Compare the different ICA algorithms in MNE

Different ICA algorithms are fit to raw MEG data, and the corresponding maps are displayed.

# Authors: Pierre Ablin <pierreablin@gmail.com>
#
# License: BSD (3-clause)

from time import time

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


print(__doc__)

Read and preprocess the data. Preprocessing consists of:

• MEG channel selection

• 1-30 Hz band-pass filter

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)

picks = mne.pick_types(raw.info, meg=True)
reject = dict(mag=5e-12, grad=4000e-13)
raw.filter(1, 30, fir_design='firwin')

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.
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)

Measurement date	December 03, 2002 19:01:10 GMT
Experimenter	Unknown
Participant	Unknown
Digitized points	146 points
Good channels	102 magnetometer, 203 gradiometer, and 59 EEG channels
Bad channels	MEG 2443, EEG 053
EOG channels	EOG 061
ECG channels	Not available
Sampling frequency	150.15 Hz
Highpass	1.00 Hz
Lowpass	30.00 Hz
Filenames	sample_audvis_filt-0-40_raw.fif
Duration	00:04:37 (HH:MM:SS)

Define a function that runs ICA on the raw MEG data and plots the components

def run_ica(method, fit_params=None):
    ica = ICA(n_components=20, method=method, fit_params=fit_params,
              max_iter='auto', random_state=0)
    t0 = time()
    ica.fit(raw, picks=picks, reject=reject)
    fit_time = time() - t0
    title = ('ICA decomposition using %s (took %.1fs)' % (method, fit_time))
    ica.plot_components(title=title)

FastICA

run_ica('fastica')

Out:

Fitting ICA to data using 305 channels (please be patient, this may take a while)
    Rejecting  epoch based on MAG : ['MEG 1711']
Artifact detected in [12642, 12943]
    Rejecting  epoch based on MAG : ['MEG 1711']
Artifact detected in [17458, 17759]
Selecting by number: 20 components
Fitting ICA took 9.9s.

Picard

run_ica('picard')

Out:

Fitting ICA to data using 305 channels (please be patient, this may take a while)
    Rejecting  epoch based on MAG : ['MEG 1711']
Artifact detected in [12642, 12943]
    Rejecting  epoch based on MAG : ['MEG 1711']
Artifact detected in [17458, 17759]
Selecting by number: 20 components
Fitting ICA took 9.7s.

Infomax

run_ica('infomax')

Out:

Fitting ICA to data using 305 channels (please be patient, this may take a while)
    Rejecting  epoch based on MAG : ['MEG 1711']
Artifact detected in [12642, 12943]
    Rejecting  epoch based on MAG : ['MEG 1711']
Artifact detected in [17458, 17759]
Selecting by number: 20 components

Fitting ICA took 6.6s.

Extended Infomax

run_ica('infomax', fit_params=dict(extended=True))

Out:

Fitting ICA to data using 305 channels (please be patient, this may take a while)
    Rejecting  epoch based on MAG : ['MEG 1711']
Artifact detected in [12642, 12943]
    Rejecting  epoch based on MAG : ['MEG 1711']
Artifact detected in [17458, 17759]
Selecting by number: 20 components
Computing Extended Infomax ICA
Fitting ICA took 16.8s.

Estimated memory usage: 511 MB

Visualise NIRS artifact correction methods Interpolate bad channels for MEG/EEG channels