Apply ICA

Brainlife App to discard ICA components in raw data using ica.apply.

1. Input file is:
   • meg/fif meg data file
   • ica/fif ica object file
2. Parameters:
   • exclude: Component numbers to exclude (in addition to any one specified in ica.exclude).
   • reject_EOG: If True, automatically reject components related to EOG artifacts.
   • reject_ECG: If True, automatically reject components related to ECG artifacts.
   • EOG_channel: The name/number of the EOG channel(s) to use. If None, EOG channel types are used.
   • ECG_channel: The name/number of the ECG channel(s) to use. If None, ECG channel types are used.
3. Ouput files are:
   • meg/fif cleaned meg data file

Authors

• Saeed ZAHRAN(saeedzahranutc@gmail.com)
• Maximilien Chaumon(maximilien.chaumon@icm-institute.org)

Citations

We kindly ask that you cite the following articles when publishing papers and code using this code.

- brainlife.io Publishing and Apps:

Avesani, P., McPherson, B., Hayashi, S. et al. The open diffusion data derivatives, brain data upcycling via integrated publishing of derivatives and reproducible open cloud services. Sci Data 6, 69 (2019). https://doi.org/10.1038/s41597-019-0073-y

- MNE-Python package:

Gramfort A, Luessi M, Larson E, Engemann DA, Strohmeier D, Brodbeck C, Goj R, Jas M, Brooks T, Parkkonen L, and Hämäläinen MS. MEG and EEG data analysis with MNE-Python. Frontiers in Neuroscience, 7(267):1–13, 2013. https://doi.org/10.3389/fnins.2013.00267

Funding Acknowledgement

brainlife.io is publicly funded and for the sustainability of the project it is helpful to Acknowledge the use of the platform. We kindly ask that you acknowledge the funding below in your publications and code reusing this code.

MIT Copyright (c) 2021 brainlife.io The University of Texas at Austin and Indiana University