def test_temporal_derivative_distribution_repair(fname, tmpdir):
"""Test running artifact rejection."""
raw = read_raw_nirx(fname)
raw = optical_density(raw)
# Add a baseline shift artifact about half way through data
max_shift = np.max(np.diff(raw._data[0]))
shift_amp = 5 * max_shift
raw._data[0, 0:30] = raw._data[0, 0:30] - (shift_amp)
assert np.max(np.diff(raw._data[0])) > shift_amp
# Ensure that applying the algorithm reduces the step change
raw = temporal_derivative_distribution_repair(raw)
assert np.max(np.diff(raw._data[0])) < shift_amp
def individual_analysis(bids_path):
# Read data with annotations in BIDS format
raw_intensity = read_raw_bids(bids_path=bids_path, verbose=False)
raw_intensity = get_long_channels(raw_intensity, min_dist=0.01)
# Convert signal to optical density and determine bad channels
raw_od = optical_density(raw_intensity)
sci = scalp_coupling_index(raw_od, h_freq=1.35, h_trans_bandwidth=0.1)
raw_od.info["bads"] = list(compress(raw_od.ch_names, sci < 0.5))
raw_od.interpolate_bads()
# Downsample and apply signal cleaning techniques
raw_od.resample(0.8)
raw_od = temporal_derivative_distribution_repair(raw_od)
# Convert to haemoglobin and filter
raw_haemo = beer_lambert_law(raw_od, ppf=0.1)
raw_haemo = raw_haemo.filter(0.02,
0.3,
h_trans_bandwidth=0.1,
l_trans_bandwidth=0.01,
verbose=False)
# Apply further data cleaning techniques and extract epochs
raw_haemo = enhance_negative_correlation(raw_haemo)
# Extract events but ignore those with
# the word Ends (i.e. drop ExperimentEnds events)
events, event_dict = events_from_annotations(raw_haemo,
verbose=False,
regexp='^(?![Ends]).*$')
epochs = Epochs(raw_haemo,
events,
event_id=event_dict,
tmin=-5,
tmax=20,
reject=dict(hbo=200e-6),
reject_by_annotation=True,
proj=True,
baseline=(None, 0),
detrend=0,
preload=True,
verbose=False)
return raw_haemo, epochs
corrupted_data[:, 450:750] = corrupted_data[:, 450:750] + 0.03
corrupted_od = mne.io.RawArray(corrupted_data,
raw_od.info,
first_samp=raw_od.first_samp)
new_annotations.append([95, 145, 245], [10, 10, 10],
["Spike", "Baseline", "Baseline"])
corrupted_od.set_annotations(new_annotations)
corrupted_od.plot(n_channels=15, duration=400, show_scrollbars=False)
# %%
# Apply temporal derivative distribution repair
# ---------------------------------------------
#
# This approach corrects baseline shift and spike artifacts without the need
# for any user-supplied parameters :footcite:`FishburnEtAl2019`.
corrected_tddr = temporal_derivative_distribution_repair(corrupted_od)
corrected_tddr.plot(n_channels=15, duration=400, show_scrollbars=False)
# %%
# We can see in the data above that the introduced spikes and shifts are
# largely removed, but some residual smaller artifact remains.
# The same can be said for the artifacts in the original data.
# %%
# References
# ----------
#
# .. footbibliography::