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Tutorial 28: Scripting

[TUTORIAL UNDER DEVELOPMENT: NOT READY FOR PUBLIC USE]

Authors: Francois Tadel, Elizabeth Bock, Sylvain Baillet

The previous tutorials explained how to use Brainstorm in an interactive way to process one subject with two acquisition runs. In the context of a typical neuroimaging study, you may have tens or hundreds of subjects to process in the same way, it is unrealistic to do everything manually. Some parts of the analysis can be processed in batches with no direct supervision, others require more attention. This tutorial introduces tools and tricks that will help you assemble an efficient analysis pipeline.

Requirements: You need a license for the Matlab environment in order to use these tools and execute custom scripts. If you are running the compiled version of Brainstorm with the MCR library, the only custom code you can run is through the menu File > Matlab console and the process "Run Matlab command".

Contents

Starting a new script
Line by line: Header
Line by line: Body
Line by line: Footer
Simplify the calls
Interaction with Matlab
Naming conventions
Running the script
Starting Brainstorm
Selecting files
Database requests
File manipulation
Loop over subject and runs
How to process many subjects
Final script
Report viewer

Starting a new script

The easiest way to get started with a new Brainstorm script is to use the script generator, already introduced in the tutorial Select files and run processes. Select some files in the Process1 or Process2 tabs, select a list of processes, and use the menu Generate .m script. The example below should work on the the protocol "TutorialIntroduction" created during the introduction tutorials.

In the Process1 tab, leave the selection box empty and click on [Run]. Instead of selecting the files from the Brainstorm interface, we will select them directly from the database using a script.
Select process File > Select files: Recordings (do not execute immediately)
Subject=Subject01, Condition=[Empty], File comment=Avg: deviant (the space is important).
This process selects all the recordings with a comment including the string "Avg: deviant", from all the folders in Subject01 (except "Intra-subject" and "Common files"). We expect to get two files: the averages of the deviant condition for both runs.
Add process Pre-process > Band-pass filter: Lower cutoff=0Hz, Upper cutoff=30Hz, Mirror.
Add process File > Save snapshot: Recordings time series, Sensors=MEG.
This will apply a low-pass filter at 30Hz and save a screen capture of the signals in the report.
Do not run the pipeline, select the menu Generate .m script instead. It saves a new .m file and opens it in the Matlab editor. Close the pipeline editor window and look at the script.
The script you just generated can be the starting point to your own custom script. The following sections explain line by line how they work and how to edit them.

Line by line: Header

% Script generated by Brainstorm (19-Jul-2016)

All the lines starting with a "%" are comments, they are never executed.

% Input files
sFiles = [];
SubjectNames = {...
    'Subject01'};

These lines define the script inputs:

sFiles: The list of files in input. Currently empty because we did not select anything in input in the Process1 list. If we had selected files, it would contain a cell array of strings with relative file paths.
SubjectNames: List of subject names that are used in the script. Most of the times, the generated script would contain only one entry, but it written as a cell array so that it is easier to extend it to multiple subjects with a loop (described further in this tutorial).

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

Start a new report of activity: Clears all the previous logs and gets ready to record new messages. The report will collect all the messages that are generated during the execution of the script by the various processes. You can explicitely add screen captures and additional messages to the current report with the function bst_report. This report will remain open until the function bst_report('Start') is called again. To display the current report, use the menu File > Report viewer.

The syntax function_name('SubFunction', arguments) is used a lot in Brainstorm, to call a subfunction available inside a .m file. This line above calls the function Report() in the file brainstorm3/toolbox/process/bst_report.m. This is made possible with the use of the short script "macro_methodcall" you will see at the top of many files. Many of the Brainstorm .m files are actually libraries of functions, rather than simple "scripts" or "functions".

Line by line: Body

% Process: Select data files in: Subject01/*/Avg: deviant
sFiles = bst_process('CallProcess', 'process_select_files_data', sFiles, [], ...
    'subjectname',   SubjectNames{1}, ...
    'condition',     '', ...
    'tag',           'Avg: deviant', ...
    'includebad',    0, ...
    'includeintra',  0, ...
    'includecommon', 0);

% Process: Low-pass:30Hz
sFiles = bst_process('CallProcess', 'process_bandpass', sFiles, [], ...
    'highpass',    0, ...
    'lowpass',     30, ...
    'mirror',      1, ...
    'sensortypes', 'MEG', ...
    'overwrite',   0);

% Process: Snapshot: Recordings time series
sFiles = bst_process('CallProcess', 'process_snapshot', sFiles, [], ...
    'target',         5, ...  % Recordings time series
    'modality',       1, ...  % MEG (All)
    'orient',         4, ...  % bottom
    'time',           0.11, ...
    'contact_time',   [0, 0.1], ...
    'contact_nimage', 12, ...
    'threshold',      20, ...
    'Comment',        'Run');

You will find one block per process you selected in the pipeline editor. They all have the same syntax:
output_files = bst_process('CallProcess', process_name, input_files_A, input_files_B, options_list);

process_name: String indicating the function corresponding to the process to execute. To know from the pipeline editor what is the path to the process function: hover your mouse over the selected process, as illustrated in this tutorial.
input_files_A: List of input files in Process1, or FilesA in Process2. It can be a cell array of files names (full path, or relative path from the protocol folder), or an array of structures describing the files in the database (returned by a previous call to bst_process).
input_files_B: Empty for Process1, or FilesB in Process2. Cell array of strings or array of struct.
options_list: Pairs of (option_name, option_values), one for each option of the process.
output_files: Array of structures describing the files in output of the process. If the process created new files, this variable contains the new files. If the process didn't create new files or was modifying exiting files, most of the time this variable would contain the same files as the input list.

Line by line: Footer

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

Closes the current report and saves it in the user Brainstorm report folder ($HOME/.brainstorm/reports). These reports are in .mat format and contain all the information necessary to re-run the execution exactly in the same way, but they not easy to read.

The parameter "sFiles" is optional, it indicates what are the files that are considered as the final results of the script. You can remove it without breaking your script: ReportFile = bst_report('Save');

bst_report('Open', ReportFile);

Opens the report viewer to display what happened during the execution. This is equivalent to using the menu File > Report viewer. You can comment this line (ie. add a "%" at the beginning of the line) if you don't want to show the report at the end of the execution.

% bst_report('Export', ReportFile, ExportDir);

This function exports the report in readable format, as an HTML that includes all the screen captures embedded in it. It is disabled by default. If you want to use this feature: remove the "%" at the beginning of the line, and define the variable ExportDir.

