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| = Decoding conditions = ''Authors: Seyed-Mahdi Khaligh-Razavi, Francois Tadel, Dimitrios Pantazis'' |
= Machine learning: Decoding / MVPA = ''Authors: Dimitrios Pantazis''', '''Seyed-Mahdi Khaligh-Razavi, Francois Tadel, '' |
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| 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. | This tutorial illustrates how to run MEG decoding using support vector machines (SVM). |
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| <<Include(DatasetDecoding, , from="\<\<HTML\(\<!-- START-PAGE --\>\)\>\>", to="\<\<HTML\(\<!-- STOP-SHORT --\>\)\>\>")>> | == License == To reference this dataset in your publications, please cite Cichy et al. (2014). |
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| * Decoding > Classification with cross-validation (process_decoding_crossval.m) * Decoding > Classification with permutation (process_decoding_permutation.m) |
* Decoding > SVM classifier decoding * Decoding > max-correlation classifier decoding |
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| These two processes work in very similar ways: | These two processes work in a similar way, but they use a different classifier, so only SVD is demonstrated here. |
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| * '''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). |
* '''Input''': the input is the channel data from two conditions (e.g. condA and condB) across time. Number of samples per condition do not have to be the same for both condA and condB, but each of them should have enough samples to create k-folds (see parameter below). * '''Output''': the output is a decoding time course, or a temporal generalization matrix (train time x test time). * '''Classifier''': Two methods are offered for the classification of MEG recordings across time: support vector machine (SVM) and max-correlation classifier. |
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| 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’. | In the context of this tutorial, we have two condition types: faces, and objects. We want to decode faces vs. objects using 306 MEG channels. |
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| * 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. |
* From the [[http://neuroimage.usc.edu/bst/download.php|Download]] page of this website, download the file: '''subj04NN_sess01-0_tsss.fif''' |
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| * "'''No, use one channel file per condition'''". <<BR>><<BR>> {{attachment:decoding_protocol.gif||height="370",width="344"}} | * "'''No, use one channel file per condition'''". <<BR>><<BR>> {{attachment:1_create_new_protocol.jpg||width="400"}} |
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| * 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'''. |
* Right click on the subject node (Subject01) > '''Review raw file'''''.'' <<BR>>Select the file format: "'''MEG/EEG: Neuromag FIFF (*.fif)'''"<<BR>>Select the file: '''subj04NN-sess01-0_tsss.fif''' <<BR>> * {{attachment:2_review_raw_file.jpg||width="440"}} <<BR>><<BR>> {{attachment:2_review_raw_file2.jpg||width="440"}} <<BR>><<BR>> * Select "Event channels" to read the triggers from the stimulus channel. <<BR>><<BR>> {{attachment:3_event_channel.jpg||width="320"}} * We will not pay attention to MEG/MRI registration because we are not going to compute any source models. The decoding is done on the sensor data. * Double click on the 'Link to raw file' to visualize the raw recordings. Event codes 13-24 indicate responses to face images, and we will combine them to a single group called 'faces'. To do so, select events 13-24 and from the menu select "'''Events > Duplicate groups'''". Then select "'''Events > Merge groups'''". The event codes are duplicated first so we do not lose the original 13-24 event codes. {{attachment:4_duplicate_groups_faces.jpg||width="500"}} <<BR>><<BR>> {{attachment:5_merge_groups_faces.jpg||width="500"}} * Event codes 49-60 indicate responses to object images, and we will combine them to a single group called 'objects'. To do so, select events 49-60 and from the menu select Events->Duplicate groups. Then select Events->Merge groups. No screenshots are shown since this is similar to above. * We will now import the 'faces' and 'objects' responses to the database. Select "'''File > Import in database"'''. <<BR>><<BR>> {{attachment:10_import_in_database.jpg||width="500"}} <<BR>><<BR>> * Select only two events: 'faces' and 'objects' * Epoch time: [-200, 800] ms * Remove DC offset: Time range: [-200, 0] ms * Do not create separate folders for each event type<<BR>> {{attachment:11_import_in_database_window.jpg||width="500"}} |
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| Select the Process2 tab at the bottom of the Brainstorm window. | * Drag and drop all the face and object trials to the Process1 tab at the bottom of the Brainstorm window. * Intuitively, you might have expected to use the Process2 tab to decode faces vs. objects. But the decoding process is designed to also handle pairwise decoding of multiple classes (not just two classes) for computational efficiency, so more that two categories can be entered in the Process1 tab.<<BR>> {{attachment:12_select_files.jpg||width="400"}} |
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| * 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 == |
== Decoding with cross-validation == |
