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)
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.
Current compensation grade : 0
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)

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,
              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)
Inferring max_pca_components from picks
    Rejecting  epoch based on MAG : ['MEG 1711']
Artifact detected in [12642, 12943]
    Rejecting  epoch based on MAG : ['MEG 1711']
Artifact detected in [17458, 17759]
Selection by number: 20 components
Fitting ICA took 3.3s.

Picard

run_ica('picard')

Out:

Fitting ICA to data using 305 channels (please be patient, this may take a while)
Inferring max_pca_components from picks
    Rejecting  epoch based on MAG : ['MEG 1711']
Artifact detected in [12642, 12943]
    Rejecting  epoch based on MAG : ['MEG 1711']
Artifact detected in [17458, 17759]
Selection by number: 20 components
Fitting ICA took 2.5s.

Infomax

run_ica('infomax')

Out:

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

Fitting ICA took 7.7s.

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)
Inferring max_pca_components from picks
    Rejecting  epoch based on MAG : ['MEG 1711']
Artifact detected in [12642, 12943]
    Rejecting  epoch based on MAG : ['MEG 1711']
Artifact detected in [17458, 17759]
Selection by number: 20 components
Computing Extended Infomax ICA
Fitting ICA took 8.1s.

Estimated memory usage: 420 MB