ICLabel & removing components

In the GUI

1. Tools › Classify components using ICLabel › Label components
2. Select which icversion of ICLabel to use:Default (recommended)OK
3. Tools › Classify components using ICLabel › Flag components as artifacts
4. Select range for flagging component for rejectionMinMaxMuscleEye
5. OK
6. Tools › Remove components from data
7. Manual rej.YesYes
8. List of component(s) to remove from dataOK
9. Plot single trials
10. Accept
11. File › Save current dataset asOverwrite it in memory (set=yes; unset=create a new dataset) ← D1 main cleanD1_main_clean.set

Script

EEG = pop_iclabel(EEG, 'default');
classes = EEG.etc.ic_classification.ICLabel.classes;
probs   = EEG.etc.ic_classification.ICLabel.classifications;
disp(classes);
disp(round(probs(1:10, :) * 100));
thresh = [NaN NaN; 0.9 1; 0.9 1; NaN NaN; NaN NaN; NaN NaN; NaN NaN];
EEG = pop_icflag(EEG, thresh);
artifactComps = find(EEG.reject.gcompreject);
addpath(fullfile(fileparts(which('iclabel')), 'viewprops'));
if ~isempty(artifactComps)
    pop_viewprops(EEG, 0, artifactComps);
end
if ~isempty(artifactComps)
    EEG = pop_subcomp(EEG, artifactComps, 0);
end
EEG.setname = 'D1 main clean';
EEG = pop_saveset(EEG, 'filename', 'D1_main_clean.set', 'filepath', pwd);
EEG = pop_eegfiltnew(EEG, 'hicutoff', 30);
EEG.setname = 'D1 main clean lp30';
EEG = pop_saveset(EEG, 'filename', 'D1_main_clean_lp30.set', 'filepath', pwd);

Which value should I choose?

Check

• Compare the signal
• Count the components
• Open the component maps

For your Methods

Components were classified with ICLabel (version 1.7; Pion-Tonachini et al., 2019), and components with a probability above 0.9 for the Eye or Muscle class were reviewed visually and removed ([N] components). Data were then low-pass filtered (passband edge 30 Hz, cutoff frequency [33.64] Hz at −6 dB, filter length [59] points; pop_eegfiltnew, firfilt plugin 2.8).