= Decoding conditions =
''Authors: Seyed-Mahdi Khaligh-Razavi, Francois Tadel, Dimitrios Pantazis''

This tutorial illustrates how to use the functions developed at Aude Oliva's lab (MIT) to run support vector machine (SVM) and linear discriminant analysis (LDA) classification on your MEG data across time.

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== Description of the decoding functions ==
Two decoding processes are available in Brainstorm:

 * Decoding > Classification with cross-validation (process_decoding_crossval.m)
 * Decoding > Classification with permutation (process_decoding_permutation.m)

These two processes work in very similar ways:

 * '''Input''': the input is the channel data  from two conditions (e.g. condA and condB) across time. Number of  samples per condition should be the same for both condA and condB. Each  of them should at least contain two samples.
 * '''Output''': the output is a decoding curve across time, showing your decoding accuracy (decoding condA vs. condB) at timepoint 't'.
 * Two methods are offered for the classification of MEG recordings across time: Support vector machine (SVM) and Linear discriminant analysis (LDA).

In  the context of this tutorial, we have two condition types: faces, and  scenes. We want to decode faces vs. scenes using 306 MEG channels. In  the data, the faces are named as condition ‘201’; and the scenes are  named as condition ‘203’.

== Download and installation ==
 * Go to the [[http://neuroimage.usc.edu/brainstorm3_register/download.php|Download]] page of this website, and download the file: '''sample_decoding.zip '''
 * Unzip      it in a folder that is not in any of the Brainstorm folders  (program     folder or database folder). This is really important that  you always     keep your original data files in a separate folder: the  program  folder    can be deleted when updating the software, and the  contents of  the    database folder is supposed to be manipulated only  by the  program    itself.

 * Start Brainstorm (Matlab scripts or stand-alone version).
 * Select the menu File > Create new protocol. Name it "'''TutorialDecoding'''" and select the options:
  * "'''Yes, use protocol's default anatomy'''",
  * "'''No, use one channel file per condition'''". <<BR>><<BR>> {{attachment:decoding_protocol.gif||height="370",width="344"}}

== Import the recordings ==
 * Go to the "functional data" view (sorted by subjects).
 * Right-click on the TutorialDecoding folder > New subject > '''Subject01''' <<BR>>Leave the default options you defined for the protocol.
 * Right click on the subject node (Subject01) > '''Review raw file'''''.'' <<BR>>Select the file format: "'''MEG/EEG: Neuromag FIFF (*.fif)'''"<<BR>>Select the file: sample_decoding/'''mem6-0_tsss_mc.fif''' <<BR>><<BR>> {{attachment:decoding_link.gif||height="169",width="547"}}
 * Select "Event channels" to read the triggers from the stimulus channel. <<BR>><<BR>> {{attachment:3_fif.png||height="151",width="351"}}
 * We will not pay much attention to MEG/MRI registration because we are not going to compute any source models, the decoding is done on the sensor data.
 * Right-click on the "Link to raw file" > '''Import in database'''. <<BR>><<BR>> {{attachment:decoding_import.gif||height="317",width="571"}}
  * Select only two events: 201 (faces) and 203(scenes)
  * Epoch time: [-100, 1000] ms
  * Remove DC offset: Time range: [-100, 0] ms
  * Do not create separate folders for each event type
 * You will get a message saying "some epochs are shorter than the others". Answer '''yes'''.

== Select files ==
Select the Process2 tab at the bottom of the Brainstorm window.

 * Drag and drop '''40 files''' from group '''201''' to the left (Files A).
 * Drag and drop '''40 files''' from group '''203''' to the right (Files B).
 * You can select more than 40 or less. The important thing is that both ‘A’ and ‘B’ should have the same number of files. <<BR>><<BR>> {{attachment:decoding_selectfiles.gif||height="369",width="398"}}

== Cross-validation ==
'''Cross'''-'''validation''' is a model '''validation''' technique for assessing how the results of our decoding analysis will generalize to an independent data set.

