def test_muscle_annotation_without_meeg_data(meas_date):
"""Call annotate_muscle_zscore with data without meg or eeg."""
raw = read_raw_fif(raw_fname, allow_maxshield='yes')
if meas_date is None:
raw.set_meas_date(None)
raw.crop(0, .1).load_data()
raw.pick_types(meg=False, stim=True)
with pytest.raises(ValueError, match="No M/EEG channel types found"):
annotate_muscle_zscore(raw, threshold=10)
def test_muscle_annotation():
"""Test correct detection muscle artifacts."""
raw = read_raw_fif(raw_fname, allow_maxshield='yes').load_data()
raw.notch_filter([50, 110, 150])
# Check 2 muscle segments are detected
annot_muscle, scores = annotate_muscle_zscore(raw,
ch_type='mag',
threshold=10)
onset = annot_muscle.onset * raw.info['sfreq']
onset = onset.astype(int)
np.testing.assert_array_equal(scores[onset].astype(int), np.array([23,
10]))
assert (annot_muscle.duration.size == 2)
def test_muscle_annotation(meas_date, events):
"""Test correct detection muscle artifacts."""
raw = read_raw_fif(raw_fname, allow_maxshield='yes').load_data()
if meas_date is None:
raw.set_meas_date(None)
raw.notch_filter([50, 110, 150])
# Check 2 muscle segments are detected
annot_muscle, scores = annotate_muscle_zscore(raw, ch_type='mag',
threshold=10)
assert annot_muscle.orig_time == raw.info["meas_date"]
onset = annot_muscle.onset * raw.info['sfreq']
if meas_date is not None:
onset -= raw.first_samp
onset = onset.astype(int)
assert_array_equal(scores[onset].astype(int), np.array([23, 10]))
assert annot_muscle.duration.size == 2
raw.set_annotations(annot_muscle)
# .. note::
# If line noise is present, you should perform notch-filtering *before*
# detecting muscle artifacts. See :ref:`tut-section-line-noise` for an
# example.
raw.notch_filter([50, 100])
# %%
# The threshold is data dependent, check the optimal threshold by plotting
# ``scores_muscle``.
threshold_muscle = 5 # z-score
# Choose one channel type, if there are axial gradiometers and magnetometers,
# select magnetometers as they are more sensitive to muscle activity.
annot_muscle, scores_muscle = annotate_muscle_zscore(
raw, ch_type="mag", threshold=threshold_muscle, min_length_good=0.2,
filter_freq=[110, 140])
# %%
# Plot muscle z-scores across recording
# --------------------------------------------------------------------------
fig, ax = plt.subplots()
ax.plot(raw.times, scores_muscle)
ax.axhline(y=threshold_muscle, color='r')
ax.set(xlabel='time, (s)', ylabel='zscore', title='Muscle activity')
# %%
# View the annotations
# --------------------------------------------------------------------------
order = np.arange(144, 164)
raw.set_annotations(annot_muscle)