Note
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Blind source separation using preconditioned ICA on EEG¶
The example runs the Picard-O algorithm proposed in:
Pierre Ablin, Jean-François Cardoso, Alexandre Gramfort “Faster ICA under orthogonal constraint” ICASSP, 2018 https://arxiv.org/abs/1711.10873
# Author: Pierre Ablin <pierre.ablin@inria.fr>
# Alexandre Gramfort <alexandre.gramfort@inria.fr>
# License: BSD 3 clause
import numpy as np
import matplotlib.pyplot as plt
import mne
from mne.datasets import sample
from scipy.stats import kurtosis
from picard import picard
print(__doc__)
Generate sample EEG data
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)
raw.filter(1, 40, n_jobs=1) # 1Hz high pass is often helpful for fitting ICA
picks = mne.pick_types(raw.info, meg=False, eeg=True, eog=False,
stim=False, exclude='bads')
random_state = 0
data = raw[picks, :][0]
data = data[:, ::2] # decimate a bit
Using default location ~/mne_data for sample...
Creating /home/circleci/mne_data
Fetching 1 file for the sample dataset ...
Downloading file 'MNE-sample-data-processed.tar.gz' from 'https://osf.io/download/86qa2?version=6' to '/home/circleci/mne_data'.
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Untarring contents of '/home/circleci/mne_data/MNE-sample-data-processed.tar.gz' to '/home/circleci/mne_data'
Attempting to create new mne-python configuration file:
/home/circleci/.mne/mne-python.json
Could not read the /home/circleci/.mne/mne-python.json json file during the writing. Assuming it is empty. Got: Expecting value: line 1 column 1 (char 0)
Download complete in 01m00s (1576.2 MB)
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 - 40 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: 40.00 Hz
- Upper transition bandwidth: 10.00 Hz (-6 dB cutoff frequency: 45.00 Hz)
- Filter length: 497 samples (3.310 s)
Run ICA on data, after reducing the dimension
Plot results
n_plots = 10
T_plots = 1000
order = np.argsort(kurtosis(Y[:, :T_plots], axis=1))[::-1]
models = [data[:n_plots], Y[order[:n_plots][::-1]]]
names = ['Observations (raw EEG)',
'ICA recovered sources']
fig, axes = plt.subplots(2, 1, figsize=(7, 7))
for ii, (model, name, ax) in enumerate(zip(models, names, axes)):
ax.set_title(name)
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
offsets = np.max(model, axis=1) - np.min(model, axis=1)
offsets = np.cumsum(offsets)
ax.plot((model[:, :T_plots] + offsets[:, np.newaxis]).T, 'k')
fig.tight_layout()
plt.show()
Total running time of the script: (1 minutes 7.103 seconds)
