ARMBR 2.0.12

Python implementation of the ARMBR blink removal method

This is the Python implementation of Artifact-reference multivariate backward regression (ARMBR): a novel method for EEG blink artifact removal with minimal data requirements (for full algorithm, see citation below).

ARMBR is a lightweight and easy-to-use method for blink artifact removal from EEG signals using multivariate backward regression. The algorithm detects the times at which eye blinks occur and then estimates their linear scalp projection by regressing a simplified, time-locked reference signal against the multichannel EEG. This projection is used to suppress blink-related components while preserving underlying brain signals. ARMBR requires minimal training data, does not depend on dedicated EOG channels, and operates robustly in both offline and real-time (online) settings, including BCI applications.

This release contains some improvements to the BCI2000 GUI. It allows the user to create batch files to fit and apply ARMBR in BCI2000.

The code is maintained at: https://github.com/S-Shah-Lab/ARMBR

The semi-synthetic data used in the ARMBR paper, along with example codes that allow testing ARMBR of the semi-synthetic data used in the paper, are available on OSF at: https://osf.io/th2g6/

If you use ARMBR in your work, please cite:

Citation:

Alkhoury L, Scanavini G, Louviot S, Radanovic A, Shah SA & Hill NJ (2025). Artifact-Reference Multivariate Backward Regression (ARMBR): A Novel Method for EEG Blink Artifact Removal with Minimal Data Requirements. Journal of Neural Engineering, 22(3). DOI: 10.1088/1741-2552/ade566 PubMed: 40527334

BibTeX:

@article{alkhoury2025armbr,
	author  = {Alkhoury, Ludvik and Scanavini, Giacomo and Louviot, Samuel and Radanovic, Ana and Shah, Sudhin A and Hill, N Jeremy},
	title   = {Artifact-Reference Multivariate Backward Regression ({ARMBR}): A Novel Method for {EEG} Blink Artifact Removal with Minimal Data Requirements},
	journal = {Journal of Neural Engineering},
	volume  = {22},
	number  = {3},
	pages   = {036048},
	year    = {2025},
	date    = {2025-06-25},
	doi     = {10.1088/1741-2552/ade566},
	url     = {https://doi.org/10.1088/1741-2552/ade566},
}