Labels for Epoch objects about mne-icalabel

[mscheltienne]

Thank you! I managed to reproduce it with this sample, changing the duration of epochs to 4 seconds:

from mne import make_fixed_length_epochs
from mne.datasets import sample
from mne.io import read_raw
from mne.preprocessing import ICA

from mne_icalabel import label_components


# load raw instance
directory = sample.data_path() / "MEG" / "sample"
fname = "sample_audvis_filt-0-40_raw.fif"
raw = read_raw(directory / fname, preload=False)
raw.pick_types(eeg=True)
raw.crop(tmin=0, tmax=20)
raw.load_data()

# create epochs
epochs = make_fixed_length_epochs(raw, duration=4., preload=True)

# fit an ICA on epochs
ica = ICA(n_components=5, method="picard")
ica.fit(epochs)

# run ICLabel
label_components(epochs, ica, method="iclabel")

I'll have a look.

[sourestdeeds]

Thats amazing! Thanks so much, my automated workflow is working super well now.

Hopefully i can use this to get some classification results on my super noisy data

[mscheltienne]

Hello, thank you for reporting the issue! but I can not reproduce based on your description..

from mne import make_fixed_length_epochs
from mne.datasets import sample
from mne.io import read_raw
from mne.preprocessing import ICA

from mne_icalabel import label_components


# load raw instance
directory = sample.data_path() / "MEG" / "sample"
fname = "sample_audvis_filt-0-40_raw.fif"
raw = read_raw(directory / fname, preload=False)
raw.pick_types(eeg=True)
raw.crop(tmin=0, tmax=20)
raw.load_data()

# create epochs
epochs = make_fixed_length_epochs(raw, duration=1., preload=True)

# fit an ICA on epochs
ica = ICA(n_components=5, method="picard")
ica.fit(epochs)

# run ICLabel
label_components(epochs, ica, method="iclabel")

Outputs:

>>> {'y_pred_proba': array([0.96021754, 0.4062748 , 0.92534626, 0.95375717, 0.5524776 ],
       dtype=float32),
 'labels': ['eye blink', 'other', 'brain', 'brain', 'other']}

Could you try to reproduce the issue by using a dataset from the MNE sample dataset as I did above, or could you share a minimally reproducible example with the attached files that raises the error?

[mscheltienne]

Looks similar to the issue in #38

[sourestdeeds]

If it helps I'm having a look at the line this is caused by. The shape of my raw and epochs are

epochs.get_data().shape
(909, 122, 513)
raw.get_data().shape
(122, 584600)

The index variable calculated in _eeg_rpsd_constants for raw and epochs is

# raw
(128, 9133)
# epochs
(128, 7)

[mscheltienne]

It's difficult to debug if I can not reproduce the error. Could you share some script and files to reproduce it?

For the shapes, with the example from the MNE sample dataset, I have:

epochs.get_data().shape
>>> (20, 59, 150)
raw.get_data().shape
>>>  (59, 3004)

Which looks similar to you, assuming you do have 122 EEG channels and 909 epochs.
And for the index variables in _eeg_rpsd_constants, breaking on line 274 (executing it):

mne-icalabel/mne_icalabel/iclabel/features.py

Line 274 in ecc6a91

index = index.T.astype(int)

# with inst as Epochs
>>> (150, 1)
# with inst as Raw
>>> (150, 39)

It's difficult to say if it's normal or not as it depends on the number of samples in your data and on the sampling frequency.

---

What happens if you try to re-fit a fresh ICA instance on the epochs?

from mne.preprocessing import ICA
from mne_icalabel import label_components

ica = ICA(n_components=5, method="picard")  # just to speed-up
ica.fit(epochs)
label_components(raw, ica, method="iclabel")

[mscheltienne]

Am I correct in assuming the sampling frequency of your data is 128 Hz?

[sourestdeeds]

Yeah, I downsampled the raw to 128Hz and then went

raw.resample(128)
raw.notch_filter(freqs=None, method='spectrum_fit', n_jobs=12)
raw.filter(7, 35, n_jobs=12)
raw.set_eeg_reference()

events, event_ids = mne.events_from_annotations(
    raw, verbose = False)
epochs = mne.Epochs(
    raw=raw, 
    events=events, 
    event_id=event_ids,
    preload=True, 
    tmin=0,
    tmax=4,
    baseline=None, 
    event_repeated='merge',
)

ica = mne.preprocessing.ICA(
    method='picard', fit_params=dict(extended=True, ortho=False)
)
ica.fit(epochs)
# ICA Labels
ic_labels = label_components(raw, ica, method='iclabel') # epochs object causes error
labels = ic_labels["labels"]
exclude_idx = [idx for idx, label in enumerate(labels) 
                if label not in ["brain"]]
print(f"Excluding these ICA components: {exclude_idx}")
# Apply ICA
ica.plot_overlay(epochs.average(), exclude=exclude_idx)
ica.apply(epochs, exclude=exclude_idx)

[mscheltienne]

@sourestdeeds It's fixed on main and will be fixed in release >= 0.2.0.