def test_long_extraction():
fnirs_data_folder = mne.datasets.fnirs_motor.data_path()
fnirs_raw_dir = os.path.join(fnirs_data_folder, 'Participant-1')
raw_intensity = mne.io.read_raw_nirx(fnirs_raw_dir).load_data()
long_chans = get_long_channels(raw_intensity)
original_num_channels = len(raw_intensity.ch_names)
assert original_num_channels == 56
long_num_channels = len(long_chans.ch_names)
assert long_num_channels == 56 - 16
new_lens = source_detector_distances(long_chans.info)
assert np.min(new_lens) >= 0.01
# Now test for non standard short length
long_chans = get_long_channels(raw_intensity, min_dist=0.022)
long_num_channels = len(long_chans.ch_names)
assert long_num_channels > 16 # There are 8 SDs in this set * hbo/hbr
new_lens = source_detector_distances(long_chans.info)
assert np.max(new_lens) >= 0.022
# Check that we dont run on other types, eg eeg.
raw_intensity.pick(picks=range(2))
raw_intensity.set_channel_types({'S1_D1 760': 'eeg', 'S1_D1 850': 'eeg'},
verbose='error')
with pytest.raises(RuntimeError, match='NIRS signals only'):
_ = get_long_channels(raw_intensity)
def test_nirx_15_0():
"""Test reading NIRX files."""
raw = read_raw_nirx(fname_nirx_15_0, preload=True)
# Test data import
assert raw._data.shape == (20, 92)
assert raw.info['sfreq'] == 6.25
assert raw.info['meas_date'] == dt.datetime(2019,
10,
27,
13,
53,
34,
209000,
tzinfo=dt.timezone.utc)
# 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"
]
# Test info import
assert raw.info['subject_info'] == {
'birthday': (2004, 10, 27),
'first_name': 'NIRX',
'last_name': 'Test',
'sex': FIFF.FIFFV_SUBJ_SEX_UNKNOWN,
'his_id': "NIRX_Test"
}
# Test trigger events
assert_array_equal(raw.annotations.description, ['1.0', '2.0', '2.0'])
# Test location of detectors
allowed_dist_error = 0.0002
locs = [ch['loc'][6:9] for ch in raw.info['chs']]
head_mri_t, _ = _get_trans('fsaverage', 'head', 'mri')
mni_locs = apply_trans(head_mri_t, locs)
assert raw.info['ch_names'][0][3:5] == 'D1'
assert_allclose(mni_locs[0], [0.0287, -0.1143, -0.0332],
atol=allowed_dist_error)
assert raw.info['ch_names'][15][3:5] == 'D8'
assert_allclose(mni_locs[15], [-0.0693, -0.0480, 0.0657],
atol=allowed_dist_error)
# Test distance between optodes matches values from
allowed_distance_error = 0.0002
assert_allclose(source_detector_distances(
raw.copy().pick("S1_D1 760").info), [0.0300],
atol=allowed_distance_error)
assert_allclose(source_detector_distances(
raw.copy().pick("S7_D7 760").info), [0.0392],
atol=allowed_distance_error)
def get_long_channels(raw, min_dist=0.01):
"""
Return channels with a long source detector separation.
Parameters
----------
raw : instance of Raw
The haemoglobin data.
min_dist : number
Minimum distance of returned channel.
Returns
-------
raw : instance of Raw
Raw instance with only long channels.
"""
long_chans = raw.copy().load_data()
_validate_type(long_chans, BaseRaw, 'raw')
picks = mne.pick_types(long_chans.info, meg=False, eeg=False, fnirs=True)
if not len(picks):
raise RuntimeError('Short channel extraction for NIRS signals only.')
dists = source_detector_distances(long_chans.info, picks=picks)
long_chans.pick(picks[dists > min_dist])
return long_chans
def get_short_channels(raw, max_dist=0.01):
"""
Return channels with a short source-detector separation.
