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,
                    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
Reading 0 ... 41699  =      0.000 ...   277.709 secs...
Setting up band-pass filter from 1 - 20 Hz
l_trans_bandwidth chosen to be 1.0 Hz
h_trans_bandwidth chosen to be 5.0 Hz
Filter length of 991 samples (6.600 sec) selected

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 4.770 seconds)

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