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| * Select process "'''Decoding > Classification with cross-validation'''": <<BR>><<BR>> {{attachment:cv_process.gif||height="388",width="346"}} * '''Low-pass cutoff frequency''': If set, it will apply a low-pass filter to all the input recordings. * '''Matlab SVM/LDA''': Require Matlab's Statistics and Machine Learning Toolbox. <<BR>><<BR>>These methods 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 (option "Number of folds"). * '''LibSVM''': Requires the LibSVM toolbox ([[http://www.csie.ntu.edu.tw/~cjlin/libsvm/#download|download]] and add to your path).<<BR>>The LibSVM cross-validation may be faster but won’t be stratified. |
* Select process "'''Decoding > SVM decoding'''" {{attachment:13_pipeline_editor_select_decoding.jpg||width="400"}} * Select 'MEG' for sensor types * Set 30 Hz for low-pass cutoff frequency. Equivalently, one could have applied a low-pass filters to the recordings and then run the decoding process. But this is a shortcut to apply a low-pass filter just for decoding without permanently altering the input recordings. * SVM decoding requires the LibSVM toolbox ([[http://www.csie.ntu.edu.tw/~cjlin/libsvm/#download|download]] and add to your path). * Select 100 for number of permutations. Alternatively use a smaller number for faster results. * Select 5 for number of k-folds * Select 'Pairwise' for decoding. Hint: if more that two classes were input to the Process1 tab, the decoding process will perform decoding separately for each possible pair of classes. It will return them in the same form as Matlab's 'squareform' function (i.e. lower triangular elements in columnwise order) * The decoding process follows a similar procedure as Pantazis et al. (2018). Namely, to reduce computational load and improve signal-to-noise ratio, we first randomly assign all trials (from each class) into k folds, and then subaverage all trials within each fold into a single trial, thus yielding a total of k subaveraged trials per class. Decoding then follows with a leave-one-out cross-validation procedure on the subavaraged trials. * For example, if we have two classes with 100 trials each, selecting 5 number of folds will randomly assign the 100 trials in 5 folds with 20 trials each. The process than will subaverage the 20 trials yielding 5 subaveraged trials for each class. <<BR>> |
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| * The process will take some time. The results are then saved in a file in the new decoding folder. <<BR>><<BR>>{{attachment:cv_file.gif}} * If you double click on it you will see a decoding curve across time (shown below). <<BR>><<BR>> <<BR>><<BR>> |
{{attachment:14_svm_decoding_pairwise.jpg||width="380"}} |
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| {{attachment:9_crossvalres2.png}} <<BR>><<BR>> | * The process will take some time. The results are then saved in a file in the new decoding |
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| 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. | {{attachment:15_svm_decoding_pairwise_results.jpg}} |
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| ''<<BR>>'' ''Matlab code: <<BR>>'' | * |
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| * ''figure; '' * ''plot(decodingcurve.Value);'' |
* |
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| <<BR>><<BR>> {{attachment:10_crossvalres3.png}} <<BR>><<BR>> | * * * * folder. <<BR>><<BR>> {{attachment:cv_file.gif||height="167",width="268"}} * If you double click on it you will see a decoding curve across time. <<BR>><<BR>> {{attachment:cv_plot.gif||height="172",width="355"}} |
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| 1. Select run -> decoding conditions -> classification with permutation 1. Set the values as the following: |
* Select process "'''Decoding > Classification with cross-validation'''". Set options as below: <<BR>><<BR>> {{attachment:perm_process.gif||height="366",width="311"}} * '''Trial bin size''': If 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. * The results are saved in a file in the new decoding folder. <<BR>><<BR>> {{attachment:perm_file.gif||height="142",width="284"}} * Right-click > Display as time series (or double click). <<BR>><<BR>> {{attachment:perm_plot.gif||height="169",width="346"}} |
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| <<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>> |
== Acknowledgment == This work was supported by the ''McGovern Institute Neurotechnology Program'' to PIs: Aude Oliva and Dimitrios Pantazis. ''http://mcgovern.mit.edu/technology/neurotechnology-program'' |
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| 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. |
1. Khaligh-Razavi SM, Bainbridge W, Pantazis D, Oliva A (2016)<<BR>>[[http://biorxiv.org/content/early/2016/04/22/049700.abstract|From what we perceive to what we remember: Characterizing representational dynamics of visual memorability]]. ''bioRxiv'', 049700. 1. Cichy RM, Pantazis D, Oliva A (2014)<<BR>>[[http://www.nature.com/neuro/journal/v17/n3/full/nn.3635.html|Resolving human object recognition in space and time]], Nature Neuroscience, 17:455–462. == Additional documentation == * Forum: Decoding in source space: http://neuroimage.usc.edu/forums/showthread.php?2719 |
Machine learning: Decoding / MVPA
Authors: Dimitrios Pantazis, Seyed-Mahdi Khaligh-Razavi, Francois Tadel,
This tutorial illustrates how to run MEG decoding using support vector machines (SVM).
Contents
License
To reference this dataset in your publications, please cite Cichy et al. (2014).
