Differences between revisions 5 and 25 (spanning 20 versions)

Full analysis with one script

This tutorial explains how to use the Brainstorm scripting interface to run a full analysis, from the raw recordings to the source reconstruction. It is based on a median nerve stimulation experiment recorded at the Montreal Neurological Institute in 2011 with a CTF MEG 275 system. The sample dataset contains 6 minutes of recordings at 1200Hz for one subject and includes 100 stimulations of each arm.

The tutorial follows the analysis steps detailed in the three advanced tutorials in the category Processing continuous recordings. You should read them before reading this tutorial, to have the explanations that go with the analysis steps.

Contents

Creating the analysis pipeline
Save the pipeline
1. Save in current workspace
2. Export as script
Report viewer

Creating the analysis pipeline

Select the menu File > Create new protocol. Name it "TutorialScript" and select the options:

"No, use individual anatomy",
"Yes, use one channel file per subject".

To start building your analysis pipeline, just click on the "Run" button in the Process1 tab. We don't need any file in input, as we are going to select the files to import in the script itself. Then add all the processes listed below. The output of each process is the input of the following one, this is why the order of the processes is important.

Import anatomy > Import FreeSurfer folder

Subject name: Subject01
Folder to import: Select the folder sample_raw/Anatomy
Number of vertices (cortex): 15000
Fiducials: Copy what is indicated below. This is a reason it is usually easier to do this step in interactive mode, and then run only the script starting from the next step.
Input: None; Output: None

Import recordings > Create link to raw file

Input: None; Output: Raw file

Pre-process > Sinusoid removal (notch)

Input: Raw file ; Output: Raw file (new)

Artifacts > Detect eye blinks

Input: Raw file ; Output: Raw file

Artifacts > Compute SSP: eye blinks

Input: Raw file ; Output: Raw file

Import recordings > Import MEG/EEG : Events

Input: Raw file ; Output: 199 epochs in 2 conditions

Pre-process > Remove DC offset

Input: 199 epochs ; Output: 199 epochs

Pre-process > Add time offset

Input: 199 epochs ; Ouput: 199 epochs

Sources > Compute noise covariance

Since the epochs are currently selected and pre-processed: we can use them to estimate the noise covariance matrix before we move on with the calculation of the average.

Input: 199 epochs ; Output: 199 epochs

Average > Average files

Input: 199 epochs ; Output: 2 averages

File > Save snapshot: Sensors/MRI registration

Input: 2 averages ; Output: 2 averages

File > Save snapshot: Recordings time series

Input: 2 averages ; Output: 2 averages

Sources > Compute head model

Input: 2 averages ; Output: 2 averages

Sources > Compute sources

Input: 2 averages ; Output: all the source files (1 raw + 2 average + 199 epochs = 202 files)

Save the pipeline

Save in current workspace

Use the menus on top of the pipeline editor to save this list of processes on your computer. The menu "Save > New..." will create an entry readily available in your Brainstorm installation in the Load section of the same menu.

Export as script

Use the menu "Generate .m script" to create a Matlab script that would have the exact same result as running this analysis pipeline from the Brainstorm interface.

% Script generated by Brainstorm v3.1 (25-Jan-2013)

% Input files
FileNamesA = [];

% Start a new report
bst_report('Start', FileNamesA);

% Process: Import FreeSurfer folder
sFiles = bst_process(...
    'CallProcess', 'process_import_freesurfer', ...
    FileNamesA, [], ...
    'subjectname', 'Subject01', ...
    'mrifile', {'C:\Work\RawData\Tutorials\sample_raw\Anatomy', 'FsDir'}, ...
    'nvertices', 15000, ...
    'nas', [126, 215, 138], ...
    'lpa', [58, 136, 121], ...
    'rpa', [197, 138, 118], ...
    'ac', [128, 137, 157], ...
    'pc', [128, 113, 157], ...
    'ih', [128, 125, 216]);

