Fully Automatic Statistical Thresholding for EEG artifact Rejection
FASTER is an automatic EEG artifact rejection method based on statistical thresholding, published by H. Nolan et. al. in 2010 [Nolan2010].
The method relies on the dissociation of neural and artifactual activity through a Blind Source Separation (BSS) algorithm, and the classification of each extracted component into clean or artifactual. Components identified as noisy are then removed from the reconstruction of the EEG.
Code Example
Note
In the current version, only the block in charge of identifying artifactual components is available. See :py:func:`eegfaster.eegfaster.art_comp`
Installation
To install this package, you can use the make file. From the root directory of the package, run:
make install
Note
The installation of the dependencies NumPy and SciPy may fail. It is recommended to install these packages manually.
Tests
To test the package against your installed python version, from the root directory of the package you can run:
make test
Issues and comments
Please, file an issue if you encounter any problem with the package or if you have any suggestions.
Contributors
Let people know how they can dive into the project, include important links to things like issue trackers, irc, twitter accounts if applicable.
References
[Nolan2010] | H. Nolan, R. Whelan, and R.B. Reilly. Faster: Fully automated statistical thresholding for eeg artifact rejection. Journal of Neuroscience Methods, 192(1):152-162, 2010. |
License
The eegfaster framework is open-sourced software licensed under the MIT license.