= Generalized Eigenvalue De-Artifacting Instrument (GEDAI) =
''Authors: Tomas Ros, Yingqi Huang, Takfarinas Medani, Raymundo Cassani''


This tutorial introduces the generalized eigenvalue de-artifacting instrument (GEDAI) algorithm which is an unsupervised EEG denoising method based on the leadfield filtering.

<<TableOfContents(2,2)>>

== Introduction ==
'''[TODO]''' A description on the method

== Install ==
Being a Brainstorm plugin, GEDAI plugin can be installed, updated and removed directly from the Brainstorm GUI. For further information, see the [[Tutorials/Plugins|plugins tutorial]].

 * From the main window go to '''Plugins > Artifacts > gedai > Install'''. 

 {{attachment:install_plugin.png||width="60%"}}

 * A message will appear saying, "Plugin '''gedai''' is not installed on your computer. '''Download''' the latest version of GEDAI now?" click 'Yes'.

 {{attachment:plugin_manager.png||width="60%"}}

 * The plugin will be downloaded and installed automatically. Once installed, you will see a confirmation message.

 {{attachment:successfully_installed.png||width="60%"}}

By following these steps, you will successfully install the bst-neuromaps plugin.

== Using GEDAI ==
'''[TODO]''' Expanding these points

=== On GEDAI provided data ===
 * Create a new protocol
 * Importing data
 * Install GEDAI plugin
 * Running GEDAI process
 Description of the process GUI and each of their elements
 * Results

=== On epilepsy EEG ===
 * We can use GEDAI on the raw EEG data in the Epilepsy tutorial
 https://neuroimage.usc.edu/brainstorm/Tutorials/Epilepsy


== Additional documentation ==
==== Related tutorials ====
 * [[https://neuroimage.usc.edu/brainstorm/Tutorials/ArtifactsDetect|Artifact detection]]
 * [[https://neuroimage.usc.edu/brainstorm/Tutorials/ArtifactsSsp|Artifact cleaning with SSP]]

==== Articles ====
* Ros, T., Férat, V., Huang, Y., Colangelo, C., Kia, S. M., Wolfers, T., Vulliemoz, S., & Michela, A. (2025). <<BR>> [[https://doi.org/10.1101/2025.10.04.680449|Return of the GEDAI: Unsupervised EEG denoising based on leadfield filtering]]. <<BR>> bioRxiv : The Preprint Server for Biology.