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

Out:

Using default location ~/mne_data for sample...
Creating ~/mne_data
Downloading archive MNE-sample-data-processed.tar.gz to /home/circleci/mne_data
Downloading https://files.osf.io/v1/resources/rxvq7/providers/osfstorage/59c0e26f9ad5a1025c4ab159?version=5&action=download&direct (1.54 GB)

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Verifying hash 12b75d1cb7df9dfb4ad73ed82f61094f.
Decompressing the archive: /home/circleci/mne_data/MNE-sample-data-processed.tar.gz
(please be patient, this can take some time)
Successfully extracted to: ['/home/circleci/mne_data/MNE-sample-data']
Attempting to create new mne-python configuration file:
/home/circleci/.mne/mne-python.json
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 sec)

Run ICA on data, after reducing the dimension

K, W, Y = picard(data, n_components=30, ortho=True, random_state=0)

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()
Observations (raw EEG), ICA recovered sources

Total running time of the script: ( 1 minutes 26.914 seconds)

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