Apply ICA (reject Components) to Epoched Data

Description

This Brainlife App applies Independent Component Analysis (ICA) decomposition to epoched MEG/EEG data using MNE-Python's ICA.apply(). The app reads an ICA object, excludes identified bad components (automatically detected for EOG/ECG artifacts or manually specified), and reconstructs the epoched data before saving it.

The app generates:

• Epoched data with the ICA components applied (meg-epo.fif)
• An overlay visualization comparing the epoched data before and after ICA application
• An HTML QC report with the ICA decomposition and excluded-component details
• A product.json summarizing the excluded components

Inputs

• epo (neuro/meeg/mne/epochs): epoched MEG/EEG data to apply ICA to (required)
• ica (neuro/meeg/mne/ica): fitted ICA decomposition object, e.g. from ICA-fit (required)

Outputs

• out_dir/meg-epo.fif (neuro/meeg/mne/epochs): epoched data with the ICA components applied
• out_figs/plot_overlay.png (generic/image/png): visualization of the ICA overlay before application
• out_report/report.html (report/html): quality control report with ICA information
• product.json: metadata about the applied ICA for the Brainlife.io interface

Configuration Parameters

key	type	default	description
exclude	string	""	Comma-separated list of component indices to exclude, in addition to any already in ica.exclude. The union of the two is excluded.
reject_EOG	boolean	false	Whether to automatically detect and exclude EOG artifact components using mne.preprocessing.ICA.find_bads_eog.
EOG_chan	string	""	EOG channel name or index for automatic EOG detection (optional).
reject_ECG	boolean	false	Whether to automatically detect and exclude ECG artifact components using mne.preprocessing.ICA.find_bads_ecg.
ECG_chan	string	""	ECG channel name or index for automatic ECG detection (optional).

Usage

Running on Brainlife.io

1. Select your epoched MEG/EEG .fif dataset as the epo input and the fitted ICA decomposition (e.g. from ICA-fit) as the ica input.
2. Optionally set exclude to a comma-separated list of additional component indices to exclude.
3. Optionally enable reject_EOG/reject_ECG to automatically detect and exclude EOG/ECG artifact components, specifying EOG_chan/ECG_chan if needed.
4. Submit the process.

Local Testing

# Edit config.json to point "epo" and "ica" at real files, then:
python main.py

Technical Details

The app uses MNE-Python's ICA functionality to apply component exclusion to epoched data. It supports:

• Manual component exclusion via index specification
• Automatic EOG artifact detection using correlation analysis
• Automatic ECG artifact detection using cross-trial phase statistics
• Quality control visualization and reporting

Authors

• Saeed Zahran (https://github.com/saeedzahranutc)
• Maximilien Chaumon (https://github.com/dnacombo)

Citations

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

Hayashi, S., Caron, B.A., Heinsfeld, A.S. et al. brainlife.io: a decentralized and open-source cloud platform to support neuroscience research. Nat Methods 21, 809–813 (2024). https://doi.org/10.1038/s41592-024-02237-2

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.

License

Copyright (c) 2026 MEEG Brainlife team. Licensed under AGPL-3.0, see license.txt.