Labeling ICA components with a GUI#

This tutorial covers how to label ICA components with a GUI.

Note

Similar to mne-qt-browser, we require the users to install a specific version of Qt. Our installation pip install mne-icalabel[gui] will not install any specific Qt version. Therefore, one can install Qt5 through either PyQt5 or PySide2 or a more modern Qt6 through either PyQt6 or PySide6 depending on their system. The users should install this separately to use the GUI functionality. See: https://www.riverbankcomputing.com/software/pyqt/ for more info on installing.

Warning

The GUI is still in active development, and may contain bugs, or changes without deprecation in future versions.

import os

import mne
from mne.preprocessing import ICA

from mne_icalabel.gui import label_ica_components

Load in some sample data

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)

# Here we'll crop to 60 seconds and drop gradiometer channels for speed
raw.crop(tmax=60.0).pick_types(meg="mag", eeg=True, stim=True, eog=True)
raw.load_data()

Opening raw data file /home/runner/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.
NOTE: pick_types() is a legacy function. New code should use inst.pick(...).
Reading 0 ... 9009  =      0.000 ...    59.999 secs...

	General
	Filename(s)	sample_audvis_filt-0-40_raw.fif
	MNE object type	Raw
	Measurement date	2002-12-03 at 19:01:10 UTC
	Participant	Unknown
	Experimenter	Unknown
	Acquisition
	Duration	00:00:60 (HH:MM:SS)
	Sampling frequency	150.15 Hz
	Time points	9,010
	Channels
	Magnetometers
	EEG
	EOG
	Stimulus
	Head & sensor digitization	146 points
	Filters
	Highpass	0.10 Hz
	Lowpass	40.00 Hz
	Projections	PCA-v1 (off) PCA-v2 (off) PCA-v3 (off) Average EEG reference (off)

Preprocess and run ICA on the data#

Before labeling components with the GUI, one needs to filter the data and then fit the ICA instance. Afterwards, one can run the GUI using the Raw data object and the fitted ICA instance.

# high-pass filter the data and then perform ICA
filt_raw = raw.copy().filter(l_freq=1.0, h_freq=None)
ica = ICA(n_components=15, max_iter="auto", random_state=97)
ica.fit(filt_raw)

Filtering raw data in 1 contiguous segment
Setting up high-pass filter at 1 Hz

FIR filter parameters
---------------------
Designing a one-pass, zero-phase, non-causal highpass filter:
- Windowed time-domain design (firwin) method
- Hamming window with 0.0194 passband ripple and 53 dB stopband attenuation
- Lower passband edge: 1.00
- Lower transition bandwidth: 1.00 Hz (-6 dB cutoff frequency: 0.50 Hz)
- Filter length: 497 samples (3.310 s)

[Parallel(n_jobs=1)]: Done  17 tasks      | elapsed:    0.0s
[Parallel(n_jobs=1)]: Done  71 tasks      | elapsed:    0.0s
[Parallel(n_jobs=1)]: Done 161 tasks      | elapsed:    0.1s
[Parallel(n_jobs=1)]: Done 161 tasks      | elapsed:    0.1s
Fitting ICA to data using 161 channels (please be patient, this may take a while)
Selecting by number: 15 components
Fitting ICA took 0.3s.

Method	fastica
Fit parameters	algorithm=parallel fun=logcosh fun_args=None max_iter=1000
Fit	22 iterations on raw data (9010 samples)
ICA components	15
Available PCA components	161
Channel types	mag, eeg
ICA components marked for exclusion	—

Annotate ICA components with the GUI#

The GUI will modify the ICA instance in place, and add the labels of each component to the labels_ attribute. The GUI will show features of the ICA components similar to the mne.viz.plot_ica_properties() function. It will also provide an interface to label each ICA component into one of seven categories:

Brain
Muscle
Eye
Heart
Line Noise
Channel Noise
Other

For more information on annotating ICA components, we suggest reading through the tutorial from ICLabel (https://labeling.ucsd.edu/tutorial/about).

gui = label_ica_components(raw, ica)

# The `ica` object is modified to contain the component labels
# after closing the GUI and can now be saved
# gui.close()  # typically you close when done

# Now, we can take a look at the components, which were modified in-place
# for the ICA instance.
print(ica.labels_)

    Using multitaper spectrum estimation with 7 DPSS windows
Not setting metadata
30 matching events found
No baseline correction applied
0 projection items activated
Creating RawArray with float64 data, n_channels=2, n_times=9010
    Range : 6450 ... 15459 =     42.956 ...   102.954 secs
Ready.
{'brain': [], 'muscle': [], 'eog': [], 'ecg': [], 'line_noise': [], 'ch_noise': [], 'other': []}

Save the labeled components#

After the GUI labels, save the components using the write_components_tsv function. This will save the ICA annotations to disc in BIDS-Derivative for EEG data format.

Note: BIDS-EEG-Derivatives is not fully specified, so this functionality may change in the future without notice.

# fname = '<some path to save the components>'
# write_components_tsv(ica, fname)

Total running time of the script: (0 minutes 2.481 seconds)

Estimated memory usage: 264 MB

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