Analysis of evoked response using ICA and PCA reduction techniquesΒΆ

This example computes PCA and ICA of evoked or epochs data. Then the PCA / ICA components, a.k.a. spatial filters, are used to transform the channel data to new sources / virtual channels. The output is visualized on the average of all the epochs.

# Authors: Jean-Remi King <jeanremi.king@gmail.com>
#          Asish Panda <asishrocks95@gmail.com>
#
# License: BSD (3-clause)

import numpy as np
import matplotlib.pyplot as plt

import mne
from mne.datasets import sample
from mne.decoding import UnsupervisedSpatialFilter

from sklearn.decomposition import PCA, FastICA

print(__doc__)

# Preprocess data
data_path = sample.data_path()

# Load and filter data, set up epochs
raw_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw.fif'
event_fname = data_path + '/MEG/sample/sample_audvis_filt-0-40_raw-eve.fif'
tmin, tmax = -0.1, 0.3
event_id = dict(aud_l=1, aud_r=2, vis_l=3, vis_r=4)

raw = mne.io.read_raw_fif(raw_fname, preload=True)
raw.filter(1, 20)
events = mne.read_events(event_fname)

picks = mne.pick_types(raw.info, meg=False, eeg=True, stim=False, eog=False,
                       exclude='bads')

epochs = mne.Epochs(raw, events, event_id, tmin, tmax, proj=False,
                    picks=picks, baseline=None, preload=True,
                    add_eeg_ref=False, verbose=False)

X = epochs.get_data()

Out:

Opening raw data file /home/ubuntu/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
add_eeg_ref defaults to True in 0.13, will default to False in 0.14, and will be removed in 0.15. We recommend to use add_eeg_ref=False and set_eeg_reference() instead.
Reading 0 ... 41699  =      0.000 ...   277.709 secs...
Band-pass filtering from 1 - 20 Hz
Multiple deprecated filter parameters were used:
phase in 0.13 is "zero-double" but will change to "zero" in 0.14
fir_window in 0.13 is "hann" but will change to "hamming" in 0.14
lower transition bandwidth in 0.13 is 0.5 Hz but will change to "auto" in 0.14
upper transition bandwidth in 0.13 is 0.5 Hz but will change to "auto" in 0.14
The default filter length in 0.13 is "10s" but will change to "auto" in 0.14

Transform data with PCA computed on the average ie evoked response

pca = UnsupervisedSpatialFilter(PCA(30), average=False)
pca_data = pca.fit_transform(X)
ev = mne.EvokedArray(np.mean(pca_data, axis=0),
                     mne.create_info(30, epochs.info['sfreq'],
                                     ch_types='eeg'), tmin=tmin)
ev.plot(show=False, window_title="PCA")
../../_images/sphx_glr_plot_decoding_unsupervised_spatial_filter_001.png

Transform data with ICA computed on the raw epochs (no averaging)

ica = UnsupervisedSpatialFilter(FastICA(30), average=False)
ica_data = ica.fit_transform(X)
ev1 = mne.EvokedArray(np.mean(ica_data, axis=0),
                      mne.create_info(30, epochs.info['sfreq'],
                                      ch_types='eeg'), tmin=tmin)
ev1.plot(show=False, window_title='ICA')

plt.show()
../../_images/sphx_glr_plot_decoding_unsupervised_spatial_filter_002.png

Total running time of the script: ( 0 minutes 5.166 seconds)

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