Description of the decoding functions
Two decoding processes are available in Brainstorm:
Decoding > SVM classifier decoding
Decoding > max-correlation classifier decoding
These two processes work in a similar way, but they use a different classifier, so only SVD is demonstrated here.
Input: the input is the channel data from two conditions (e.g. condA and condB) across time. Number of samples per condition do not have to be the same for both condA and condB, but each of them should have enough samples to create k-folds (see parameter below).
Output: the output is a decoding time course, or a temporal generalization matrix (train time x test time).
Classifier: Two methods are offered for the classification of MEG recordings across time: support vector machine (SVM) and max-correlation classifier.
In the context of this tutorial, we have two condition types: faces, and objects. We want to decode faces vs. objects using 306 MEG channels.
Download and installation
From the Download page of this website, download the file: subj04NN_sess01-0_tsss.fif
- 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".
Import the recordings
- Go to the "functional data" view (sorted by subjects).
Right-click on the TutorialDecoding folder > New subject > Subject01
Leave the default options you defined for the protocol.Right click on the subject node (Subject01) > Review raw file.
Select the file format: "MEG/EEG: Neuromag FIFF (*.fif)"
Select the file: subj04NN-sess01-0_tsss.fif
Select "Event channels" to read the triggers from the stimulus channel.
- We will not pay attention to MEG/MRI registration because we are not going to compute any source models. The decoding is done on the sensor data.
Double click on the 'Link to raw file' to visualize the raw recordings. Event codes 13-24 indicate responses to face images, and we will combine them to a single group called 'faces'. To do so, select events 13-24 and from the menu select "Events > Duplicate groups". Then select "Events > Merge groups". The event codes are duplicated first so we do not lose the original 13-24 event codes.
Event codes 49-60 indicate responses to object images, and we will combine them to a single group called 'objects'. To do so, select events 49-60 and from the menu select Events->Duplicate groups. Then select Events->Merge groups. No screenshots are shown since this is similar to above.
We will now import the 'faces' and 'objects' responses to the database. Select "File > Import in database".
- Select only two events: 'faces' and 'objects'
- Epoch time: [-200, 800] ms
- Remove DC offset: Time range: [-200, 0] ms
Do not create separate folders for each event type
Select files
- Drag and drop all the face and object trials to the Process1 tab at the bottom of the Brainstorm window.
Intuitively, you might have expected to use the Process2 tab to decode faces vs. objects. But the decoding process is designed to also handle pairwise decoding of multiple classes (not just two classes) for computational efficiency, so more that two categories can be entered in the Process1 tab.
Decoding with 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.
Select process "Decoding > SVM decoding"
- Select 'MEG' for sensor types
- Set 30 Hz for low-pass cutoff frequency. Equivalently, one could have applied a low-pass filters to the recordings and then run the decoding process. But this is a shortcut to apply a low-pass filter just for decoding without permanently altering the input recordings.
SVM decoding requires the LibSVM toolbox (download and add to your path).
- Select 100 for number of permutations. Alternatively use a smaller number for faster results.
- Select 5 for number of k-folds
- Select 'Pairwise' for decoding. Hint: if more that two classes were input to the Process1 tab, the decoding process will perform decoding separately for each possible pair of classes. It will return them in the same form as Matlab's 'squareform' function (i.e. lower triangular elements in columnwise order)
- The decoding process follows a similar procedure as Pantazis et al. (2018). Namely, to reduce computational load and improve signal-to-noise ratio, we first randomly assign all trials (from each class) into k folds, and then subaverage all trials within each fold into a single trial, thus yielding a total of k subaveraged trials per class. Decoding then follows with a leave-one-out cross-validation procedure on the subavaraged trials.
For example, if we have two classes with 100 trials each, selecting 5 number of folds will randomly assign the 100 trials in 5 folds with 20 trials each. The process than will subaverage the 20 trials yielding 5 subaveraged trials for each class.
- The process will take some time. The results are then saved in a file in the new decoding
folder.
If you double click on it you will see a decoding curve across time.
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.
Select process "Decoding > Classification with cross-validation". Set options as below:
Trial bin size: If 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.
The results are saved in a file in the new decoding folder.
Right-click > Display as time series (or double click).
Acknowledgment
This work was supported by the McGovern Institute Neurotechnology Program to PIs: Aude Oliva and Dimitrios Pantazis. http://mcgovern.mit.edu/technology/neurotechnology-program
References
Khaligh-Razavi SM, Bainbridge W, Pantazis D, Oliva A (2016)
From what we perceive to what we remember: Characterizing representational dynamics of visual memorability. bioRxiv, 049700.Cichy RM, Pantazis D, Oliva A (2014)
Resolving human object recognition in space and time, Nature Neuroscience, 17:455–462.
Additional documentation
Forum: Decoding in source space: http://neuroimage.usc.edu/forums/showthread.php?2719