Parameters
----------
raw : instance of Raw
Raw instance containing fNIRS data.
max_dist : number
Maximum distance of returned channels (m).
Returns
-------
raw : instance of Raw
Raw instance with only short channels.
"""
short_chans = raw.copy().load_data()
_validate_type(short_chans, BaseRaw, 'raw')
picks = mne.pick_types(short_chans.info,
meg=False,
eeg=False,
fnirs=True,
exclude=[])
if not len(picks):
raise RuntimeError('Short channel extraction for NIRS signals only.')
dists = source_detector_distances(short_chans.info, picks=picks)
short_chans.pick(picks[dists < max_dist])
return short_chans
def short_channel_regression(raw, max_dist=0.01):
"""
Systemic correction regression based on nearest short channel.
Method as described by NIRx and based on
:footcite:`fabbri2004optical`, :footcite:`saager2005direct`,
and :footcite:`scholkmann2014measuring`.
Parameters
----------
raw : instance of Raw
Raw instance containing optical density data.
max_dist : number
Channels less than this distance are considered short (m).
Returns
-------
raw : instance of Raw
The modified raw instance.
References
----------
.. footbibliography::
"""
raw = raw.copy().load_data()
_validate_type(raw, BaseRaw, 'raw')
picks_od = pick_types(raw.info, fnirs='fnirs_od')
if len(picks_od) == 0:
raise RuntimeError('Data must be optical density.')
distances = source_detector_distances(raw.info)
picks_short = picks_od[distances[picks_od] < max_dist]
picks_long = picks_od[distances[picks_od] > max_dist]
if len(picks_short) == 0:
raise RuntimeError('No short channels present.')
if len(picks_long) == 0:
raise RuntimeError('No long channels present.')
for pick in picks_long:
short_idx = _find_nearest_short(raw, pick, picks_short)
A_l = raw.get_data(pick).ravel()
A_s = raw.get_data(short_idx).ravel()
# Eqn 27 Scholkmann et al 2014
alfa = np.dot(A_s, A_l) / np.dot(A_s, A_s)
# Eqn 26 Scholkmann et al 2014
raw._data[pick] = A_l - alfa * A_s
return raw
def test_long_extraction():
fnirs_data_folder = mne.datasets.fnirs_motor.data_path()
fnirs_raw_dir = os.path.join(fnirs_data_folder, 'Participant-1')
raw_intensity = mne.io.read_raw_nirx(fnirs_raw_dir).load_data()
long_chans = get_long_channels(raw_intensity)
original_num_channels = len(raw_intensity.ch_names)
assert original_num_channels == 56
long_num_channels = len(long_chans.ch_names)
assert long_num_channels == 56 - 16
new_lens = source_detector_distances(long_chans.info)
assert np.min(new_lens) >= 0.01
# Now test for non standard short length
long_chans = get_long_channels(raw_intensity, min_dist=0.022)
long_num_channels = len(long_chans.ch_names)
assert long_num_channels > 16 # There are 8 SDs in this set * hbo/hbr
new_lens = source_detector_distances(long_chans.info)
assert np.max(new_lens) >= 0.022
def test_nirx_15_0():
"""Test reading NIRX files."""
raw = read_raw_nirx(fname_nirx_15_0, preload=True)
# Test data import
assert raw._data.shape == (20, 92)
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"
]
# Test info import
assert raw.info['subject_info'] == {
'first_name': 'NIRX',
'last_name': 'Test',
'sex': '0'
}
# Test trigger events
assert_array_equal(raw.annotations.description, ['1.0', '2.0', '2.0'])
# Test location of detectors
allowed_dist_error = 0.0002
locs = [ch['loc'][6:9] for ch in raw.info['chs']]
head_mri_t, _ = _get_trans('fsaverage', 'head', 'mri')
mni_locs = apply_trans(head_mri_t, locs)
assert raw.info['ch_names'][0][3:5] == 'D1'
assert_allclose(mni_locs[0], [0.0287, -0.1143, -0.0332],
atol=allowed_dist_error)
assert raw.info['ch_names'][15][3:5] == 'D8'
assert_allclose(mni_locs[15], [-0.0693, -0.0480, 0.0657],
atol=allowed_dist_error)
# Test distance between optodes matches values from
allowed_distance_error = 0.0002
distances = source_detector_distances(raw.info)
assert_allclose(distances[::2], [
0.0301, 0.0315, 0.0343, 0.0368, 0.0408, 0.0399, 0.0393, 0.0367, 0.0336,
0.0447
],
atol=allowed_distance_error)
def test_snirf_nirsport2_w_positions():
"""Test reading SNIRF files with known positions."""
