MARA plug-in for EEGLAB

MARA ("Multiple Artifact Rejection Algorithm") is an open-source EEGLAB plug-in which automatizes the process of hand-labeling independent components for artifact rejection. The core of MARA is a supervised machine learning algorithm that learns from expert ratings of 1290 components by extracting six features from the spatial, the spectral and the temporal domain. Features were optimized to solve the binary classification problem "reject vs. accept". Thus, MARA is not limited to a specific type of artifact, and should be able to handle eye artifacts, muscular artifacts and loose electrodes equally well.

Requirements

In addition to the requirements of EEGLAB, MARA needs the Matlab Statistics Toolbox, the Optimization Toolbox and the Signal Processing Toolbox.

Download MARA

Reference

MARA is based on the following scientific publications:

Irene Winkler, Stefan Haufe and Michael Tangermann. Automatic Classification of Artifactual ICA-Components for Artifact Removal in EEG Signals. Behavioral and Brain Functions, 7:30, 2011. [url]

Irene Winkler, Stephanie Brandl, Franziska Horn, Eric Waldburger, Carsten Allefeld and Michael Tangermann. Robust artifactual independent component classification for BCI practitioners. Journal of Neural Engineering, 11 035013, 2014. [url]

Contact

If you have questions, suggestions or bug reports, please contact Irene Winkler, irene.winkler at tu-berlin.de