mne.preprocessing.annotate_muscle_zscore#
- mne.preprocessing.annotate_muscle_zscore(raw, threshold=4, ch_type=None, min_length_good=0.1, filter_freq=(110, 140), n_jobs=None, verbose=None)[source]#
Create annotations for segments that likely contain muscle artifacts.
Detects data segments containing activity in the frequency range given by
filter_freq
whose envelope magnitude exceeds the specified z-score threshold, when summed across channels and divided bysqrt(n_channels)
. False-positive transient peaks are prevented by low-pass filtering the resulting z-score time series at 4 Hz. Only operates on a single channel type, ifch_type
isNone
it will select the first type in the listmag
,grad
,eeg
. See [1] for background on choosingfilter_freq
andthreshold
.- Parameters:
- rawinstance of
Raw
Data to estimate segments with muscle artifacts.
- threshold
float
The threshold in z-scores for marking segments as containing muscle activity artifacts.
- ch_type‘mag’ | ‘grad’ | ‘eeg’ |
None
The type of sensors to use. If
None
it will take the first type inmag
,grad
,eeg
.- min_length_good
float
|None
The shortest allowed duration of “good data” (in seconds) between adjacent annotations; shorter segments will be incorporated into the surrounding annotations.``None`` is equivalent to
0
. Default is0.1
.- filter_freqarray_like, shape (2,)
The lower and upper frequencies of the band-pass filter. Default is
(110, 140)
.- n_jobs
int
|None
The number of jobs to run in parallel. If
-1
, it is set to the number of CPU cores. Requires thejoblib
package.None
(default) is a marker for ‘unset’ that will be interpreted asn_jobs=1
(sequential execution) unless the call is performed under ajoblib.parallel_backend()
context manager that sets another value forn_jobs
.- verbose
bool
|str
|int
|None
Control verbosity of the logging output. If
None
, use the default verbosity level. See the logging documentation andmne.verbose()
for details. Should only be passed as a keyword argument.
- rawinstance of
- Returns:
- annot
mne.Annotations
Periods with muscle artifacts annotated as BAD_muscle.
- scores_muscle
array
Z-score values averaged across channels for each sample.
- annot
References