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 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.

rawinstance of Raw

Data to estimate segments with muscle artifacts.


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 in mag, grad, eeg.

min_length_goodfloat | 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.

filter_freqarray_like, shape (2,)

The lower and upper frequencies of the band-pass filter. Default is (110, 140).

n_jobsint | 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_config context manager that sets another value for n_jobs.

verbosebool | 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.


Periods with muscle artifacts annotated as BAD_muscle.


Z-score values averaged across channels for each sample.


Examples using mne.preprocessing.annotate_muscle_zscore#

Annotate muscle artifacts

Annotate muscle artifacts