ica.plot_overlay data disappears

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1
  • os: Windows 11
  • python: 3.10.6

During ICA my data disappears
(and the code has worked fine in a previous project)

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Then I run ICA (inside a class - therefore “self”)

self.ica = mne.preprocessing.ICA(n_components=8, max_iter=10000, random_state=97)
self.ica.fit(self.raw_arr)
self.ica.plot_sources(self.raw_arr, title=f'ICA components sub-{self.sub_nr}_ses-{self.ses_nr}_run-{self.run_nr}', show_scrollbars=False)
self.ica.plot_components(colorbar=True, ch_type = self.ch_type , reject='auto')

Which displays everything fine.

Its is not until I plot_overlay that the data disappears

self.ica.plot_overlay(self.raw_arr, exclude=components, picks='eeg', show = True)

image

Even when I try to exclude no components, the results after cleaning are always straight lines.

Does anybody know how to fix this?

EDIT:
Also, when I try to actually exclude the components, the reconstructed array is left without data as well.

self.ica.exclude = components
self.reconst_arr = self.raw_arr.copy()
self.ica.apply(self.reconst_arr)

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2

What is the value of components? It looks like you are excluding all components, which would explain why the data is just flat lines. BTW, if you really have only 8 channels ICA might not be the best method (to remove artifacts and/or separate sources), usually people recommend at least 32 channels.

3

I’ve tried to remove with components = [0] for ex.
No use no matter what I do. The function calls are later in the code as I have built a class for filtering.

Yes, I only have 8 channels, so I’ll look into other possible methods to artifact removal later on

4

I cannot reproduce with example data:

import os
import mne
from mne.preprocessing import ICA

sample_data_folder = mne.datasets.sample.data_path()
sample_data_raw_file = os.path.join(sample_data_folder, 'MEG', 'sample',
                                    'sample_audvis_filt-0-40_raw.fif')
raw = mne.io.read_raw_fif(sample_data_raw_file)
raw.crop(tmax=200).pick_types(eeg=True).pick_channels(raw.ch_names[:8])
raw.load_data()
filt_raw = raw.copy().filter(1, None)
ica = ICA(random_state=97)
ica.fit(filt_raw)

ica.plot_overlay(filt_raw, exclude=[0])

However, if I exclude all components, then the black signals (after cleaning) are flat lines just like in your example:

ica.plot_overlay(filt_raw, exclude=list(range(8)))

I am pretty sure that this is what is happening, so please double-check the actual value of your exclude argument as well as ica.exclude.

If this is not the case, we’ll need to see a reproducible example with your data to further investigate.

1 Like
5

Okey, so I have made a class called Recording, which includes different functions for preprocessing and ICA.

Everything works fine until I try to run test.test_exclude(components=[0,1])
which in turn runs

def test_exclude(self, components):
        self.ica.plot_overlay(self.raw_arr, exclude=components, picks='eeg', show = True)

I have tried running it with no components, all components and a subset. It always yields the same results.

Also, if I skip this step, and go straight to excluding and reconstructing
test.exclude_ICA(components=[0])
which runs

def exclude_ICA(self, components):
        self.ica.exclude = components
        self.reconst_arr = self.raw_arr.copy()
        self.ica.apply(self.reconst_arr)

None which yields any data

I’m very stumped

6

Some ideas:

  • Can you post the output when you run these two functions?
  • Can you also print your ICA object (i.e. self) at various stages (before and after excluding at least)?
  • Can you remove the exclude parts in these functions to see if it works?
  • Can you print components inside the function just to make sure that nothing funny is going on (like list default args)?
1 Like