= Multivariate Temporal Response Function =
''Authors: Anna Zaidi, Raymundo Cassani''

This tutorial will host the steps to use the [[https://github.com/mickcrosse/mTRF-Toolbox|mTRF-Toolbox]] in Brainstorm. ...

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== Introduction ==
This tutorial introduces the Temporal Response Function (TRF) analysis within the Brainstorm environment, employing the mTRF Toolbox. TRF analysis is instrumental in delineating the dynamics of the brain's response to continuous stimuli, such as speech and music, providing insights into the underlying neural mechanisms.

== Install mTRF-Toolbox ==
In order to use the process files required for TRF analysis, you will need to download the [[https://www.frontiersin.org/journals/human-neuroscience/articles/10.3389/fnhum.2016.00604/full|mTRF toolbox]].

First, visit the [[https://github.com/mickcrosse/mTRF-Toolbox.git|mTRF Toolbox GitHub]] page to download the latest version of the toolbox. Click the "Code" button and extract the downloaded zip file into a directory that MATLAB can access.

Then, open MATLAB and add the toolbox to your MATLAB path using the addpath function. This ensures MATLAB recognizes the toolbox commands.

Alternatively, you can install the mtrf manually from Brainstorm directly. For that, go to Plug-ins > Statistics > mtrf. Or, the plugin will install automatically once you call the process for the first time.

== Preparing the data ==
Using the Introduction dataset, you will first need to import the entire recordings for whichever run you want to study into your database. Right-click on the raw file, and then 'Import in database'.
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Once this is done, the recordings will have appeared in a new file. Some recordings will be labeled as bad and therefore need to be handled. Right-click on the file and select 'Accept Trial'.

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== Saving the TRF weights ==
In order to start your TRF analysis, you will need to run the 'Temporal Response Function Analysis' process. First, drag the imported file in the 'Process1' box. Click 'Run', then select 'Encoding' > 'Temporal Response Function Analysis'.
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Set the analysis parameters, including the range of time lags to investigate, and the events that you want to analyze. In this tutorial, choose -100ms for minimum time lag, 200ms maximum time lag and we will be looking at the TRF related to deviant and standard beeps. Once your parameters are chosen, hit 'Run'. This will save two files in your database as matrices containing the weights between neural and response data.
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