mdelpozobanos / eegadjust

ADJUST method for EEG artifact rejection

Artifact Detector based on the Joint Use of Spatial and Temporal features

ADJUST is an automatic EEG artifact rejection method based on spatial and temporal features, published by A. Mognon et. al. in 2010 [Mognon2010].

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 blinks, vertical eye movements, horizontal eye movements, generic discontinuities or clean. 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:`eegadjust.eegadjust.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

[Mognon2010]

A. Mognon, Jovicich J., Bruzzone L., and Buiatti M. Adjust: An automatic eeg artifact detector based on the joint use of spatial and temporal features. Psychophysiology, pages 229-240, July 2010.

License

The eegadjust framework is open-sourced software licensed under the MIT license.