ICA finds directions in the feature space corresponding to projections with high non-Gaussianity. We thus obtain a decomposition into independent components, and the artifact’s contribution is localized in only a small number of components. These components have to be correctly identified and removed.
If EOG or ECG recordings are available, they can be used in ICA to automatically select the corresponding artifact components from the decomposition. To do so, you have to first build an Epoch object around blink or heartbeat event.
import numpy as np
import mne
from mne.datasets import sample
from mne.preprocessing import ICA
from mne.preprocessing import create_eog_epochs
# getting some data ready
data_path = sample.data_path()
raw_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw.fif'
raw = mne.io.read_raw_fif(raw_fname, preload=True)
raw.filter(1, 40, n_jobs=2) # 1Hz high pass is often helpful for fitting ICA
picks_meg = mne.pick_types(raw.info, meg=True, eeg=False, eog=False,
stim=False, exclude='bads')
Out:
Opening raw data file /home/ubuntu/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
Current compensation grade : 0
Range : 6450 ... 48149 = 42.956 ... 320.665 secs
Ready.
Reading 0 ... 41699 = 0.000 ... 277.709 secs...
Band-pass filtering from 1 - 40 Hz
Before applying artifact correction please learn about your actual artifacts by reading Introduction to artifacts and artifact detection.
ICA parameters:
n_components = 25 # if float, select n_components by explained variance of PCA
method = 'fastica' # for comparison with EEGLAB try "extended-infomax" here
decim = 3 # we need sufficient statistics, not all time points -> save time
Define the ICA object instance
ica = ICA(n_components=n_components, method=method)
print(ica)
Out:
<ICA | no decomposition, fit (fastica): samples, no dimension reduction>
we avoid fitting ICA on crazy environmental artifacts that would dominate the variance and decomposition
reject = dict(mag=5e-12, grad=4000e-13)
ica.fit(raw, picks=picks_meg, decim=decim, reject=reject)
print(ica)
Out:
Fitting ICA to data using 305 channels.
Please be patient, this may take some time
Inferring max_pca_components from picks.
Rejecting epoch based on MAG : [u'MEG 1711']
Artifact detected in [4242, 4343]
Rejecting epoch based on MAG : [u'MEG 1711']
Artifact detected in [5858, 5959]
Selection by number: 25 components
<ICA | raw data decomposition, fit (fastica): 13635 samples, 25 components, channels used: "mag"; "grad">
Plot ICA components
ica.plot_components() # can you see some potential bad guys?
Let’s use a more efficient way to find artefacts
eog_average = create_eog_epochs(raw, reject=dict(mag=5e-12, grad=4000e-13),
picks=picks_meg).average()
# We simplify things by setting the maximum number of components to reject
n_max_eog = 1 # here we bet on finding the vertical EOG components
eog_epochs = create_eog_epochs(raw, reject=reject) # get single EOG trials
eog_inds, scores = ica.find_bads_eog(eog_epochs) # find via correlation
ica.plot_scores(scores, exclude=eog_inds) # look at r scores of components
# we can see that only one component is highly correlated and that this
# component got detected by our correlation analysis (red).
ica.plot_sources(eog_average, exclude=eog_inds) # look at source time course
Out:
EOG channel index for this subject is: [375]
Filtering the data to remove DC offset to help distinguish blinks from saccades
Now detecting blinks and generating corresponding events
Number of EOG events detected : 46
46 matching events found
No baseline correction applied
Created an SSP operator (subspace dimension = 3)
Loading data for 46 events and 151 original time points ...
0 bad epochs dropped
EOG channel index for this subject is: [375]
Filtering the data to remove DC offset to help distinguish blinks from saccades
Now detecting blinks and generating corresponding events
Number of EOG events detected : 46
46 matching events found
No baseline correction applied
Created an SSP operator (subspace dimension = 4)
Loading data for 46 events and 151 original time points ...
0 bad epochs dropped
That component is also showing a prototypical average vertical EOG time course.
Pay attention to the labels, a customized read-out of the ica.labels_
print(ica.labels_)
Out:
{u'eog/0/EOG 061': [17], 'eog': [17]}
These labels were used by the plotters and are added automatically by artifact detection functions. You can also manually edit them to annotate components.
