- MNE-Python version: 0.23
- Mac OS 11.3.1, Python 3.9
I’m trying to learn more about detecting various types of artifacts. My raw data comes from surface electrodes and I’ve performed notch filtering to remove line noise from my raw file.
I can detect muscular artifacts using the documentation described at Annotate muscle artifacts — MNE 0.23.4 documentation
threshold_muscle = 5 annot_muscle, scores_muscle = annotate_muscle_zscore(raw, ch_type="eeg", threshold=threshold_muscle, min_length_good=0.2,filter_freq=[110,140]) order = np.arange(144, 164) raw.set_annotations(annot_muscle) raw.plot(start=5, duration=20, order=order)
My starts and durations are stored in the lists
My questions are as follows:
Is there a way to choose a reasonable
threshold_musclevalue using the zscore - Muscle Activity graph? For example, could I take the mean of the values on the graph plus one or two standard deviations?
What do the values 144 and 164 in
If I’m interested in ocular artifacts, I would use
mn.preprocessing.find_eog_eventsalong with the directions at Overview of artifact detection — MNE 0.23.4 documentation.
Now my starts are stored in the list
raw.annotations.onset (different from that above) and my durations are all .5 sec.
What is the most efficient means to combine the starts and durations for the muscular artifacts and the starts and durations for the ocular artifacts so as to plot my raw data with both types of artifacts highlighted but in different colors?