Note
Go to the end to download the full example code.
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
# Copyright the MNE-Python contributors.
Read and preprocess the data. Preprocessing consists of:
MEG channel selection
1-30 Hz band-pass filter
data_path = sample.data_path()
meg_path = data_path / "MEG" / "sample"
raw_fname = meg_path / "sample_audvis_filt-0-40_raw.fif"
raw = mne.io.read_raw_fif(raw_fname).crop(0, 60).pick("meg").load_data()
reject = dict(mag=5e-12, grad=4000e-13)
raw.filter(1, 30, fir_design="firwin")
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 ... 9009 = 0.000 ... 59.999 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 s)
[Parallel(n_jobs=1)]: Done 17 tasks | elapsed: 0.0s
[Parallel(n_jobs=1)]: Done 71 tasks | elapsed: 0.0s
[Parallel(n_jobs=1)]: Done 161 tasks | elapsed: 0.1s
[Parallel(n_jobs=1)]: Done 287 tasks | elapsed: 0.1s
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, reject=reject)
fit_time = time() - t0
title = f"ICA decomposition using {method} (took {fit_time:.1f}s)"
ica.plot_components(title=title)
FastICA
run_ica("fastica")
Fitting ICA to data using 305 channels (please be patient, this may take a while)
Selecting by number: 20 components
Fitting ICA took 0.5s.
Picard
run_ica("picard")
Fitting ICA to data using 305 channels (please be patient, this may take a while)
Selecting by number: 20 components
Fitting ICA took 1.2s.
Infomax
run_ica("infomax")
Fitting ICA to data using 305 channels (please be patient, this may take a while)
Selecting by number: 20 components
Computing Infomax ICA
Fitting ICA took 1.4s.
Extended Infomax
run_ica("infomax", fit_params=dict(extended=True))
Fitting ICA to data using 305 channels (please be patient, this may take a while)
Selecting by number: 20 components
Computing Extended Infomax ICA
Fitting ICA took 2.4s.
Total running time of the script: (0 minutes 18.900 seconds)