meeg_utils.qc.Muscle#
- class meeg_utils.qc.Muscle(*, threshold=4.0, filter_freq=(110.0, 140.0), ch_type=None, warn_fraction=None)[source]#
Share of the recording contaminated by muscle activity.
Uses
mne.preprocessing.annotate_muscle_zscore()with MNE’s defaults: the 110-140 Hz envelope, z-scored and averaged over channels, above 4.- Parameters:
threshold (float) – z-score threshold (MNE default 4).
filter_freq (tuple of float) – Band in which muscle activity is measured (MNE default 110-140 Hz).
ch_type (str | None) – Channel type to use;
Nonelets MNE choose (mag, then grad, then eeg).warn_fraction (float | None) – Fraction of time above which the recording is flagged. There is no established limit, and how much muscle activity is acceptable depends on the task (speaking, movement) and the analysis (high-frequency activity is affected most).
None(default) reports the fraction without a verdict.
Notes
Figures:
"scores"(muscle z-score over time with the threshold).Methods
__init__(*[, threshold, filter_freq, ...])compute(raw)Measure the recording.
get_metadata_routing()Get metadata routing of this object.
get_params([deep])Get parameters for this estimator.
not_applicable(raw)Return why the check cannot run on
raw, orNoneif it can.plot([kind, inst])Draw quality-control figures.
set_params(**params)Set the parameters of this estimator.
Attributes
levelThe worst verdict of the findings (
"ok"if there are none).modalitiesData types the check needs at least one of ("meg", "eeg").
Key of the check in reports.
kind -> whether it needs data (
inst).systemsAcquisition systems the check applies to;
Nonemeans all.