% Process: Create link to raw file
sFiles = bst_process(...
    'CallProcess', 'process_import_data_raw', ...
    sFiles, [], ...
    'subjectname', 'Subject01', ...
    'datafile', {'C:\Work\RawData\Tutorials\sample_raw\Data\EF1982_somatosensory_20111109_01_AUX-f.ds', 'CTF'}, ...
    'channelalign', 1);

% Process: Sinusoid removal: 60Hz 120Hz 180Hz
sFiles = bst_process(...
    'CallProcess', 'process_sin_remove', ...
    sFiles, [], ...
    'freqlist', [60, 120, 180], ...
    'sensortypes', 'MEG, EEG', ...
    'reverse', 1);

% Process: Detect eye blinks
sFiles = bst_process(...
    'CallProcess', 'process_evt_detect_eog', ...
    sFiles, [], ...
    'channelname', 'EEG058', ...
    'timewindow', [0, 359.9991667], ...
    'eventname', 'blink');

% Process: SSP EOG: blink
sFiles = bst_process(...
    'CallProcess', 'process_ssp_eog', ...
    sFiles, [], ...
    'eventname', 'blink', ...
    'sensortypes', 'MEG, MEG MAG, MEG GRAD');

% Process: Import MEG/EEG: Events
sFiles = bst_process(...
    'CallProcess', 'process_import_data_event', ...
    sFiles, [], ...
    'subjectname', 'Subject01', ...
    'condition', '', ...
    'eventname', 'left, right', ...
    'timewindow', [0, 359.9991667], ...
    'epochtime', [-0.1, 0.3], ...
    'createcond', 1, ...
    'ignoreshort', 1, ...
    'usectfcomp', 1, ...
    'usessp', 1, ...
    'freq', [], ...
    'baseline', []);

% Process: Remove baseline: [-100ms,-1ms]
sFiles = bst_process(...
    'CallProcess', 'process_baseline', ...
    sFiles, [], ...
    'baseline', [-0.1, -0.0008333333333], ...
    'sensortypes', 'MEG, EEG', ...
    'overwrite', 1);

% Process: Add time offset: -4.20ms
sFiles = bst_process(...
    'CallProcess', 'process_timeoffset', ...
    sFiles, [], ...
    'offset', -0.0042, ...
    'overwrite', 1);

% Process: Compute noise covariance
sFiles = bst_process(...
    'CallProcess', 'process_noisecov', ...
    sFiles, [], ...
    'baseline', [-0.1042, -0.005], ...
    'dcoffset', 1, ...
    'method', 1, ...  % Full noise covariance matrix
    'copycond', 0, ...
    'copysubj', 0);

% Process: Average: By condition (subject average)
sFiles = bst_process(...
    'CallProcess', 'process_average', ...
    sFiles, [], ...
    'avgtype', 3);  % By condition (subject average)

% Process: Snapshot: Sensors/MRI registration
sFiles = bst_process(...
    'CallProcess', 'process_snapshot', ...
    sFiles, [], ...
    'target', 1, ...  % Sensors/MRI registration
    'modality', 1, ...  % MEG (All)
    'orient', 1, ...  % left
    'time', 0, ...
    'contact_time', [0, 0.1], ...
    'contact_nimage', 12, ...
    'comment', 'MEG/MRI Registration');

% Process: Snapshot: Recordings time series
sFiles = bst_process(...
    'CallProcess', 'process_snapshot', ...
    sFiles, [], ...
    'target', 5, ...  % Recordings time series
    'modality', 1, ...  % MEG (All)
    'orient', 1, ...  % left
    'time', 0, ...
    'contact_time', [0, 0.1], ...
    'contact_nimage', 12, ...
    'comment', 'MEG/MRI Registration');

% Process: Compute head model
sFiles = bst_process(...
    'CallProcess', 'process_headmodel', ...
    sFiles, [], ...
    'sourcespace', 1, ...
    'meg', 3, ...  % Overlapping spheres
    'eeg', 3, ...
    'openmeeg', struct(...
         'BemSelect', [0, 0, 1], ...
         'BemCond', [1, 0.0125, 1], ...
         'BemNames', {{'Scalp', 'Skull', 'Brain'}}, ...
         'BemFiles', {{}}, ...
         'isAdjoint', 0, ...
         'isAdaptative', 1, ...
         'isSplit', 0, ...
         'SplitLength', 4000));