ExportDir must be a string that defines where to save the HTML report. It can be wither the absolute path to a HTML file (eg. 'C:\Users\myuser\Documents\report_example.html') or just a folder (eg. 'C:\Users\myuser\Documents'). If you enter only a path to a folder, a default file name including the protocol name and a date tag is generated (report_ProtocolName_YYMMDD_HHMMSS.html).

Simplify the calls

This script you generated is like any Matlab script: you can edit it, rename the variables, add tests and loops, etc. The first important thing to understand is how to edit the options or change the inputs/outputs of a process. The script generator uses only one variable for all the file lists (sFiles) and the output process is always the input of the following process. This is usually too restrictive to write a full analysis script: we commonly need to have multiple lists of files or to run two different operations on the same file.

Let's consider the first process call, which selects the averages for the Deviant condition in both runs.

sFiles = bst_process('CallProcess', 'process_select_files_data', sFiles, [], ...
    'subjectname',   SubjectNames{1}, ...
    'condition',     '', ...
    'tag',           'Avg: deviant', ...
    'includebad',    0, ...
    'includeintra',  0, ...
    'includecommon', 0);

There is no need to set the parameter sFiles, because there is no input, you can replace it with an empty matrix []. You can therefore remove the line "sFiles = [];". We can also rename the output variable "sAvgData", to be a bit more specific.

sAvgData = bst_process('CallProcess', 'process_select_files_data', [], [], ...

You can omit all the options that are not defined, not used, or kept to their default values:

sAvgData = bst_process('CallProcess', 'process_select_files_data', [], [], ...
    'subjectname',   SubjectNames{1}, ...
    'tag',           'Avg: deviant');

Edit the call to the low-pass filter: Change the input to sAvgData and the output to sAvgDataLow, this way we will be able to keep track of the two files if we need to use them independently later in the script.

sAvgDataLow = bst_process('CallProcess', 'process_bandpass', sAvgData, [], ...
    'highpass',    0, ...
    'lowpass',     30, ...
    'sensortypes', 'MEG');

Edit the call to the snapshot process: Change the input to sAvgDataLow, and remove the output parameter (we are not expecting output from it).

bst_process('CallProcess', 'process_snapshot', sAvgDataLow, [], ...
    'target',   5, ...  % Recordings time series
    'modality', 1);     % MEG (All)

Replace the last lines with the following, to export the report instead of opening in the report viewer (edit the file path to point at your own user folder instead).

ReportFile = bst_report('Save');
bst_report('Export', ReportFile, 'C:\Users\franc\Documents\report_test.html');

Interaction with Matlab

Select the code for the first process in the Matlab editor, right-click > Evaluate selection (or press F9).

If you haven't executed your script yet, you will get the following error in the Matlab command window:

Undefined variable "SubjectNames" or class "SubjectNames".

The variable SubjectNames is not defined yet: Execute the first two lines "SubjectNames = {'Subject01'}", then try again. You should now have a new variable in your Matlab workspace, which points at the two average files. Type "sAvgData(1)" in your command window to display the first element:

>> sAvgData(1)
ans =
          iStudy: 6
           iItem: 1
        FileName: 'Subject01/S01_AEF_20131218_01_600Hz_notch/data_deviant_average_160513_1329.mat'
        FileType: 'data'
         Comment: 'Avg: deviant (39 files)'
       Condition: 'S01_AEF_20131218_01_600Hz_notch'
     SubjectFile: 'Subject01/brainstormsubject.mat'
     SubjectName: 'Subject01'
        DataFile: ''
     ChannelFile: 'Subject01/S01_AEF_20131218_01_600Hz_notch/channel_ctf_acc1.mat'
    ChannelTypes: {'ADC A'  'ADC V'  'DAC'  'ECG'  'EOG'  'FitErr'  'HLU'  'MEG'  'MEG REF' ...}

The field "sAvgData(1).FileName" contains the relative path to the to the Deviant average in the first run. This structure sAvgData contains also a lot of information that can be helpful in your script:

iStudy / iItem: Reference of the file in the database (described later in this tutorial).
FileType: 'raw' (continuous files), 'data' (recordings), 'results' (sources), 'timefreq' (time-frequency, spectrum and connectivity), or 'matrix' (any time series extracted from other files).
Comment: Comment field of the file (what is displayed in the database explorer)
Condition: Name of the condition/folder in which the file is located
SubjectFile: Relative path to the subject file (brainstormsubject.mat)
SubjectName: Name of the subject
DataFile: For types 'results' or 'timefreq', path of the parent file in the database explorer
ChannelFile: Relative path to the channel file
ChannelTypes: Cell array of channel types available for the input file

Naming conventions

To help you navigate in the Brainstorm code, here are some naming conventions:

Structures: Name starting with a "s" followed by a capital letter (eg. sFiles).
Indices: Either loop variables or array indices, name starting with a "i" (eg. iSubject, iStudy, iTime).
Counts: Number of elements in a group, name starting with a "n" (eg. nAvg, nTrials, nSubjects).
File names: Scripts and functions, only lower case, separation with "_" (eg. process_average)
Sub-functions: Inside a .m file, name starting with a capital, camel-case (eg. CallProcess, Start).

Running the script

The simplified script looks like this:

% Input files
SubjectNames = {'Subject01'};
% Start a new report
bst_report('Start');

% Process: Select data files in: Subject01/*/Avg: Deviant
sAvgData = bst_process('CallProcess', 'process_select_files_data', [], [], ...
    'subjectname',   SubjectNames{1}, ...
    'tag',           'Avg: deviant');
% Process: Low-pass:30Hz
sAvgDataLow = bst_process('CallProcess', 'process_bandpass', sAvgData, [], ...
    'highpass',    0, ...
    'lowpass',     30, ...
    'sensortypes', 'MEG');
% Process: Snapshot: Recordings time series
bst_process('CallProcess', 'process_snapshot', sAvgDataLow, [], ...
    'target',   5, ...  % Recordings time series
    'modality', 1);     % MEG (All)

% Save and display report
ReportFile = bst_report('Save');
bst_report('Export', ReportFile, 'C:\Users\franc\Documents\report_test.html');

You have three ways to execute it:

Select all the lines (Ctrl+A) and evaluate it in Matlab (F9).
In the Editor toolbar of the Matlab environment, click on the button [Run].
Save the file, go to this folder with Matlab (or add it to your path) and type the name of the script in the command window (without the ".m" at the end).

At the end of the execution, nothing happens, because we indicated we wanted to just export the report instead of opening it. To check out the report of execution: use the menu File > Report viewer from the Brainstorm window, or open the report_test.html that was saved somewhere on your computer.

