Note
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Plot channel-level thresholds#
This example demonstrates how to use autoreject to find
channel-wise thresholds.
# Author: Mainak Jas <mainak.jas@telecom-paristech.fr>
# License: BSD-3-Clause
Let us first load the raw data using mne.io.read_raw_fif().
import mne
from mne import io
from mne.datasets import sample
data_path = sample.data_path()
meg_path = data_path / 'MEG' / 'sample'
raw_fname = meg_path / 'sample_audvis_filt-0-40_raw.fif'
raw = io.read_raw_fif(raw_fname, preload=True)
Opening raw data file /home/circleci/mne_data/MNE-sample-data/MEG/sample/sample_audvis_filt-0-40_raw.fif...
Read a total of 4 projection items:
PCA-v1 (1 x 102) idle
PCA-v2 (1 x 102) idle
PCA-v3 (1 x 102) idle
Average EEG reference (1 x 60) idle
Range : 6450 ... 48149 = 42.956 ... 320.665 secs
Ready.
Reading 0 ... 41699 = 0.000 ... 277.709 secs...
We can extract the events (or triggers) for epoching our signal.
event_fname = meg_path / 'sample_audvis_filt-0-40_raw-eve.fif'
event_id = {'Auditory/Left': 1}
tmin, tmax = -0.2, 0.5
events = mne.read_events(event_fname)
Now that we have the events, we can extract the trials for the selection
of channels defined by picks.
epochs = mne.Epochs(raw, events, event_id, tmin, tmax,
baseline=(None, 0),
reject=None, verbose=False, preload=True)
picks = mne.pick_types(epochs.info, meg='grad', eeg=False, stim=False,
eog=False, exclude='bads')
Now, we compute the channel-level thresholds using
autoreject.compute_thresholds(). The method parameter will determine
how we will search for thresholds over a range of potential candidates.
import numpy as np # noqa
from autoreject import compute_thresholds # noqa
# Get a dictionary of rejection thresholds
threshes = compute_thresholds(epochs, picks=picks, method='random_search',
random_state=42, augment=False,
verbose=True)
/home/circleci/project/autoreject/utils.py:73: UserWarning: 2 channels are marked as bad. These will be ignored. If you want them to be considered by autoreject please remove them from epochs.info["bads"].
warnings.warn(
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Finally, let us plot a histogram of the channel-level thresholds to verify that the thresholds are indeed different for different sensors.
import matplotlib.pyplot as plt # noqa
from autoreject import set_matplotlib_defaults # noqa
set_matplotlib_defaults(plt)
unit = r'fT/cm'
scaling = 1e13
plt.figure(figsize=(6, 5))
plt.tick_params(axis='x', which='both', bottom='off', top='off')
plt.tick_params(axis='y', which='both', left='off', right='off')
plt.hist(scaling * np.array(list(threshes.values())), 30,
color='g', alpha=0.4)
plt.xlabel('Threshold (%s)' % unit)
plt.ylabel('Number of sensors')
plt.xlim((100, 950))
plt.tight_layout()
plt.show()
Total running time of the script: (0 minutes 13.913 seconds)