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 by sqrt(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, if ch_type
is None
it will select the first type in the list
mag
, grad
, eeg
.
See [1] for background on choosing
filter_freq
and threshold
.
Raw
Data to estimate segments with muscle artifacts.
float
The threshold in z-scores for marking segments as containing muscle activity artifacts.
None
The type of sensors to use. If None
it will take the first type in
mag
, grad
, eeg
.
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 is 0.1
.
The lower and upper frequencies of the band-pass filter.
Default is (110, 140)
.
int
| None
The number of jobs to run in parallel. If -1
, it is set
to the number of CPU cores. Requires the joblib
package.
None
(default) is a marker for ‘unset’ that will be interpreted
as n_jobs=1
(sequential execution) unless the call is performed under
a joblib.parallel_backend()
context manager that sets another
value for n_jobs
.
str
| int
| None
Control verbosity of the logging output. If None
, use the default
verbosity level. See the logging documentation and
mne.verbose()
for details. Should only be passed as a keyword
argument.
mne.Annotations
Periods with muscle artifacts annotated as BAD_muscle.
array
Z-score values averaged across channels for each sample.
References
mne.preprocessing.annotate_muscle_zscore
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