Starting Brainstorm

- gui / nogui - selecting protocol - delete existing protocol

Selecting files

- Inputs / outputs

- Select processes

- Adding tags to help with the file selection later

Database requests

bst_get / bst_set

iStudy: Index of the study, get the structure with sStudy=bst_get('Study', sInput.iStudy)
iItem: Index of file in its category (example for recordings: sStudy.Data(sInput.iItem))

sStudy = bst_get('Study', sAvgData(1).iStudy)

sStudy.Data(sAvgData(1).iItem)

File manipulation

Modify a structure manually: Export to Matlab/Import from Matlab
File manipulation: file_short, file_fullpath, in_bst_*...
Documentation of all file structures: point at the appropriate tutorials
Select files from the database (with bst_get and processes)

Loop over subject and runs

Creating loops is not supported yet by the script generator, but relatively easy to do from a script without having to know too much about Matlab programming.

1) Fill the cell array SubjectNames with all your subjects names, with the same dimensions as the list of input raw files (sFiles)

2) Add a "for" loop that includes all the bst_process() calls (leave the bst_report() calls and input definition outside)
3) Inside the loop, replace SubjectNames with SubjectNames{i} and sFiles with sFiles(i)

How to process many subjects

This section proposes a standard workflow for processing a full group study with Brainstorm. It contains the same steps of analysis as the introduction tutorials, but separating what can be done automatically from what should be done manually. This workflow can be adapted to most ERP studies (stimulus-based).

Prototype: Start by processing one or two subjects completely interactively (exactly like in the introduction tutorials). Use the few pilot subjects that you have for your study to prototype the analysis pipeline and check manually all the intermediate stages. Take notes of what you're doing along the way, so that you can later write a script that reproduces the same operations.
Anatomical fiducials: Set NAS/LPA/RPA and compute the MNI transformation for each subject.
- Segmentation: Run FreeSurfer/BrainSuite to get surfaces and atlases for all the subjects.
- File > Batch MRI fiducials: This menu prompts for the selection of the fiducials for all the subjects and saves a file fiducials.m in each segmentation folder. You will not have to redo this even if you have to start over your analysis from the beginning.
- Script: Write a loop that calls the process "Import anatomy folder" for all the subjects.
- Alternatives: Create and import the subjects one by one and set the fiducials at the import time. Or use the default anatomy for all the subjects (or use warped templates).
Script #1: Pre-processing: Loop on the subjects and the acquisition runs.
- Create link to raw files: Link the subject and noise recordings to the database.
- Event markers: Read and group triggers from digital and analog channel, fix stimulation delays
- Evaluation: Power spectrum density of the recordings to evaluate their quality.
- Pre-processing: Notch filter, sinusoid removal, band-pass filter.
- Evaluation: Power spectrum density of the recordings to make sure the filters worked well.
- Cleanup: Delete the links to the original files (the filtered ones are copied in the database).
- Detect artifacts: Detect heartbeats, Detect eye blinks, Remove simultaneous.
- Compute SSP: Heartbeats, Blinks (this selects the first component of each decomposition)
- Compute ICA: If you have some artifacts you'd like to remove with ICA (no default selection).
- Screenshots: Check the MRI/sensors registration, PSD before and after corrections, SSP.
- Export the report: One report per subject, or one report for all the subjects, saved in HTML.
Manual inspection #1:
- Check the reports: Information messages (number of events, errors and warnings) and screen captures (registration problems, obvious noisy channels, incorrect SSP topographies).
- Mark bad channels: Open the recordings, select the channels and mark them as bad. Or use the process "Set bad channels" to mark the same bad channels in multiple files.
- Fix the SSP/ICA: For the suspicious runs: Open the file, adjust the list of blink and cardiac events, remove and recompute the SSP decompositions, manually select the components.
- Detect other artifacts: Run the process on all the runs of all the subjects at once (select all the files in Process1 and run the process, or generate the equivalent script).
- Mark bad segments: Review the artifacts detected in 1-7Hz and 40-240Hz, keep only the ones you really want to remove, then mark the event categories as bad. Review quickly the rest of the file and check that there are no other important artifacts.
- Additional SSP: If you find one type of artifact that repeats (typically saccades and SQUID jumps), you can create additional SSP projectors, either with the process "SSP: Generic" or directly from a topography figure (right-click on the figure > Snapshot> Use as SSP projector).
Script #2: Subject-level analysis: Epoching, averaging, sources, time-frequency.
- Importing: Process "Import MEG/EEG: Events" and "Pre-process > Remove DC offset".
- Averaging: Average trials by run, average runs by subject (registration problem in MEG).
- Noise covariance: Compute from empty room or resting recordings, copy to other folders.
- Head model: Compute for each run, or compute once and copy if the runs are co-registered.
- Sources: Compute for each run, average across runs and subjects in source space for MEG.
- Time-frequency: Computation with Hilbert transform or Morlet wavelets, then normalize.
- Screenshots: Check the quality of all the averages (time series, topographies, sources).
- Export the report: One report per subject, or one report for all the subjects, saved in HTML.
Manual inspection #2:
- Check the reports: Check the number of epochs imported and averaged in each condition, check the screen capture of the averages (all the primary responses should be clearly visible).
- Regions of interest: If not using predefined regions from an atlas, define the scouts on the anatomy of each subject (or on the template and then project them to the subjects).
Script #3: Group analysis, ROI-based analysis, etc.
- Averaging: Group averages for the sensor data, the sources and the time-frequency maps.
- Statistics: Contrast between conditions or groups of subjects.
- Regions of interest: Any operation that involve scouts.