Now let’s see how we would modify our signals if we would remove this component from the data
ica.plot_overlay(eog_average, exclude=eog_inds, show=False)
# red -> before, black -> after. Yes! We remove quite a lot!
# to definitely register this component as a bad one to be removed
# there is the ``ica.exclude`` attribute, a simple Python list
ica.exclude.extend(eog_inds)
# from now on the ICA will reject this component even if no exclude
# parameter is passed, and this information will be stored to disk
# on saving
# uncomment this for reading and writing
# ica.save('my-ica.fif')
# ica = read_ica('my-ica.fif')
Out:
Transforming to ICA space (25 components)
Zeroing out 1 ICA components
Exercise: find and remove ECG artifacts using ICA!
1) make a bipolar reference from frontal EEG sensors and use as virtual EOG channel. This can be tricky though as you can only hope that the frontal EEG channels only reflect EOG and not brain dynamics in the prefrontal cortex. 2) Go for a semi-automated approach, using template matching. In MNE-Python option 2 is easily achievable and it might be better, so let’s have a look at it.
from mne.preprocessing.ica import corrmap # noqa
The idea behind corrmap is that artefact patterns are similar across subjects and can thus be identified by correlating the different patterns resulting from each solution with a template. The procedure is therefore semi-automatic. Corrmap hence takes at least a list of ICA solutions and a template, that can be an index or an array. As we don’t have different subjects or runs available today, here we will fit ICA models to different parts of the recording and then use as a user-defined template the ICA that we just fitted for detecting corresponding components in the three “new” ICAs. The following block of code addresses this point and should not be copied, ok? We’ll start by simulating a group of subjects or runs from a subject
start, stop = [0, len(raw.times) - 1]
intervals = np.linspace(start, stop, 4, dtype=int)
icas_from_other_data = list()
raw.pick_types(meg=True, eeg=False) # take only MEG channels
for ii, start in enumerate(intervals):
if ii + 1 < len(intervals):
stop = intervals[ii + 1]
print('fitting ICA from {0} to {1} seconds'.format(start, stop))
this_ica = ICA(n_components=n_components, method=method).fit(
raw, start=start, stop=stop, reject=reject)
icas_from_other_data.append(this_ica)
Out:
fitting ICA from 0 to 13899 seconds
Fitting ICA to data using 305 channels.
Please be patient, this may take some time
Inferring max_pca_components from picks.
Rejecting epoch based on MAG : [u'MEG 1711']
Artifact detected in [12642, 12943]
Selection by number: 25 components
fitting ICA from 13899 to 27799 seconds
Fitting ICA to data using 305 channels.
Please be patient, this may take some time
Inferring max_pca_components from picks.
Rejecting epoch based on MAG : [u'MEG 1711']
Artifact detected in [3612, 3913]
Selection by number: 25 components
fitting ICA from 27799 to 41699 seconds
Fitting ICA to data using 305 channels.
Please be patient, this may take some time
Inferring max_pca_components from picks.
Selection by number: 25 components
Do not copy this at home! You start by reading in a collections of ICA solutions, something like
icas = [mne.preprocessing.read_ica(fname) for fname in ica_fnames]
print(icas_from_other_data)
Out:
[<ICA | raw data decomposition, fit (fastica): 13545 samples, 25 components, channels used: "mag"; "grad">, <ICA | raw data decomposition, fit (fastica): 13545 samples, 25 components, channels used: "mag"; "grad">, <ICA | raw data decomposition, fit (fastica): 13846 samples, 25 components, channels used: "mag"; "grad">]
use our previous ICA as reference.
reference_ica = ica
Investigate our reference ICA, here we use the previous fit from above.
reference_ica.plot_components()
Which one is the bad EOG component? Here we rely on our previous detection algorithm. You will need to decide yourself in that situation where no other detection is available.
reference_ica.plot_sources(eog_average, exclude=eog_inds)
Indeed it looks like an EOG, also in the average time course.
So our template shall be a tuple like (reference_run_index, component_index):
template = (0, eog_inds[0])
Now we can do the corrmap.
fig_template, fig_detected = corrmap(
icas_from_other_data, template=template, label="blinks", show=True,
threshold=.8, ch_type='mag')
Out:
Median correlation with constructed map: 1.000
Displaying selected ICs per subject.
No maps selected for subject(s) 1, 2, consider a more liberal threshold.
Nice, we have found similar ICs from the other runs! This is even nicer if we have 20 or 100 ICA solutions in a list.
You can also use SSP for correcting for artifacts. It is a bit simpler, faster but is less precise than ICA. And it requires that you know the event timing of your artifact. See Artifact Correction with SSP.
Total running time of the script: (0 minutes 49.622 seconds)
plot_artifacts_correction_ica.py
plot_artifacts_correction_ica.ipynb