raw = read_raw_snirf(nirx_nirsport2_103_2,
preload=True,
optode_frame="mri")
# Test data import
assert raw._data.shape == (40, 128)
assert_almost_equal(raw.info['sfreq'], 10.2, decimal=1)
# Test channel naming
assert raw.info['ch_names'][:4] == [
'S1_D1 760', 'S1_D1 850', 'S1_D6 760', 'S1_D6 850'
]
assert raw.info['ch_names'][24:26] == ['S6_D4 760', 'S6_D4 850']
# Test frequency encoding
assert raw.info['chs'][0]['loc'][9] == 760
assert raw.info['chs'][1]['loc'][9] == 850
assert sum(short_channels(raw.info)) == 16
# Test distance between optodes matches values from
# nirsite https://github.com/mne-tools/mne-testing-data/pull/86
# figure 3
allowed_distance_error = 0.005
distances = source_detector_distances(raw.info)
assert_allclose(distances[::2][:14], [
0.0304, 0.0411, 0.008, 0.0400, 0.008, 0.0310, 0.0411, 0.008, 0.0299,
0.008, 0.0370, 0.008, 0.0404, 0.008
],
atol=allowed_distance_error)
# Test location of detectors
# The locations of detectors can be seen in the first
# figure on this page...
# https://github.com/mne-tools/mne-testing-data/pull/86
allowed_dist_error = 0.0002
locs = [ch['loc'][6:9] for ch in raw.info['chs']]
head_mri_t, _ = _get_trans('fsaverage', 'head', 'mri')
mni_locs = apply_trans(head_mri_t, locs)
assert raw.info['ch_names'][0][3:5] == 'D1'
assert_allclose(mni_locs[0], [-0.0841, -0.0464, -0.0129],
atol=allowed_dist_error)
assert raw.info['ch_names'][2][3:5] == 'D6'
assert_allclose(mni_locs[2], [-0.0841, -0.0138, 0.0248],
atol=allowed_dist_error)
assert raw.info['ch_names'][34][3:5] == 'D5'
assert_allclose(mni_locs[34], [0.0845, -0.0451, -0.0123],
atol=allowed_dist_error)
# Test location of sensors
# The locations of sensors can be seen in the second
# figure on this page...
# https://github.com/mne-tools/mne-testing-data/pull/86
allowed_dist_error = 0.0002
locs = [ch['loc'][3:6] for ch in raw.info['chs']]
head_mri_t, _ = _get_trans('fsaverage', 'head', 'mri')
mni_locs = apply_trans(head_mri_t, locs)
assert raw.info['ch_names'][0][:2] == 'S1'
assert_allclose(mni_locs[0], [-0.0848, -0.0162, -0.0163],
atol=allowed_dist_error)
assert raw.info['ch_names'][9][:2] == 'S2'
assert_allclose(mni_locs[9], [-0.0, -0.1195, 0.0142],
atol=allowed_dist_error)
assert raw.info['ch_names'][34][:2] == 'S8'
assert_allclose(mni_locs[34], [0.0828, -0.046, 0.0285],
atol=allowed_dist_error)
mon = raw.get_montage()
assert len(mon.dig) == 43
def test_nirsport_v2():
"""Test NIRSport2 file."""