Final script

The following script from the Brainstorm distribution reproduces the introduction tutorials ("Get started"): brainstorm3/toolbox/script/tutorial_introduction.m

function tutorial_introduction(tutorial_dir, reports_dir)
% TUTORIAL_INTRODUCTION: Script that runs all the Brainstorm introduction tutorials.
%
% INPUTS: 
%    - tutorial_dir : Directory where the sample_introduction.zip file has been unzipped
%    - reports_dir  : Directory where to save the execution report (instead of displaying it)

% @=============================================================================
% This function is part of the Brainstorm software:
% https://neuroimage.usc.edu/brainstorm
% 
% Copyright (c) University of Southern California & McGill University
% This software is distributed under the terms of the GNU General Public License
% as published by the Free Software Foundation. Further details on the GPLv3
% license can be found at http://www.gnu.org/copyleft/gpl.html.
% 
% FOR RESEARCH PURPOSES ONLY. THE SOFTWARE IS PROVIDED "AS IS," AND THE
% UNIVERSITY OF SOUTHERN CALIFORNIA AND ITS COLLABORATORS DO NOT MAKE ANY
% WARRANTY, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO WARRANTIES OF
% MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE, NOR DO THEY ASSUME ANY
% LIABILITY OR RESPONSIBILITY FOR THE USE OF THIS SOFTWARE.
%
% For more information type "brainstorm license" at command prompt.
% =============================================================================@
%
% Author: Francois Tadel, 2016-2017


% ===== FILES TO IMPORT =====
% Output folder for reports
if (nargin < 2) || isempty(reports_dir) || ~isdir(reports_dir)
    reports_dir = [];
end
% You have to specify the folder in which the tutorial dataset is unzipped
if (nargin == 0) || isempty(tutorial_dir) || ~file_exist(tutorial_dir)
    error('The first argument must be the full path to the dataset folder.');
end
% Subject name
SubjectName = 'Subject01';
% Build the path of the files to import
AnatDir    = fullfile(tutorial_dir, 'sample_introduction', 'anatomy');
Run1File   = fullfile(tutorial_dir, 'sample_introduction', 'data', 'S01_AEF_20131218_01_600Hz.ds');
Run2File   = fullfile(tutorial_dir, 'sample_introduction', 'data', 'S01_AEF_20131218_02_600Hz.ds');
NoiseFile  = fullfile(tutorial_dir, 'sample_introduction', 'data', 'S01_Noise_20131218_02_600Hz.ds');
% Check if the folder contains the required files
if ~file_exist(Run1File)
    error(['The folder ' tutorial_dir ' does not contain the folder from the file sample_introduction.zip.']);
end
% Re-inialize random number generator
if (bst_get('MatlabVersion') >= 712)
    rng('default');
end


%% ===== TUTORIAL #1: CREATE PROTOCOL ================================================
%  ===================================================================================
disp([10 'DEMO> Tutorial #1: Create protocol' 10]);
% The protocol name has to be a valid folder name (no spaces, no weird characters...)
ProtocolName = 'TutorialIntroduction';
% Start brainstorm without the GUI
if ~brainstorm('status')
    brainstorm nogui
end
% Delete existing protocol
gui_brainstorm('DeleteProtocol', ProtocolName);
% Create new protocol
gui_brainstorm('CreateProtocol', ProtocolName, 0, 0);
% Start a new report
bst_report('Start');
% Reset colormaps
bst_colormaps('RestoreDefaults', 'meg');
% Set the current display mode to 'butterfly'
bst_set('TSDisplayMode', 'butterfly');


%% ===== TUTORIAL #2: IMPORT ANATOMY =================================================
%  ===================================================================================
disp([10 'DEMO> Tutorial #2: Import anatomy' 10]);
% Process: Import FreeSurfer folder
bst_process('CallProcess', 'process_import_anatomy', [], [], ...
    'subjectname', SubjectName, ...
    'mrifile',     {AnatDir, 'FreeSurfer'}, ...
    'nvertices',   15000, ...
    'nas', [127, 213, 139], ...
    'lpa', [ 52, 113,  96], ...
    'rpa', [202, 113,  91]);
% This automatically calls the SPM registration procedure because the AC/PC/IH points are not defined



%% ===== TUTORIAL #3: EXPLORE ANATOMY ================================================
%  ===================================================================================
disp([10 'DEMO> Tutorial #3: Explore anatomy' 10]);
% Get subject definition
sSubject = bst_get('Subject', SubjectName);
% Get MRI file and surface files
MriFile    = sSubject.Anatomy(sSubject.iAnatomy).FileName;
CortexFile = sSubject.Surface(sSubject.iCortex).FileName;
HeadFile   = sSubject.Surface(sSubject.iScalp).FileName;
% Display MRI
hFigMri1 = view_mri(MriFile);
hFigMri3 = view_mri_3d(MriFile, [], [], 'NewFigure');
hFigMri2 = view_mri_slices(MriFile, 'x', 20); 
pause(0.5);
% Close figures
close([hFigMri1 hFigMri2 hFigMri3]);
% Display scalp and cortex
hFigSurf = view_surface(HeadFile);
hFigSurf = view_surface(CortexFile, [], [], hFigSurf);
hFigMriSurf = view_mri(MriFile, CortexFile);
% Figure configuration
iTess = 2;
panel_surface('SetShowSulci',     hFigSurf, iTess, 1);
panel_surface('SetSurfaceColor',  hFigSurf, iTess, [1 0 0]);
panel_surface('SetSurfaceSmooth', hFigSurf, iTess, 0.5, 0);
panel_surface('SetSurfaceTransparency', hFigSurf, iTess, 0.8);
figure_3d('SetStandardView', hFigSurf, 'left');
pause(0.5);
% Close figures
close([hFigSurf hFigMriSurf]);



%% ===== TUTORIAL #4: CHANNEL FILE ===================================================
%  ===================================================================================
disp([10 'DEMO> Tutorial #4: Channel file' 10]);
% Process: Create link to raw files
sFilesRun1 = bst_process('CallProcess', 'process_import_data_raw', [], [], ...
    'subjectname',  SubjectName, ...
    'datafile',     {Run1File, 'CTF'}, ...
    'channelalign', 1);
sFilesRun2 = bst_process('CallProcess', 'process_import_data_raw', [], [], ...
    'subjectname',  SubjectName, ...
    'datafile',     {Run2File, 'CTF'}, ...
    'channelalign', 1);
sFilesNoise = bst_process('CallProcess', 'process_import_data_raw', [], [], ...
    'subjectname',  SubjectName, ...
    'datafile',     {NoiseFile, 'CTF'}, ...
    'channelalign', 0);
sFilesRaw = [sFilesRun1, sFilesRun2, sFilesNoise];
% Process: Snapshot: Sensors/MRI registration
bst_process('CallProcess', 'process_snapshot', [sFilesRun1, sFilesRun2], [], ...
    'target',   1, ...  % Sensors/MRI registration
    'modality', 1, ...  % MEG (All)
    'orient',   1, ...  % left
    'Comment',  'MEG/MRI Registration');

