@@ -1003,6 +1004,7 @@ def interpolate_bads(self, reset_bads=True, mode='accurate',
         origin = _check_origin(origin, self.info)
         _interpolate_bads_eeg(self, origin=origin)
         _interpolate_bads_meg(self, mode=mode, origin=origin)
+        _interpolate_bads_nirs(self)
 
         if reset_bads is True:
             self.info['bads'] = []

mne/channels/interpolation.py

@@ -11,6 +11,7 @@
 from ..io.pick import pick_types, pick_channels, pick_info
 from ..surface import _normalize_vectors
 from ..forward import _map_meg_channels
+from ..utils import _check_option
 
 
 def _calc_h(cosang, stiffness=4, n_legendre_terms=50):
@@ -213,3 +214,58 @@ def _interpolate_bads_meg(inst, mode='accurate', origin=(0., 0., 0.04),
     info_to = pick_info(inst.info, picks_bad)
     mapping = _map_meg_channels(info_from, info_to, mode=mode, origin=origin)
     _do_interp_dots(inst, mapping, picks_good, picks_bad)
+
+
+@verbose
+def _interpolate_bads_nirs(inst, method='nearest', verbose=None):
+    """Interpolate bad nirs channels. Simply replaces by closest non bad.
+
+    Parameters
+    ----------
+    inst : mne.io.Raw, mne.Epochs or mne.Evoked
+        The data to interpolate. Must be preloaded.
+    method : str
+        Only the method 'nearest' is currently available. This method replaces
+        each bad channel with the nearest non bad channel.
+    %(verbose)s
+    """
+    from scipy.spatial.distance import pdist, squareform
+    from mne.preprocessing.nirs import _channel_frequencies,\
+        _check_channels_ordered
+
+    # Returns pick of all nirs and ensures channels are correctly ordered
+    freqs = np.unique(_channel_frequencies(inst))
+    picks_nirs = _check_channels_ordered(inst, freqs)
+    if len(picks_nirs) == 0:
+        return
+
+    nirs_ch_names = [inst.info['ch_names'][p] for p in picks_nirs]
+    bads_nirs = [ch for ch in inst.info['bads'] if ch in nirs_ch_names]
+    if len(bads_nirs) == 0:
+        return
+    picks_bad = pick_channels(inst.info['ch_names'], bads_nirs, exclude=[])
+    bads_mask = [p in picks_bad for p in picks_nirs]
+
+    chs = [inst.info['chs'][i] for i in picks_nirs]
+    locs3d = np.array([ch['loc'][:3] for ch in chs])
+
+    _check_option('fnirs_method', method, ['nearest'])
+
+    if method == 'nearest':
+
+        dist = pdist(locs3d)
+        dist = squareform(dist)
+
+        for bad in picks_bad:
+            dists_to_bad = dist[bad]
+            # Ignore distances to self
+            dists_to_bad[dists_to_bad == 0] = np.inf
+            # Ignore distances to other bad channels
+            dists_to_bad[bads_mask] = np.inf
+            # Find closest remaining channels for same frequency
+            closest_idx = np.argmin(dists_to_bad) + (bad % 2)
+            inst._data[bad] = inst._data[closest_idx]
+
+        inst.info['bads'] = []
+
+    return inst

mne/channels/tests/test_interpolation.py

@@ -3,11 +3,15 @@
 import numpy as np
 from numpy.testing import (assert_allclose, assert_array_equal)
 import pytest
+from itertools import compress
 
 from mne import io, pick_types, pick_channels, read_events, Epochs
 from mne.channels.interpolation import _make_interpolation_matrix
 from mne.datasets import testing
 from mne.utils import run_tests_if_main
+from mne.preprocessing.nirs import optical_density, scalp_coupling_index
+from mne.datasets.testing import data_path
+from mne.io import read_raw_nirx
 
 base_dir = op.join(op.dirname(__file__), '..', '..', 'io', 'tests', 'data')
 raw_fname = op.join(base_dir, 'test_raw.fif')
@@ -219,4 +223,21 @@ def test_interpolation_ctf_comp():
     assert raw.info['bads'] == []
 
 
+@testing.requires_testing_data
+def test_interpolation_nirs():
+    """Test interpolating bad nirs channels."""
+    fname = op.join(data_path(download=False),
+                    'NIRx', 'nirx_15_2_recording_w_overlap')
+    raw_intensity = read_raw_nirx(fname, preload=False)
+    raw_od = optical_density(raw_intensity)
+    sci = scalp_coupling_index(raw_od)
+    raw_od.info['bads'] = list(compress(raw_od.ch_names, sci < 0.5))
+    bad_0 = np.where([name == raw_od.info['bads'][0] for
+                      name in raw_od.ch_names])[0][0]
+    bad_0_std_pre_interp = np.std(raw_od._data[bad_0])
+    raw_od.interpolate_bads()
+    assert raw_od.info['bads'] == []
+    assert bad_0_std_pre_interp > np.std(raw_od._data[bad_0])
+
+
 run_tests_if_main()

mne/preprocessing/nirs/nirs.py

@@ -58,7 +58,7 @@ def short_channels(info, threshold=0.01):
 
 def _channel_frequencies(raw):
     """Return the light frequency for each channel."""
-    picks = _picks_to_idx(raw.info, 'fnirs', exclude=[])
+    picks = _picks_to_idx(raw.info, 'fnirs', exclude=[], allow_empty=True)
     freqs = np.empty(picks.size, int)
     for ii in picks:
         freqs[ii] = raw.info['chs'][ii]['loc'][9]
@@ -69,7 +69,7 @@ def _check_channels_ordered(raw, freqs):
     """Check channels followed expected fNIRS format."""
     # Every second channel should be same SD pair
     # and have the specified light frequencies.
-    picks = _picks_to_idx(raw.info, 'fnirs', exclude=[])
+    picks = _picks_to_idx(raw.info, 'fnirs', exclude=[], allow_empty=True)
     if len(picks) % 2 != 0:
         raise ValueError(
             'NIRS channels not ordered correctly. An even number of NIRS '