raw = read_raw_nirx(nirsport2, preload=True)
assert raw._data.shape == (40, 128)
# Test distance between optodes matches values from
# nirsite https://github.com/mne-tools/mne-testing-data/pull/86
# figure 3
allowed_distance_error = 0.005
distances = source_detector_distances(raw.info)
assert_allclose(distances[::2][:14], [
0.0304, 0.0411, 0.008, 0.0400, 0.008, 0.0310, 0.0411, 0.008, 0.0299,
0.008, 0.0370, 0.008, 0.0404, 0.008
],
atol=allowed_distance_error)
# Test location of detectors
# The locations of detectors can be seen in the first
# figure on this page...
# https://github.com/mne-tools/mne-testing-data/pull/86
allowed_dist_error = 0.0002
locs = [ch['loc'][6:9] for ch in raw.info['chs']]
head_mri_t, _ = _get_trans('fsaverage', 'head', 'mri')
mni_locs = apply_trans(head_mri_t, locs)
assert raw.info['ch_names'][0][3:5] == 'D1'
assert_allclose(mni_locs[0], [-0.0841, -0.0464, -0.0129],
atol=allowed_dist_error)
assert raw.info['ch_names'][2][3:5] == 'D6'
assert_allclose(mni_locs[2], [-0.0841, -0.0138, 0.0248],
atol=allowed_dist_error)
assert raw.info['ch_names'][34][3:5] == 'D5'
assert_allclose(mni_locs[34], [0.0845, -0.0451, -0.0123],
atol=allowed_dist_error)
# Test location of sensors
# The locations of sensors can be seen in the second
# figure on this page...
# https://github.com/mne-tools/mne-testing-data/pull/86
locs = [ch['loc'][3:6] for ch in raw.info['chs']]
head_mri_t, _ = _get_trans('fsaverage', 'head', 'mri')
mni_locs = apply_trans(head_mri_t, locs)
assert raw.info['ch_names'][0][:2] == 'S1'
assert_allclose(mni_locs[0], [-0.0848, -0.0162, -0.0163],
atol=allowed_dist_error)
assert raw.info['ch_names'][9][:2] == 'S2'
assert_allclose(mni_locs[9], [-0.0, -0.1195, 0.0142],
atol=allowed_dist_error)
assert raw.info['ch_names'][34][:2] == 'S8'
assert_allclose(mni_locs[34], [0.0828, -0.046, 0.0285],
atol=allowed_dist_error)
assert len(raw.annotations) == 3
assert raw.annotations.description[0] == '1.0'
assert raw.annotations.description[2] == '6.0'
# Lose tolerance as I am eyeballing the time differences on screen
assert_allclose(np.diff(raw.annotations.onset), [2.3, 3.1], atol=0.1)
mon = raw.get_montage()
assert len(mon.dig) == 43
def test_nirx_15_3_short():
"""Test reading NIRX files."""
raw = read_raw_nirx(fname_nirx_15_3_short, preload=True)
# Test data import
assert raw._data.shape == (26, 220)
assert raw.info['sfreq'] == 12.5
# Test channel naming
assert raw.info['ch_names'][:4] == [
"S1_D2 760", "S1_D2 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
assert raw.info['chs'][1]['loc'][9] == 850
# Test info import
assert raw.info['subject_info'] == dict(birthday=(2020, 8, 18),
sex=0,
first_name="testMontage\\0A"
"TestMontage",
his_id="testMontage\\0A"
"TestMontage")
# Test distance between optodes matches values from
# https://github.com/mne-tools/mne-testing-data/pull/72
allowed_distance_error = 0.001
distances = source_detector_distances(raw.info)
assert_allclose(distances[::2], [
0.0304, 0.0078, 0.0310, 0.0086, 0.0416, 0.0072, 0.0389, 0.0075, 0.0558,
0.0562, 0.0561, 0.0565, 0.0077
],
atol=allowed_distance_error)
# Test which channels are short
# These are the ones marked as red at
# https://github.com/mne-tools/mne-testing-data/pull/72
is_short = short_channels(raw.info)
assert_array_equal(is_short[:9:2], [False, True, False, True, False])
is_short = short_channels(raw.info, threshold=0.003)
assert_array_equal(is_short[:3:2], [False, False])
is_short = short_channels(raw.info, threshold=50)
assert_array_equal(is_short[:3:2], [True, True])
# Test trigger events
assert_array_equal(raw.annotations.description, ['4.0', '2.0', '1.0'])