% View sensors
hFig = view_surface(HeadFile);
hFig = view_channels(sFilesRun1.ChannelFile, 'MEG', 1, 1, hFig);
% Hide sensors
pause(0.5);
hFig = view_channels(sFilesRun1.ChannelFile, 'MEG', 0, 0, hFig);
% View coils
hFig = view_channels(sFilesRun1.ChannelFile, 'CTF', 1, 1, hFig);
% View helmet
pause(0.5);
hFig = view_helmet(sFilesRun1.ChannelFile, hFig);
pause(0.5);
close(hFig);
% Edit good/bad channel for current file
gui_edit_channel(sFilesRun1.ChannelFile);
pause(0.5);
% Unload everything
bst_memory('UnloadAll', 'Forced');



%% ===== TUTORIAL #5: REVIEW RAW =====================================================
%  ===================================================================================
disp([10 'DEMO> Tutorial #5: Review raw' 10]);
% Process: Convert to continuous (CTF): Continuous
bst_process('CallProcess', 'process_ctf_convert', sFilesRaw, [], ...
    'rectype', 2);  % Continuous

% View recordings
hFigMeg = view_timeseries(sFilesRun1.FileName, 'MEG');
hFigEeg = view_timeseries(sFilesRun1.FileName, 'Misc');
hFigSel = view_timeseries(sFilesRun1.FileName, 'MEG', {'MLT11','MLT12','MLT13'});
% Figure configuration
pause(0.5);
panel_record('SetTimeLength', 3);
panel_record('SetStartTime', 100);
panel_record('SetDisplayMode', hFigMeg, 'column');
panel_montage('SetCurrentMontage', hFigMeg, 'CTF LT');
% Set filters: panel_filter('SetFilters', LowPassEnabled, LowPassValue, HighPassEnabled, HighPassValue, SinRemovalEnabled, SinRemovalValue, MirrorEnabled, FullSourcesEnabled)
panel_filter('SetFilters', 1, 100, 1, 1, 0, [], 0, 0);
pause(0.5);
panel_record('SetDisplayMode', hFigMeg, 'butterfly');
panel_montage('SetCurrentMontage', hFigMeg, '');
% Close figures
close([hFigMeg hFigEeg hFigSel]);



%% ===== TUTORIAL #8: STIM DELAYS ====================================================
%  ===================================================================================
disp([10 'DEMO> Tutorial #8: Stim delays' 10]);
% Process: Detect: standard_fix
bst_process('CallProcess', 'process_evt_detect_analog', [sFilesRun1, sFilesRun2], [], ...
    'eventname',   'standard_fix', ...
    'channelname', 'UADC001', ...
    'timewindow',  [], ...
    'threshold',   1.2, ...
    'blanking',    0.2, ...
    'highpass',    0, ...
    'lowpass',     0, ...
    'refevent',    'standard', ...
    'isfalling',   0, ...
    'ispullup',    0, ...
    'isclassify',  0);
% Process: Detect: deviant_fix
bst_process('CallProcess', 'process_evt_detect_analog', [sFilesRun1, sFilesRun2], [], ...
    'eventname',   'deviant_fix', ...
    'channelname', 'UADC001', ...
    'timewindow',  [], ...
    'threshold',   1.2, ...
    'blanking',    0.2, ...
    'highpass',    0, ...
    'lowpass',     0, ...
    'refevent',    'deviant', ...
    'isfalling',   0, ...
    'ispullup',    0, ...
    'isclassify',  0);
% Process: Read from channel
bst_process('CallProcess', 'process_evt_read', [sFilesRun1, sFilesRun2], [], ...
    'stimchan',  'UDIO001', ...
    'trackmode', 1, ...  % Value: detect the changes of channel value
    'zero',      0);

% Process: Delete events
bst_process('CallProcess', 'process_evt_delete', [sFilesRun1, sFilesRun2], [], ...
    'eventname', 'standard, deviant, button');
% Process: Rename event (standard_fix>standard)
bst_process('CallProcess', 'process_evt_rename', [sFilesRun1, sFilesRun2], [], ...
    'src',  'standard_fix', ...
    'dest', 'standard');
% Process: Rename event (deviant_fix>deviant)
bst_process('CallProcess', 'process_evt_rename', [sFilesRun1, sFilesRun2], [], ...
    'src',  'deviant_fix', ...
    'dest', 'deviant');
% Process: Rename event (64>button)
bst_process('CallProcess', 'process_evt_rename', [sFilesRun1, sFilesRun2], [], ...
    'src',  '64', ...
    'dest', 'button');



%% ===== TUTORIAL #10: FREQUENCY FILTERS =============================================
%  ===================================================================================
disp([10 'DEMO> Tutorial #10: Frequency filters' 10]);
% Process: Sinusoid removal: 60Hz 120Hz 180Hz 300Hz
sFilesNotch = bst_process('CallProcess', 'process_notch', sFilesRaw, [], ...
    'freqlist',    [60, 120, 180], ...
    'sensortypes', 'MEG', ...
    'read_all',    0);
% Process: Power spectrum density (Welch)
sFilesPsd = bst_process('CallProcess', 'process_psd', [sFilesRaw, sFilesNotch], [], ...
    'timewindow',  [], ...
    'win_length',  4, ...
    'win_overlap', 50, ...
    'clusters',    {}, ...
    'sensortypes', 'MEG', ...
    'edit', struct(...
         'Comment',    'Power', ...
         'TimeBands',  [], ...
         'Freqs',      [], ...
         'ClusterFuncTime', 'none', ...
         'Measure',    'power', ...
         'Output',     'all', ...
         'SaveKernel', 0));
% Process: Snapshot: Frequency spectrum
bst_process('CallProcess', 'process_snapshot', sFilesPsd, [], ...
    'target',   10, ...  % Frequency spectrum
    'modality', 1, ...   % MEG (All)
    'Comment',  'Power spectrum density');
% Process: Delete folders
bst_process('CallProcess', 'process_delete', sFilesRaw, [], ...
    'target', 2);  % Delete folders
% Separate the three outputs
sFilesRun1  = {sFilesNotch(1).FileName};
sFilesRun2  = {sFilesNotch(2).FileName};
sFilesNoise = {sFilesNotch(3).FileName};



%% ===== TUTORIAL #11: BAD CHANNELS ==================================================
%  ===================================================================================
% % Process: Set bad channels
% sFiles = bst_process('CallProcess', 'process_channel_setbad', sFilesRun2, [], ...
%     'sensortypes', 'MRT51, MLO52, MLO42, MLO43');



