[Mne_analysis] EOG artifac correction with ICA

Tue Aug 8 10:15:23 EDT 2017

Hi Maria,

The many correlated components indicate that the ICA solution might be of
poor quality.
Is there a reason why you use 25 components?
I'd recommend to use as many components as the number of orthogonal
dimensions in your data, e.g. 64, depending on your SSS / maxfilter
settings.
Also the score is quite low at 1.2. It will make you include many
uninteresting components.
Maybe you want to give it a try and we have another look?

Denis

On Tue, 8 Aug 2017 at 15:01, Denis-Alexander Engemann <
denis.engemann at gmail.com> wrote:

> Hi Maria,
>
> I'm currently traveling. I'll get back to you later.
>
> Denis
>
> On Sat, Aug 5, 2017 at 12:21 PM Maria Hakonen <maria.hakonen at gmail.com>
> wrote:
>
>> 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:
>>
>>
>> [image: ak_sentences16b_scores.png]
>>
>>
>>
>> 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:
>>
>> [image: ak_sentences16b_scores2.png]
>>
>>
>> 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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