 1. Select run -> decoding conditions -> classification with cross validation <<BR>><<BR>> {{attachment:6_decodingcrossval.png}} <<BR>><<BR>> Here, you will have three choices for cross-validation. If you have Matlab Statistics and Machine Learning Toolbox, you can use ‘Matlab SVM’ or ‘Matlab LDA’. You can also install the ‘LibSVM’ toolbox (https://www.csie.ntu.edu.tw/~cjlin/libsvm/ ); and addpath it to your Matlab session. LibSVM may be faster. The LibSVM cross-validation won’t be stratified. However, if you select Matlab SVM/LDA, it will do a k-fold stratified cross-validation for you, meaning that each fold will contain the same proportions of the two types of class labels.

You can also set the number of folds for cross-validation; and the cut-off frequency for low-pass filtering – this is to smooth your data.

 . 2- To continue, set the values as shown below: <<BR>><<BR>> {{attachment:7_crossvaloptions.png}} <<BR>><<BR>> 3- The process will take some time.The results are then saved in a file (‘Matlab SVM Decoding_201_203’) under the new ‘decoding’ node. If you double click on it you will see a decoding curve across time (shown below). <<BR>><<BR>> {{attachment:8_crossvalres1.png}} <<BR>><<BR>>

{{attachment:9_crossvalres2.png}} <<BR>><<BR>>

The internal brainstorm plot is not perfect for this purpose. To make a proper plot you can plot the results yourself. Right click on the result file (Matlab SVM Decoding_201_203), select 'export to Matlab' from the menu. Give it a name ‘decodingcurve’. Then using Matlab plot function you can plot the decoding accuracies across time.

''<<BR>>'' ''Matlab code:  <<BR>>''

 * ''figure; ''
 * ''plot(decodingcurve.Value);''

<<BR>><<BR>> {{attachment:10_crossvalres3.png}} <<BR>><<BR>>

== Permutation ==
This is an iterative procedure. The training and test data for the SVM/LDA classifier are selected in each iteration by randomly permuting the samples and grouping them into bins of size n (you can select the trial bin sizes). In each iteration two samples (one from each condition) are left out for test. The rest of the data are used to train the classifier with.

 1. Select run -> decoding conditions -> classification with permutation
 1. Set the values as the following:

<<BR>><<BR>> {{attachment:11_permoptions.png}} <<BR>><<BR>>

If the ‘Trial bin size’ is greater than 1, the training data will be randomly grouped into bins of the size you determine here. The samples within each bin are then averaged (we refer to this as sub-averaging); the classifier is then trained using the averaged samples. For example, if you have 40 faces and 40 scenes, and you set the trial bin size to 5; then for each condition you will have 8 bins each containing 5 samples. Seven bins from each condition will be used for training, and the two left-out bins (one face bin, one scene bin) will be used for testing the classifier performance.

 . 3- The results are saved under the ‘decoding ’ node . If you selected ‘Matlab SVM’, the file name will be ‘Matlab SVM Decoding-permutation_201_203’ . Double click on it. You will get the plot blow:

<<BR>><<BR>> {{attachment:12_permres1.png}} <<BR>><<BR>>

This might not be very intuitive. You can export the decoding results into Matlab and plot it yourself. If you export the decoding results into Matlab, the imported structure will have two important fields:

 * a) Value: this is the mean decoding accuracy across all permutations
 * b) Std: this is the standard deviation across all permutations.

If you plot the mean value (decodingcurve.Value), below is what you will get. You also have access to the standard deviation (decodingcurve.Std), in case you want to plot it.

<<BR>><<BR>> {{attachment:13_permres2.png}} <<BR>><<BR>>

== References ==
 1. Khaligh-Razavi, S-M; Bainbridge, W; Pantazis, D; and  Oliva, A. (2015) Introducing Brain Memorability: an MEG study (in preparation).
 1. Cichy, R.M., Pantazis, D., and Oliva, A. (2014). Resolving human object recognition in space and time. Nat Neurosci 17, 455–462.

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