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.