% Process: Compute sources
sFiles = bst_process(...
    'CallProcess', 'process_inverse', ...
    sFiles, [], ...
    'method', 1, ...  % Minimum norm estimates (wMNE)
    'wmne', struct(...
         'NoiseCov', [], ...
         'InverseMethod', 'wmne', ...
         'SNR', 3, ...
         'diagnoise', 0, ...
         'SourceOrient', {{'fixed'}}, ...
         'loose', 0.2, ...
         'depth', 1, ...
         'weightexp', 0.5, ...
         'weightlimit', 10, ...
         'regnoise', 1, ...
         'magreg', 0.1, ...
         'gradreg', 0.1, ...
         'eegreg', 0.1, ...
         'fMRI', [], ...
         'fMRIthresh', [], ...
         'fMRIoff', 0.1, ...
         'pca', 1), ...
    'sensortypes', 'MEG, MEG MAG, MEG GRAD, EEG', ...
    'output', 1);  % Kernel only: shared

% Save and display report
ReportFile = bst_report('Save', sFiles);
bst_report('Open', ReportFile);

Report viewer

Click on Run to start the script.

As this process is taking screen captures, do not use your computer for something else at the same time: if another window covers the Brainstorm figure, it will not capture the right images.

At the end, the report viewer is opened to show the status of all the processes, the information messages, the list of input and output files, and the screen captures. The report is saved in your home folder ($home/.brainstorm/reports). If you close this window, you can get it back with the menu File > Report viewer.

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-To start building your analysis pipeline, just click on the "'''Run'''" button in the Process1 tab. Then add all the processes listed below.

=== Import > Import MRI ===
+To start building your analysis pipeline, just click on the "'''Run'''" button in the Process1 tab. We don't need any file in input, as we are going to select the files to import in the script itself. Then add all the processes listed below. The output of each process is the input of the following one, this is why the order of the processes is important.

=== Import anatomy > Import FreeSurfer folder ===
 * Subject name: Subject01
 * Folder to import: Select the folder sample_raw/Anatomy
 * Number of vertices (cortex): 15000
 * Fiducials: Copy what is indicated below. This is a reason it is usually easier to do this step in interactive mode, and then run only the script starting from the next step.
 * Input: None;  Output: None

{{attachment:procImportFs.gif}}

=== Import recordings > Create link to raw file ===
Input: None;  Output: Raw file

{{attachment:procLinkRaw.gif}}

=== Pre-process > Sinusoid removal (notch) ===
Input: Raw file ;  Output: Raw file  (new)

{{attachment:procSin.gif}}

=== Artifacts > Detect eye blinks ===
Input: Raw file ;  Output: Raw file

{{attachment:procDetectEog.gif}}

=== Artifacts > Compute SSP: eye blinks ===
Input: Raw file ;  Output: Raw file

{{attachment:procSspEog.gif}}

=== Import recordings > Import MEG/EEG : Events ===
Input: Raw file ;  Output: 199 epochs in 2 conditions

{{attachment:procImportEvt.gif}}

=== Pre-process > Remove DC offset ===
Input: 199 epochs ;  Output: 199 epochs

{{attachment:procBaseline.gif}}

=== Pre-process > Add time offset ===
Input: 199 epochs ; Ouput: 199 epochs

{{attachment:procBaseline.gif}}

=== Sources > Compute noise covariance ===
Since the epochs are currently selected and pre-processed: we can use them to estimate the noise covariance matrix before we move on with the calculation of the average.