# Test location of detectors
# The locations of detectors can be seen in the first
# figure on this page...
# https://github.com/mne-tools/mne-testing-data/pull/72
# And have been manually copied below
allowed_dist_error = 0.0002
locs = [ch['loc'][6:9] for ch in raw.info['chs']]
head_mri_t, _ = _get_trans('fsaverage', 'head', 'mri')
mni_locs = apply_trans(head_mri_t, locs)
assert raw.info['ch_names'][0][3:5] == 'D2'
assert_allclose(mni_locs[0], [-0.0841, -0.0464, -0.0129],
atol=allowed_dist_error)
assert raw.info['ch_names'][4][3:5] == 'D1'
assert_allclose(mni_locs[4], [0.0846, -0.0142, -0.0156],
atol=allowed_dist_error)
assert raw.info['ch_names'][8][3:5] == 'D3'
assert_allclose(mni_locs[8], [0.0207, -0.1062, 0.0484],
atol=allowed_dist_error)
assert raw.info['ch_names'][12][3:5] == 'D4'
assert_allclose(mni_locs[12], [-0.0196, 0.0821, 0.0275],
atol=allowed_dist_error)
assert raw.info['ch_names'][16][3:5] == 'D5'
assert_allclose(mni_locs[16], [-0.0360, 0.0276, 0.0778],
atol=allowed_dist_error)
assert raw.info['ch_names'][19][3:5] == 'D6'
assert_allclose(mni_locs[19], [0.0388, -0.0477, 0.0932],
atol=allowed_dist_error)
assert raw.info['ch_names'][21][3:5] == 'D7'
assert_allclose(mni_locs[21], [-0.0394, -0.0483, 0.0928],
atol=allowed_dist_error)
def test_nirx_15_2_short():
"""Test reading NIRX files."""
raw = read_raw_nirx(fname_nirx_15_2_short, preload=True)
# Test data import
assert raw._data.shape == (26, 145)
assert raw.info['sfreq'] == 12.5
assert raw.info['meas_date'] == dt.datetime(2019,
8,
23,
7,
37,
4,
540000,
tzinfo=dt.timezone.utc)
# 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"]
# Test frequency encoding
assert raw.info['chs'][0]['loc'][9] == 760
assert raw.info['chs'][1]['loc'][9] == 850
# Test info import
assert raw.info['subject_info'] == dict(sex=1,
first_name="MNE",
middle_name="Test",
last_name="Recording",
birthday=(2014, 8, 23),
his_id="MNE_Test_Recording")
# Test distance between optodes matches values from
# nirsite https://github.com/mne-tools/mne-testing-data/pull/51
# step 4 figure 2
allowed_distance_error = 0.0002
distances = source_detector_distances(raw.info)
assert_allclose(distances[::2], [
0.0304, 0.0078, 0.0310, 0.0086, 0.0416, 0.0072, 0.0389, 0.0075, 0.0558,
0.0562, 0.0561, 0.0565, 0.0077
],
atol=allowed_distance_error)
# Test which channels are short
# These are the ones marked as red at
# https://github.com/mne-tools/mne-testing-data/pull/51 step 4 figure 2
is_short = short_channels(raw.info)
assert_array_equal(is_short[:9:2], [False, True, False, True, False])
is_short = short_channels(raw.info, threshold=0.003)
assert_array_equal(is_short[:3:2], [False, False])
is_short = short_channels(raw.info, threshold=50)
assert_array_equal(is_short[:3:2], [True, True])
# Test trigger events
assert_array_equal(raw.annotations.description, ['3.0', '2.0', '1.0'])