%% ===== TUTORIAL #12: ARTIFACTS DETECTION ===========================================
%  ===================================================================================
disp([10 'DEMO> Tutorial #12: Artifacts detection' 10]);
% Process: Detect heartbeats
bst_process('CallProcess', 'process_evt_detect_ecg', [sFilesRun1, sFilesRun2], [], ...
    'channelname', 'ECG', ...
    'timewindow',  [], ...
    'eventname',   'cardiac');
% Process: Detect eye blinks
bst_process('CallProcess', 'process_evt_detect_eog',  [sFilesRun1, sFilesRun2], [], ...
    'channelname', 'VEOG', ...
    'timewindow',  [], ...
    'eventname',   'blink');
% Process: Remove simultaneous
bst_process('CallProcess', 'process_evt_remove_simult', [sFilesRun1, sFilesRun2], [], ...
    'remove', 'cardiac', ...
    'target', 'blink', ...
    'dt',     0.25, ...
    'rename', 0);



%% ===== TUTORIAL #13: SSP ===========================================================
%  ===================================================================================
disp([10 'DEMO> Tutorial #13: SSP' 10]);
% Process: SSP ECG: cardiac
bst_process('CallProcess', 'process_ssp_ecg', [sFilesRun1, sFilesRun2], [], ...
    'eventname',   'cardiac', ...
    'sensortypes', 'MEG', ...
    'usessp',      0, ...
    'select',      1);
% Process: SSP EOG: blink
bst_process('CallProcess', 'process_ssp_eog', sFilesRun1, [], ...
    'eventname',   'blink', ...
    'sensortypes', 'MEG', ...
    'usessp',      0, ...
    'select',      [1 2]);
bst_process('CallProcess', 'process_ssp_eog', sFilesRun2, [], ...
    'eventname',   'blink', ...
    'sensortypes', 'MEG', ...
    'usessp',      0, ...
    'select',      1);


%% ===== TUTORIAL #14: BAD SEGMENTS ==================================================
%  ===================================================================================
disp([10 'DEMO> Tutorial #14: Bad segments' 10]);
% Process: Detect other artifacts
bst_process('CallProcess', 'process_evt_detect_badsegment', [sFilesRun1, sFilesRun2], [], ...
    'timewindow',  [], ...
    'sensortypes', 'MEG', ...
    'threshold',   3, ...  % 3
    'isLowFreq',   1, ...
    'isHighFreq',  1);

% Process: Rename event (1-7Hz > saccade)  (Run02 only)
bst_process('CallProcess', 'process_evt_rename', sFilesRun2, [], ...
    'src',  '1-7Hz', ...
    'dest', 'saccade');

% Manual selection of saccades (cannot be done from the pipeline editor: manual edition of the structures)
sMatRun2 = in_bst_data(sFilesRun2{1}, 'F');
iEvtSaccade = find(strcmpi({sMatRun2.F.events.label}, 'saccade'));
sMatRun2.F.events(iEvtSaccade).times   = [30, 81.5, 104, 142.5, 167, 187.5, 246.5, 319;  31, 83, 105, 144, 168, 188.5, 248, 320];
sMatRun2.F.events(iEvtSaccade).epochs  = ones(1, size(sMatRun2.F.events(iEvtSaccade).times, 2));
sMatRun2.F.events(iEvtSaccade).channels = [];
sMatRun2.F.events(iEvtSaccade).notes    = [];
bst_save(file_fullpath(sFilesRun2{1}), sMatRun2, 'v6', 1);

% Process: SSP: saccade  (Run02 only)
bst_process('CallProcess', 'process_ssp', sFilesRun2, [], ...
    'timewindow',  [], ...
    'eventname',   'saccade', ...
    'eventtime',   [-0.2, 0.2], ...
    'bandpass',    [1.5, 7], ...
    'sensortypes', 'MEG', ...
    'usessp',      1, ...
    'saveerp',     0, ...
    'method',      1, ...
    'select',      1);
% Process: Detect other artifacts  (Run02 only)
bst_process('CallProcess', 'process_evt_detect_badsegment', sFilesRun2, [], ...
    'timewindow',  [], ...
    'sensortypes', 'MEG', ...
    'threshold',   3, ...  % 3
    'isLowFreq',   1, ...
    'isHighFreq',  1);

% Process: Rename event (1-7Hz > bad_1-7Hz)
bst_process('CallProcess', 'process_evt_rename', [sFilesRun1, sFilesRun2], [], ...
    'src',  '1-7Hz', ...
    'dest', 'bad_1-7Hz');
% Process: Rename event (40-240Hz > bad_40-240Hz)
bst_process('CallProcess', 'process_evt_rename', [sFilesRun1, sFilesRun2], [], ...
    'src',  '40-240Hz', ...
    'dest', 'bad_40-240Hz');
% Process: Snapshot: SSP projectors
bst_process('CallProcess', 'process_snapshot', [sFilesRun1, sFilesRun2], [], ...
    'target',  2, ...  % SSP projectors
    'Comment', 'SSP projectors');



%% ===== TUTORIAL #15: IMPORT EVENTS =================================================
%  ===================================================================================
disp([10 'DEMO> Tutorial #15: Import events' 10]);
% Process: Import MEG/EEG: Events (Run01)
sFilesEpochs1 = bst_process('CallProcess', 'process_import_data_event', sFilesRun1, [], ...
    'subjectname', SubjectName, ...
    'condition',   '', ...
    'eventname',   'standard, deviant', ...
    'timewindow',  [], ...
    'epochtime',   [-0.100, 0.500], ...
    'createcond',  0, ...
    'ignoreshort', 1, ...
    'usectfcomp',  1, ...
    'usessp',      1, ...
    'freq',        [], ...
    'baseline',    [-0.1, -0.0017]);
% Process: Import MEG/EEG: Events (Run02)
sFilesEpochs2 = bst_process('CallProcess', 'process_import_data_event', sFilesRun2, [], ...
    'subjectname', SubjectName, ...
    'condition',   '', ...
    'eventname',   'standard, deviant', ...
    'timewindow',  [], ...
    'epochtime',   [-0.100, 0.500], ...
    'createcond',  0, ...
    'ignoreshort', 1, ...
    'usectfcomp',  1, ...
    'usessp',      1, ...
    'freq',        [], ...
    'baseline',    [-0.1, -0.0017]);
% Display raster plot
hFigRaster = view_erpimage({sFilesEpochs1.FileName}, 'erpimage', 'MEG');
panel_display();
bst_report('Snapshot', hFigRaster, sFilesEpochs1(1).FileName, 'ERP image');
close(hFigRaster);


