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=1, 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
The number of jobs to run in parallel (default 1). Requires the joblib package.
- verbosebool,
str
,int
, orNone
If not None, override default verbose level (see
mne.verbose()
and Logging documentation for more). If used, it should be passed as a keyword-argument only.
- 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
- 1
Suresh Muthukumaraswamy. High-frequency brain activity and muscle artifacts in MEG/EEG: a review and recommendations. Frontiers in Human Neuroscience, 7:138, 2013. doi:10.3389/fnhum.2013.00138.