# Test location of detectors
# The locations of detectors can be seen in the first
# figure on this page...
# https://github.com/mne-tools/mne-testing-data/pull/51
# And have been manually copied below
# These values were reported in mm, but according to this page...
# https://mne.tools/stable/auto_tutorials/intro/plot_40_sensor_locations.html
# 3d locations should be specified in meters, so that's what's tested below
# Detector locations are stored in the third three loc values
allowed_dist_error = 0.0002
locs = [ch['loc'][6:9] for ch in raw.info['chs']]
head_mri_t, _ = _get_trans('fsaverage', 'head', 'mri')
mni_locs = apply_trans(head_mri_t, locs)
assert raw.info['ch_names'][0][3:5] == 'D1'
assert_allclose(mni_locs[0], [-0.0841, -0.0464, -0.0129],
atol=allowed_dist_error)
assert raw.info['ch_names'][4][3:5] == 'D3'
assert_allclose(mni_locs[4], [0.0846, -0.0142, -0.0156],
atol=allowed_dist_error)
assert raw.info['ch_names'][8][3:5] == 'D2'
assert_allclose(mni_locs[8], [0.0207, -0.1062, 0.0484],
atol=allowed_dist_error)
assert raw.info['ch_names'][12][3:5] == 'D4'
assert_allclose(mni_locs[12], [-0.0196, 0.0821, 0.0275],
atol=allowed_dist_error)
assert raw.info['ch_names'][16][3:5] == 'D5'
assert_allclose(mni_locs[16], [-0.0360, 0.0276, 0.0778],
atol=allowed_dist_error)
assert raw.info['ch_names'][19][3:5] == 'D6'
assert_allclose(mni_locs[19], [0.0352, 0.0283, 0.0780],
atol=allowed_dist_error)
assert raw.info['ch_names'][21][3:5] == 'D7'
assert_allclose(mni_locs[21], [0.0388, -0.0477, 0.0932],
atol=allowed_dist_error)
def short_channel_regression(raw, max_dist=0.01):
"""
Short channel regression based on nearest channel.
Fabbri, Francesco, et al. "Optical measurements of absorption changes in
two-layered diffusive media."
Physics in Medicine & Biology 49.7 (2004): 1183.
Saager, Rolf B., and Andrew J. Berger. "Direct characterization and
removal of interfering absorption trends in two-layer turbid media."
JOSA A 22.9 (2005): 1874-1882.
Scholkmann, Felix, Andreas Jaakko Metz, and Martin Wolf.
"Measuring tissue hemodynamics and oxygenation by continuous-wave
functional near-infrared spectroscopy—how robust are the different
calculation methods against movement artifacts?."
Physiological measurement 35.4 (2014): 717.
Parameters
----------
raw : instance of Raw
Haemoglobin data.
max_dist : number
Channels less than this distance are considered short (m).
Returns
-------
raw : instance of Raw
The modified raw instance.
"""
raw = raw.copy().load_data()
_validate_type(raw, BaseRaw, 'raw')
picks_od = pick_types(raw.info, fnirs='fnirs_od')
if len(picks_od) == 0:
raise RuntimeError('Data must be optical density.')
distances = source_detector_distances(raw.info)
picks_short = picks_od[distances[picks_od] < max_dist]
picks_long = picks_od[distances[picks_od] > max_dist]
if len(picks_short) == 0:
raise RuntimeError('No short channels present.')
if len(picks_long) == 0:
raise RuntimeError('No long channels present.')
for pick in picks_long:
short_idx = _find_nearest_short(raw, pick, picks_short)
A_l = raw.get_data(pick).ravel()
A_s = raw.get_data(short_idx).ravel()
# Eqn 27 Scholkmann et al 2014
alfa = np.dot(A_s, A_l) / np.dot(A_s, A_s)
# Eqn 26 Scholkmann et al 2014
raw._data[pick] = A_l - alfa * A_s
return raw
