Compute ICA components on epochs¶

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

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 filter
epoching -0.2 to 0.5 seconds with respect to events
rejection based on peak-to-peak amplitude

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)
raw.pick_types(meg=True, eeg=False, exclude='bads', stim=True).load_data()
raw.filter(1, 30, fir_design='firwin')

# peak-to-peak amplitude rejection parameters
reject = dict(grad=4000e-13, mag=4e-12)
# 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,
                    reject=reject)

Out:

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.
Removing projector <Projection | Average EEG reference, active : False, n_channels : 60>
Reading 0 ... 41699  =      0.000 ...   277.709 secs...
Filtering raw data in 1 contiguous segment
Setting up band-pass filter from 1 - 30 Hz

FIR filter parameters
---------------------
Designing a one-pass, zero-phase, non-causal bandpass filter:
- Windowed time-domain design (firwin) method
- Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation
- Lower passband edge: 1.00
- Lower transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 0.50 Hz)
- Upper passband edge: 30.00 Hz
- Upper transition bandwidth: 7.50 Hz (-6 dB cutoff frequency: 33.75 Hz)
- Filter length: 497 samples (3.310 sec)

319 events found
Event IDs: [ 1  2  3  4  5 32]
Not setting metadata
Not setting metadata
319 matching events found
Applying baseline correction (mode: mean)
Created an SSP operator (subspace dimension = 3)
3 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, threshold='auto')

ica.plot_components(ecg_inds)

Out:

Fitting ICA to data using 305 channels (please be patient, this may take a while)
Inferring max_pca_components from picks
Loading data for 319 events and 106 original time points ...
    Rejecting  epoch based on MAG : ['MEG 1711']
    Rejecting  epoch based on MAG : ['MEG 1711']
2 bad epochs dropped
Selecting by explained variance: 125 components
Loading data for 317 events and 106 original time points ...
Fitting ICA took 26.3s.
Reconstructing ECG signal from Magnetometers
Setting up band-pass filter from 8 - 16 Hz

FIR filter parameters
---------------------
Designing a two-pass forward and reverse, zero-phase, non-causal bandpass filter:
- Windowed frequency-domain design (firwin2) method
- Hann window
- Lower passband edge: 8.00
- Lower transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 7.75 Hz)
- Upper passband edge: 16.00 Hz
- Upper transition bandwidth: 0.50 Hz (-12 dB cutoff frequency: 16.25 Hz)
- Filter length: 1502 samples (10.003 sec)

Number of ECG events detected : 284 (average pulse 61 / min.)
Not setting metadata
Not setting metadata
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
Reconstructing ECG signal from Magnetometers
Using threshold: 0.41 for CTPS ECG detection

Plot properties of ECG components:

ica.plot_properties(epochs, picks=ecg_inds)

Out:

Loading data for 317 events and 106 original time points ...
Loading data for 317 events and 106 original time points ...
    Using multitaper spectrum estimation with 7 DPSS windows
Not setting metadata
Not setting metadata
317 matching events found
No baseline correction applied
0 projection items activated
0 bad epochs dropped
Not setting metadata
Not setting metadata
317 matching events found
No baseline correction applied
0 projection items activated
0 bad epochs dropped

Plot the estimated source of detected ECG related components