= Tutorial 11: Time-frequency =
This tutorial introduces how to compute the time-frequency decomposition of MEG recordings and cortical currents using Morlet wavelets. There are several ways to reachthe same result, please read all the sections carefully and then chose the method that is best suited for your own data.

<<TableOfContents(2)>>

== Compute from recordings ==
 * We are going first to compute the time-frequency decomposition for the two averaged recordings we have in the protocol.
 * Drag'n'drop the two ERP files from StimRightThumb and StimLeftThumb in the "Processes" list, as explained in tutorial 9 (Processes). Select the "Recordings" button.<<BR>> {{attachment:fileSelection.gif}}
 * Click on Run. Then select the process: Spectral analysis > Time-frequency decomposition.<<BR>> {{attachment:processSelection.gif}}
 * You do not have anything else to configure here: we want to get the time-frequency decomposition for all the time points, and we do not want to save the output file in a specific condition. The only thing that you may do is add a comment to the file (text field in the top of the figure). Click on Run.<<BR>> {{attachment:timeFreqOptionsBasic.gif}}
 * This options window allows you to configure the time and the frequency resolutions, the wavelets themselves, the sensors to process, and the type of output.

== Description of the options ==
 *
== Next ==
This is the last tutorial for Brainstorm  introduction. You had an overview of most of the software features.

Now you can go back to the main [[Tutorials]] page, and read tutorials that are closer to your centers of interest.