Compute ICA components on epochsΒΆ

ICA is fit to MEG raw data. We assume that the non-stationary EOG artifacts have already been removed. The sources matching the ECG are automatically found and displayed.

Note that this example does quite a bit of processing, so even on a fast machine it can take about a minute to complete.

# Authors: Denis Engemann <denis.engemann@gmail.com>
#
# License: BSD (3-clause)

import mne
from mne.preprocessing import ICA, create_ecg_epochs
from mne.datasets import sample

print(__doc__)

Read and preprocess the data. Preprocessing consists of:

  • meg channel selection
  • 1 - 30 Hz band-pass IIR filter
  • epoching -0.2 to 0.5 seconds with respect to events
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.pick_types(meg=True, eeg=False, exclude='bads', stim=True)
raw.filter(1, 30, method='iir')

# longer + more epochs for more artifact exposure
events = mne.find_events(raw, stim_channel='STI 014')
epochs = mne.Epochs(raw, events, event_id=None, tmin=-0.2, tmax=0.5)

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
    Range : 6450 ... 48149 =     42.956 ...   320.665 secs
Ready.
Current compensation grade : 0
add_eeg_ref defaults to True in 0.13, will default to False in 0.14, and will be removed in 0.15. We recommend to use add_eeg_ref=False and set_eeg_reference() instead.
Reading 0 ... 41699  =      0.000 ...   277.709 secs...
Band-pass filtering from 1 - 30 Hz
319 events found
Events id: [ 1  2  3  4  5 32]
319 matching events found
Applying baseline correction (mode: mean)
Created an SSP operator (subspace dimension = 3)
4 projection items activated

Fit ICA model using the FastICA algorithm, detect and plot components explaining ECG artifacts.

ica = ICA(n_components=0.95, method='fastica').fit(epochs)

ecg_epochs = create_ecg_epochs(raw, tmin=-.5, tmax=.5)
ecg_inds, scores = ica.find_bads_ecg(ecg_epochs)

ica.plot_components(ecg_inds)
../../_images/sphx_glr_plot_run_ica_001.png

Out:

Fitting ICA to data using 305 channels.
Please be patient, this may take some time
Inferring max_pca_components from picks.
Loading data for 319 events and 106 original time points ...
0 bad epochs dropped
Selection by explained variance: 120 components
Loading data for 319 events and 106 original time points ...
Reconstructing ECG signal from Magnetometers
Band-pass filtering from 8 - 16 Hz
Number of ECG events detected : 284 (average pulse 61 / min.)
Creating RawArray with float64 data, n_channels=1, n_times=41700
    Range : 0 ... 41699 =      0.000 ...   277.709 secs
Ready.
284 matching events found
No baseline correction applied
Created an SSP operator (subspace dimension = 3)
Loading data for 284 events and 151 original time points ...
0 bad epochs dropped

Plot properties of ECG components:

ica.plot_properties(epochs, picks=ecg_inds)
  • ../../_images/sphx_glr_plot_run_ica_002.png
  • ../../_images/sphx_glr_plot_run_ica_003.png
  • ../../_images/sphx_glr_plot_run_ica_004.png

Out:

Loading data for 319 events and 106 original time points ...

Total running time of the script: ( 1 minutes 21.660 seconds)

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