%% ===== TUTORIAL #16: AVERAGE =======================================================
%  ===================================================================================
disp([10 'DEMO> Tutorial #16: Average' 10]);
% Process: Average: By trial group (folder average)
sFilesAvg = bst_process('CallProcess', 'process_average', [sFilesEpochs1, sFilesEpochs2], [], ...
    'avgtype',    5, ...  % By trial groups (folder average)
    'avg_func',   1, ...  % Arithmetic average: mean(x)
    'weighted',   0, ...
    'keepevents', 1);
% Process: Delete events 'cardiac'
bst_process('CallProcess', 'process_evt_delete', sFilesAvg, [], ...
    'eventname', 'cardiac');
% Process: Snapshot: Recordings time series
bst_process('CallProcess', 'process_snapshot', sFilesAvg, [], ...
    'target',     5, ...  % Recordings time series
    'modality',   1, ...  % MEG (All)
    'Comment',    'Evoked response');
% Set colormap: global color scale
bst_colormaps('SetMaxMode', 'meg', 'global');
% Process: Snapshot: Recordings topography (contact sheet)
bst_process('CallProcess', 'process_snapshot', sFilesAvg, [], ...
    'target',         7, ...  % Recordings topography (contact sheet)
    'modality',       1, ...  % MEG
    'contact_time',   [0, 0.350], ...
    'contact_nimage', 15, ...
    'Comment',        'Evoked response');

% Process: Average+Stderr: By trial group (subject average)
sFilesAvgAll = bst_process('CallProcess', 'process_average', [sFilesEpochs1, sFilesEpochs2], [], ...
    'avgtype',    6, ...  % By trial group (subject average)
    'avg_func',   7, ...  % Arithmetic average + Standard error
    'weighted',   0, ...
    'keepevents', 1);
% Process: Delete events 'cardiac'
bst_process('CallProcess', 'process_evt_delete', sFilesAvgAll, [], ...
    'eventname', 'cardiac');
% Process: Delete events 'saccade'
bst_process('CallProcess', 'process_evt_delete', sFilesAvgAll, [], ...
    'eventname', 'saccade');
% Process: Snapshot: Recordings time series
bst_process('CallProcess', 'process_snapshot', sFilesAvgAll, [], ...
    'target',     5, ...  % Recordings time series
    'modality',   1, ...  % MEG (All)
    'Comment',    'Evoked response');


%% ===== TUTORIAL #17: EXPLORATION ===================================================
%  ===================================================================================
disp([10 'DEMO> Tutorial #17: Bad segments' 10]);
% View averages
hFigMeg1  = view_timeseries(sFilesAvg(1).FileName, 'MEG');
hFigMeg2  = view_timeseries(sFilesAvg(2).FileName, 'MEG');
hFigEeg1  = view_timeseries(sFilesAvg(1).FileName, 'Misc');
hFigEeg2  = view_timeseries(sFilesAvg(2).FileName, 'Misc');
hFigTopo1 = view_topography(sFilesAvg(1).FileName, 'MEG', '2DSensorCap');
hFigTopo2 = view_topography(sFilesAvg(2).FileName, 'MEG', '2DSensorCap');
hFigTp2   = view_topography(sFilesAvg(3).FileName, 'MEG', '3DSensorCap');
hFigTp3   = view_topography(sFilesAvg(3).FileName, 'MEG', '2DDisc');
hFigTp4   = view_topography(sFilesAvg(3).FileName, 'MEG', '2DLayout');
% Set time: 90ms
panel_time('SetCurrentTime', 0.090);
% Set filters: 40Hz low-pass, no high-pass
panel_filter('SetFilters', 1, 40, 0, [], 0, [], 0, 0);
% View selected sensors
SelectedChannels = {'MLC31', 'MLC32'};
bst_figures('SetSelectedRows', SelectedChannels);
view_timeseries(sFilesAvg(4).FileName, [], SelectedChannels);
% Select time window
figure_timeseries('SetTimeSelectionManual', hFigMeg1, [0.070, 0.130]);
% Show sensors on 2DSensorCap topography
isMarkers = 1;
isLabels = 0;
figure_3d('ViewSensors', hFigTopo1, isMarkers, isLabels);
% Display time contact sheet for a figure
pause(0.5);
hContactFig = view_contactsheet( hFigTopo2, 'time', 'fig', [], 12, [0 0.120] );
pause(0.5);
close(hContactFig);



%% ===== TUTORIAL #18: COLORMAPS =====================================================
%  ===================================================================================
disp([10 'DEMO> Tutorial #18: Colormaps' 10]);
% Set 'Meg' colormap to 'jet'
bst_colormaps('SetColormapName', 'meg', 'jet');
pause(0.5);
% Set 'Meg' colormap to 'rwb'
bst_colormaps('SetColormapName', 'meg', 'cmap_rbw');
% Set colormap to display absolute values
bst_colormaps('SetColormapAbsolute', 'meg', 1);
% Normalize colormap for each time frame
bst_colormaps('SetMaxMode', 'meg', 'local');
% Hide colorbar
bst_colormaps('SetDisplayColorbar', 'meg', 0);
pause(0.5);
% Restore colormap to default values
bst_colormaps('RestoreDefaults', 'meg');
% Edit good/bad channel for current file
gui_edit_channelflag(sFilesAvg(1).FileName);
% Close figures
pause(0.5);
bst_memory('UnloadAll', 'Forced');



%% ===== TUTORIAL #20: HEAD MODEL ====================================================
%  ===================================================================================
disp([10 'DEMO> Tutorial #20: Head model' 10]);
% Process: Compute head model
bst_process('CallProcess', 'process_headmodel', sFilesAvg, [], ...
    'comment',      '', ...
    'sourcespace',  1, ...
    'meg',          3);  % Overlapping spheres
% Get study structure
sStudy = bst_get('Study', sFilesAvg(1).iStudy);
% Show spheres
hFig = view_spheres(sStudy.HeadModel(sStudy.iHeadModel).FileName, sStudy.Channel.FileName, sSubject);
pause(0.5);
close(hFig);



%% ===== TUTORIAL #21: NOISE COVARIANCE ==============================================
%  ===================================================================================
disp([10 'DEMO> Tutorial #21: Noise covariance' 10]);
% Process: Compute covariance (noise or data)
bst_process('CallProcess', 'process_noisecov', sFilesNoise, [], ...
    'baseline',       [], ...
    'sensortypes',    'MEG, EEG, SEEG, ECOG', ...
    'target',         1, ...  % Noise covariance     (covariance over baseline time window)
    'dcoffset',       1, ...  % Block by block, to avoid effects of slow shifts in data
    'identity',       0, ...
    'copycond',       1, ...
    'copysubj',       0, ...
    'replacefile',    1);  % Replace
% Process: Snapshot: Noise covariance
bst_process('CallProcess', 'process_snapshot', sFilesNoise, [], ...
    'target',  3, ...  % Noise covariance
    'Comment', 'Noise covariance');

