def _interpolate_bads_nirs(inst, method='nearest', exclude=(), verbose=None):
from scipy.spatial.distance import pdist, squareform
from mne.preprocessing.nirs import _validate_nirs_info
# Returns pick of all nirs and ensures channels are correctly ordered
picks_nirs = _validate_nirs_info(inst.info)
if len(picks_nirs) == 0:
return
nirs_ch_names = [inst.info['ch_names'][p] for p in picks_nirs]
nirs_ch_names = [ch for ch in nirs_ch_names if ch not in exclude]
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'] = [ch for ch in inst.info['bads'] if ch in exclude]
return inst
def peak_power(raw, time_window=10, threshold=0.1, l_freq=0.7, h_freq=1.5,
l_trans_bandwidth=0.3, h_trans_bandwidth=0.3,
verbose=False):
"""
Compute peak spectral power metric for each channel and time window.
As described in [1]_ and [2]_.
This method provides a metric of data quality along the duration of
the measurement. The user can specify the window over which the
metric is computed.
Parameters
----------
raw : instance of Raw
The haemoglobin data.
time_window : number
The duration of the window over which to calculate the metric.
Default is 10 seconds as in PHOEBE paper.
threshold : number
Values below this are marked as bad and annotated in the raw file.
%(l_freq)s
%(h_freq)s
%(l_trans_bandwidth)s
%(h_trans_bandwidth)s
%(verbose)s
Returns
-------
raw : instance of Raw
The Raw data. Optionally annotated with bad segments.
scores : array (n_nirs, n_windows)
Array of peak power values.
times : list
List of the start and end times of each window used to compute the
peak spectral power.
References
----------
.. [1] Pollonini L et al., “PHOEBE: a method for real time mapping of
optodes-scalp coupling in functional near-infrared spectroscopy” in
Biomed. Opt. Express 7, 5104-5119 (2016).
.. [2] Hernandez, Samuel Montero, and Luca Pollonini. "NIRSplot: a tool for
quality assessment of fNIRS scans." Optics and the Brain.
Optical Society of America, 2020.
"""
raw = raw.copy().load_data()
_validate_type(raw, BaseRaw, 'raw')
if not len(pick_types(raw.info, fnirs='fnirs_od')):
raise RuntimeError('Scalp coupling index '
'should be run on optical density data.')
picks = _validate_nirs_info(raw.info)
filtered_data = filter_data(raw._data, raw.info['sfreq'], l_freq, h_freq,
picks=picks, verbose=verbose,
l_trans_bandwidth=l_trans_bandwidth,
h_trans_bandwidth=h_trans_bandwidth)
window_samples = int(np.ceil(time_window * raw.info['sfreq']))
n_windows = int(np.floor(len(raw) / window_samples))
scores = np.zeros((len(picks), n_windows))
times = []
for window in range(n_windows):
start_sample = int(window * window_samples)
end_sample = start_sample + window_samples
end_sample = np.min([end_sample, len(raw) - 1])
t_start = raw.times[start_sample]
t_stop = raw.times[end_sample]
times.append((t_start, t_stop))
for ii in picks[::2]:
c1 = filtered_data[ii][start_sample:end_sample]
c2 = filtered_data[ii + 1][start_sample:end_sample]
# protect against zero
c1 = c1 / (np.std(c1) or 1)
c2 = c2 / (np.std(c2) or 1)
c = np.correlate(c1, c2, "full")
c = c / (window_samples)
[f, pxx] = periodogram(c, fs=raw.info['sfreq'], window='hamming')
scores[ii, window] = max(pxx)
scores[ii + 1, window] = max(pxx)
if (threshold is not None) & (max(pxx) < threshold):
raw.annotations.append(t_start, time_window, 'BAD_PeakPower',
ch_names=[raw.ch_names[ii:ii + 2]])
return raw, scores, times
def _fnirs_check_bads(info):
_validate_nirs_info(info)