Input: 199 epochs ;  Output: 199 epochs

{{attachment:procNoiseCov.gif}}

=== Average > Average files ===
Input: 199 epochs ;  Output: 2 averages

{{attachment:procAverage.gif}}

=== File > Save snapshot: Sensors/MRI registration ===
Input: 2 averages ;  Output: 2 averages

{{attachment:procSnapReg.gif}}

=== File > Save snapshot: Recordings time series ===
Input: 2 averages ;  Output: 2 averages

{{attachment:procSnapTs.gif}}

=== Sources > Compute head model ===
Input: 2 averages ;  Output: 2 averages

{{attachment:procHeadmodel.gif}}

=== Sources > Compute sources ===
Input: 2 averages ;  Output: all the source files (1 raw + 2 average + 199 epochs = 202 files)

{{attachment:procSources.gif}}

== Save the pipeline ==
=== Save in current workspace ===
Use the menus on top of the pipeline editor to save this list of processes on your computer. The menu "Save > New..." will create an entry readily available in your Brainstorm installation in the Load section of the same menu.

{{attachment:savePipeline.gif}}

=== Export as script ===
Use the menu "Generate .m script" to create a Matlab script that would have the exact same result as running this analysis pipeline from the Brainstorm interface.

{{{
% Script generated by Brainstorm v3.1 (25-Jan-2013)

% Input files
FileNamesA = [];

% Start a new report
bst_report('Start', FileNamesA);

% Process: Import FreeSurfer folder
sFiles = bst_process(...
    'CallProcess', 'process_import_freesurfer', ...
    FileNamesA, [], ...
    'subjectname', 'Subject01', ...
    'mrifile', {'C:\Work\RawData\Tutorials\sample_raw\Anatomy', 'FsDir'}, ...
    'nvertices', 15000, ...
    'nas', [126, 215, 138], ...
    'lpa', [58, 136, 121], ...
    'rpa', [197, 138, 118], ...
    'ac', [128, 137, 157], ...
    'pc', [128, 113, 157], ...
    'ih', [128, 125, 216]);

% Process: Create link to raw file
sFiles = bst_process(...
    'CallProcess', 'process_import_data_raw', ...
    sFiles, [], ...
    'subjectname', 'Subject01', ...
    'datafile', {'C:\Work\RawData\Tutorials\sample_raw\Data\EF1982_somatosensory_20111109_01_AUX-f.ds', 'CTF'}, ...
    'channelalign', 1);

% Process: Sinusoid removal: 60Hz 120Hz 180Hz
sFiles = bst_process(...
    'CallProcess', 'process_sin_remove', ...
    sFiles, [], ...
    'freqlist', [60, 120, 180], ...
    'sensortypes', 'MEG, EEG', ...
    'reverse', 1);

% Process: Detect eye blinks
sFiles = bst_process(...
    'CallProcess', 'process_evt_detect_eog', ...
    sFiles, [], ...
    'channelname', 'EEG058', ...
    'timewindow', [0, 359.9991667], ...
    'eventname', 'blink');

% Process: SSP EOG: blink
sFiles = bst_process(...
    'CallProcess', 'process_ssp_eog', ...
    sFiles, [], ...
    'eventname', 'blink', ...
    'sensortypes', 'MEG, MEG MAG, MEG GRAD');

% Process: Import MEG/EEG: Events
sFiles = bst_process(...
    'CallProcess', 'process_import_data_event', ...
    sFiles, [], ...
    'subjectname', 'Subject01', ...
    'condition', '', ...
    'eventname', 'left, right', ...
    'timewindow', [0, 359.9991667], ...
    'epochtime', [-0.1, 0.3], ...
    'createcond', 1, ...
    'ignoreshort', 1, ...
    'usectfcomp', 1, ...
    'usessp', 1, ...
    'freq', [], ...
    'baseline', []);

% Process: Remove baseline: [-100ms,-1ms]
sFiles = bst_process(...
    'CallProcess', 'process_baseline', ...
    sFiles, [], ...
    'baseline', [-0.1, -0.0008333333333], ...
    'sensortypes', 'MEG, EEG', ...
    'overwrite', 1);

% Process: Add time offset: -4.20ms
sFiles = bst_process(...
    'CallProcess', 'process_timeoffset', ...
    sFiles, [], ...
    'offset', -0.0042, ...
    'overwrite', 1);

% Process: Compute noise covariance
sFiles = bst_process(...
    'CallProcess', 'process_noisecov', ...
    sFiles, [], ...
    'baseline', [-0.1042, -0.005], ...
    'dcoffset', 1, ...
    'method', 1, ...  % Full noise covariance matrix
    'copycond', 0, ...
    'copysubj', 0);

