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比较 MNE 中不同的 ICA 算法

对原始 MEG 数据拟合不同的 ICA 算法，并显示相应的成分图。

# Authors: Pierre Ablin <pierreablin@gmail.com>
#
# License: BSD-3-Clause
# Copyright the MNE-Python contributors.

from time import time

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

print(__doc__)

读取并预处理数据。预处理包括：

• 选择 MEG 通道

• 1–30 Hz 带通滤波

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)

	General
	Filename(s)	sample_audvis_filt-0-40_raw.fif
	MNE object type	Raw
	Measurement date	2002-12-03 at 19:01:10 UTC
	Participant	Unknown
	Experimenter	Unknown
	Acquisition
	Duration	00:01:01 (HH:MM:SS)
	Sampling frequency	150.15 Hz
	Time points	9,010
	Channels
	Magnetometers	102
	Gradiometers	203 and 1 bad
	Head & sensor digitization	146 points
	Filters
	Highpass	1.00 Hz
	Lowpass	30.00 Hz
	Projections	PCA-v1 (off) PCA-v2 (off) PCA-v3 (off)

定义一个函数，在原始 MEG 数据上运行 ICA 并绘制成分

def run_ica(method, fit_params=None):
    ica = ICA(
        n_components=20,
        method=method,
        fit_params=fit_params,
        max_iter="auto",
        rng=29,
    )
    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 0.3s.

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 0.3s.

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 0.6s.

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 2.3s.