Check for ICA (toward Epochs Rejection)

đź’¬ Support & Discussions
,
4

If you’re using conda (as suggested in the MNE installation instructions), you can install picard via

conda install -c conda-forge python-picard

Alternatively, you can install it via pip:

pip install python-picard

To remove eye blinks, you’ll want to follow the following approach:

  • epoch your data and run ica.fit() on your epochs to get the components
  • go back to your raw data, identify eye blinks and create epochs around the blink events. MNE-Python can do this automatically for you using mne.preprocessing.create_eog_epochs()
  • using the ICA fit you created in the first step, run ica.find_bads_eog() on the EOG epochs. This will return a list of ICs that appear to be related to ocular activity
  • You can review the effects of excluding those components by adding them to ica.exclude and running ica.plot_overlay() on the averaged EOG epochs, i.e., ica.plot_overlay(eog_epochs.average())
  • Run ica.apply() on your epochs to remove ocular artifacts.

If you’re brave & curious, you can have a look at how we’re handling it in the MNE Study Template; but be warned, the code is slightly more complex than what I’ve explained above.