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 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.

Parameters:

rawinstance of Raw: Data to estimate segments with muscle artifacts.
thresholdfloat: 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_backend() 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.

Returns:

annotmne.Annotations: Periods with muscle artifacts annotated as BAD_muscle.
scores_musclearray: Z-score values averaged across channels for each sample.

References

Examples using `mne.preprocessing.annotate_muscle_zscore`#

Annotate muscle artifacts

mne.preprocessing.annotate_muscle_zscore#

Examples using mne.preprocessing.annotate_muscle_zscore#

Examples using `mne.preprocessing.annotate_muscle_zscore`#