% Process: Average: By condition (subject average)
sFiles = bst_process(...
    'CallProcess', 'process_average', ...
    sFiles, [], ...
    'avgtype', 3);  % By condition (subject average)

% Process: Snapshot: Sensors/MRI registration
sFiles = bst_process(...
    'CallProcess', 'process_snapshot', ...
    sFiles, [], ...
    'target', 1, ...  % Sensors/MRI registration
    'modality', 1, ...  % MEG (All)
    'orient', 1, ...  % left
    'time', 0, ...
    'contact_time', [0, 0.1], ...
    'contact_nimage', 12, ...
    'comment', 'MEG/MRI Registration');

% Process: Snapshot: Recordings time series
sFiles = bst_process(...
    'CallProcess', 'process_snapshot', ...
    sFiles, [], ...
    'target', 5, ...  % Recordings time series
    'modality', 1, ...  % MEG (All)
    'orient', 1, ...  % left
    'time', 0, ...
    'contact_time', [0, 0.1], ...
    'contact_nimage', 12, ...
    'comment', 'MEG/MRI Registration');

% Process: Compute head model
sFiles = bst_process(...
    'CallProcess', 'process_headmodel', ...
    sFiles, [], ...
    'sourcespace', 1, ...
    'meg', 3, ...  % Overlapping spheres
    'eeg', 3, ...
    'openmeeg', struct(...
         'BemSelect', [0, 0, 1], ...
         'BemCond', [1, 0.0125, 1], ...
         'BemNames', {{'Scalp', 'Skull', 'Brain'}}, ...
         'BemFiles', {{}}, ...
         'isAdjoint', 0, ...
         'isAdaptative', 1, ...
         'isSplit', 0, ...
         'SplitLength', 4000));

% Process: Compute sources
sFiles = bst_process(...
    'CallProcess', 'process_inverse', ...
    sFiles, [], ...
    'method', 1, ...  % Minimum norm estimates (wMNE)
    'wmne', struct(...
         'NoiseCov', [], ...
         'InverseMethod', 'wmne', ...
         'SNR', 3, ...
         'diagnoise', 0, ...
         'SourceOrient', {{'fixed'}}, ...
         'loose', 0.2, ...
         'depth', 1, ...
         'weightexp', 0.5, ...
         'weightlimit', 10, ...
         'regnoise', 1, ...
         'magreg', 0.1, ...
         'gradreg', 0.1, ...
         'eegreg', 0.1, ...
         'fMRI', [], ...
         'fMRIthresh', [], ...
         'fMRIoff', 0.1, ...
         'pca', 1), ...
    'sensortypes', 'MEG, MEG MAG, MEG GRAD, EEG', ...
    'output', 1);  % Kernel only: shared

% Save and display report
ReportFile = bst_report('Save', sFiles);
bst_report('Open', ReportFile);
}}}
== Report viewer ==
Click on Run to start the script.

As this process is taking screen captures, do not use your computer for something else at the same time: if another window covers the Brainstorm figure, it will not capture the right images.

At the end, the report viewer is opened to show the status of all the processes, the information messages, the list of input and output files, and the screen captures. The report is saved in your home folder ($home/.brainstorm/reports). If you close this window, you can get it back with the menu File > Report viewer.

{{attachment:report1.gif}}

{{attachment:report2.gif}}

Software

Users

Development

Full analysis with one script

Creating the analysis pipeline

Import anatomy > Import FreeSurfer folder

Import recordings > Create link to raw file

Pre-process > Sinusoid removal (notch)

Artifacts > Detect eye blinks

Artifacts > Compute SSP: eye blinks

Import recordings > Import MEG/EEG : Events

Pre-process > Remove DC offset

Pre-process > Add time offset

Sources > Compute noise covariance

Average > Average files

File > Save snapshot: Sensors/MRI registration

File > Save snapshot: Recordings time series

Sources > Compute head model

Sources > Compute sources

Save the pipeline

Save in current workspace

Export as script

Report viewer