Dear mne experts,
Could someone please help me with two questions related to the EOG artifact
removal from raw MEG data with ICA?
I have created my script following this example:
https://martinos.org/mne/stable/auto_tutorials/plot_artifacts_correction_ica.html?highlight=ica
I tested the script with one EOG channel and got the following result:
Question 1: Why are components 5 and 12 marked with red even if there are
other components with stronger (although negative) correlation (e.g. 13 and
16)?
Thereafter, I tested the script with two EOG channels and got:
Question 2: Why are the correlation values with EOG61 now different
compared to the case when I only used EOG61 and not EOG62?
I am using mne 0.14.1.
Many thanks already in advance if you can help!
Best,
Maria
Here is yet the code that I am using (I have used low threshold value for
testing purposes, but will use higher threshold, e.g. 3, in the final
analysis):
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import numpy as np
import mne
from mne.preprocessing import ICA
from mne.preprocessing import create_eog_epochs
subject = 'ak'
session = 'sentences16b'
data_path = '/m/nbe/scratch/braindata/mhhakone/intell/TaskIII/MEG-data/' +
subject + '/'
raw_fname = data_path + subject + '_' + session + '_raw_tsss.fif'
raw = mne.io.read_raw_fif(raw_fname, preload=True)
raw.filter(1, 40, n_jobs=2)
picks_meg = mne.pick_types(raw.info, meg=True, eeg=False, eog=False,
stim=False, exclude='bads')
n_components = 25 # if float, select n_components by explained variance of
PCA
method = 'fastica' # for comparison with EEGLAB try "extended-infomax" here
decim = 3 # we need sufficient statistics, not all time points -> saves
time
# we will also set state of the random number generator - ICA is a
# non-deterministic algorithm, but we want to have the same decomposition
# and the same order of components each time this tutorial is run
random_state = 23
ica = ICA(n_components=n_components, method=method,
random_state=random_state)
print(ica)
reject = dict(mag=5e-12, grad=4000e-13)
ica.fit(raw, picks=picks_meg, decim=decim, reject=reject)
#ica.save('my-ica.fif')
eog_average = create_eog_epochs(raw,ch_name='EOG 061',
reject=dict(mag=5e-12, grad=4000e-13),
picks=picks_meg).average()
eog_epochs = create_eog_epochs(raw,ch_name='EOG 061',reject=reject) # get
single EOG trials
eog_inds, scores = ica.find_bads_eog(eog_epochs,ch_name='EOG
061',threshold=1.2) # find via correlation
ica.plot_scores(scores, exclude=eog_inds) # look at r scores of components
file_end='_scores.png'
filename = subject+'_'+session+file_end
plt.savefig(data_path+filename)
plt.close()
ica.plot_sources(eog_average, exclude=eog_inds) # look at source time
course
file_end='_sources.png'
filename = subject+'_'+session+file_end
plt.savefig(data_path+filename)
plt.close()
fig_list = ica.plot_properties(eog_epochs, picks=eog_inds,
psd_args={'fmax': 35.},
image_args={'sigma': 1.})
for i in range(len(fig_list)):
file_end='_properties_component'+str(i)+'.png'
filename = subject+'_'+session+file_end
fig_list[i].savefig(data_path+filename)
plt.close()
ica.plot_overlay(eog_average, exclude=eog_inds, show=False)
file_end='_overlay.png'
filename = subject+'_'+session+file_end
plt.savefig(data_path+filename)
plt.close()
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