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| = 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)>> == Introduction to Morlet wavelets == Dimitrios: Write your text here. == 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>><<BR>> {{attachment:fileSelection.gif}} * Click on Run. Then select the process: Spectral analysis > Time-frequency decomposition.<<BR>><<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>><<BR>> {{attachment:timeFreqOptionsBasic.gif}} * This option 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 == * '''Comment''': This is the string that will be displayed in the database explorer to represent the output file. * '''Time definition''': * This panel describes the time points for which you will get a value at the end of the computation. * '''Same as input file''': If you select this option, you would get the time-frequency decomposition (TF) for each of the time samples in the input file (here: 375 samples between -50ms to 250ms) * '''Group in time bands''': This option adds another step of computation: * 1) It first computes the TF for each of the time points in the initial file, excactly like with the "same as input file" option * 2) For each time band: it groups the corresponding time points by averaging the power of theire TF values. * Definition of the time bands: two mtehods * Enter your own time bands in the text area, one line per time band, with following forma: "name : start_time, stop_time" * Click on the "Generate" button to create automatically a list of time bands with the same length. You will be asked the maximal length of each time band. * '''Frequency definition''': * == 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. |
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