% Process: Compute covariance (noise or data)  [Run01]
bst_process('CallProcess', 'process_noisecov', sFilesEpochs1, [], ...
    'baseline',       [-0.1, -0.0017], ...
    'datatimewindow', [0, 0.5], ...
    'sensortypes',    'MEG, EEG, SEEG, ECOG', ...
    'target',         2, ...  % Data covariance      (covariance over data time window)
    'dcoffset',       1, ...  % Block by block, to avoid effects of slow shifts in data
    'identity',       0, ...
    'copycond',       0, ...
    'copysubj',       0, ...
    'replacefile',    1);  % Replace
% Process: Compute covariance (noise or data)  [Run02]
bst_process('CallProcess', 'process_noisecov', sFilesEpochs2, [], ...
    'baseline',       [-0.1, -0.0017], ...
    'datatimewindow', [0, 0.5], ...
    'sensortypes',    'MEG, EEG, SEEG, ECOG', ...
    'target',         2, ...  % Data covariance      (covariance over data time window)
    'dcoffset',       1, ...  % Block by block, to avoid effects of slow shifts in data
    'identity',       0, ...
    'copycond',       0, ...
    'copysubj',       0, ...
    'replacefile',    1);  % Replace
% Process: Snapshot: Data covariance
bst_process('CallProcess', 'process_snapshot', [sFilesEpochs1(1), sFilesEpochs2(1)], [], ...
    'target',  12, ...  % Data covariance
    'Comment', 'Data covariance');



%% ===== TUTORIAL #22: SOURCE ESTIMATION =============================================
%  ===================================================================================
disp([10 'DEMO> Tutorial #22: Source estimation' 10]);
% === GET DEVIANT AVERAGE RUN01 ===
% Process: Select recordings in: Subject01/S01_AEF_20131218_01_600Hz_notch
sFiles01 = bst_process('CallProcess', 'process_select_files_data', [], [], ...
    'subjectname', SubjectName, ...
    'condition',   'S01_AEF_20131218_01_600Hz_notch', ...
    'includebad',  0);
% Process: Select file comments with tag: deviant
sFilesAvgDeviant01 = bst_process('CallProcess', 'process_select_tag', sFiles01, [], ...
    'tag',    'Avg: deviant', ...
    'search', 2, ...  % Search the file comments
    'select', 1);     % Select only the files with the tag

% === CONSTRAINED EXAMPLE ===
% Minimum norm options
InverseOptions = struct(...
    'Comment',        'MN: MEG', ...
    'InverseMethod',  'minnorm', ...
    'InverseMeasure', 'amplitude', ...
    'SourceOrient',   {{'fixed'}}, ...
    'Loose',          0.2, ...
    'UseDepth',       1, ...
    'WeightExp',      0.5, ...
    'WeightLimit',    10, ...
    'NoiseMethod',    'reg', ...
    'NoiseReg',       0.1, ...
    'SnrMethod',      'fixed', ...
    'SnrRms',         0.001, ...
    'SnrFixed',       3, ...
    'ComputeKernel',  1, ...
    'DataTypes',      {{'MEG'}});
% Process: Compute sources [2018]
sFilesSrcDeviant01 = bst_process('CallProcess', 'process_inverse_2018', sFilesAvgDeviant01, [], ...
    'output',  2, ...  % Kernel only: one per file
    'inverse', InverseOptions);

% === DISPLAY SOURCES MANUALLY ===
% View time series
hFigSrc1 = view_timeseries(sFilesAvgDeviant01(1).FileName, 'MEG');
% View on the cortex surface
hFigSrc2 = script_view_sources(sFilesSrcDeviant01.FileName, 'cortex');
% Set current time to 90ms
panel_time('SetCurrentTime', 0.090);
% Set orientation
figure_3d('SetStandardView', hFigSrc2, 'left');
% Set surface threshold
iSurf = 1;
thresh = .30; 
panel_surface('SetDataThreshold', hFigSrc2, iSurf, thresh);
% Set surface smoothing
panel_surface('SetSurfaceSmooth', hFigSrc2, iSurf, .6, 0);
% Show sulci
panel_surface('SetShowSulci', hFigSrc2, iSurf, 1);

% View sources on MRI (3D orthogonal slices)
hFigSrc3 = script_view_sources(sFilesSrcDeviant01.FileName, 'mri3d');
panel_surface('SetDataThreshold', hFigSrc3, iSurf, thresh);
% Set the position of the cuts in the 3D figure
cutsPosVox = [74 93 159];
panel_surface('PlotMri', hFigSrc3, cutsPosVox);

% View sources with MRI Viewer
hFigSrc4 = script_view_sources(sFilesSrcDeviant01.FileName, 'mriviewer');
panel_surface('SetDataThreshold', hFigSrc4, iSurf, thresh);
% Set the position of the cuts in the MRI Viewer (values in millimeters)
figure_mri('SetLocation', 'voxel', hFigSrc4, [], cutsPosVox);
% Close figures
close([hFigSrc1 hFigSrc2 hFigSrc3 hFigSrc4]);

% === UNCONSTRAINED EXAMPLE ===
% Unconstrained minnorm
InverseOptions.Comment        = 'MN: MEG';
InverseOptions.InverseMeasure = 'amplitude';
InverseOptions.SourceOrient   = {'free'};
% Process: Compute sources [2018]
sFilesSrcUnconst = bst_process('CallProcess', 'process_inverse_2018', sFilesAvgDeviant01, [], ...
    'output',  2, ...  % Kernel only: one per file
    'inverse', InverseOptions);


% === NORMALIZED SOURCES ===
% dSPM
InverseOptions.Comment        = 'dSPM: MEG';
InverseOptions.InverseMeasure = 'dspm2018';
InverseOptions.SourceOrient   = {'fixed'};
sFilesSrcDspm = bst_process('CallProcess', 'process_inverse_2018', sFilesAvgDeviant01, [], ...
    'output',  2, ...  % Kernel only: one per file
    'inverse', InverseOptions);
% sLORETA (old function)
sFilesSrcSloreta = bst_process('CallProcess', 'process_inverse', sFilesAvgDeviant01, [], ...
    'comment', '', ...
    'method',  3, ...  % sLORETA
    'wmne', struct(...
         'SourceOrient', {{'fixed'}}, ...
         'loose',        0.2, ...
         'SNR',          3, ...
         'pca',          1, ...
         'diagnoise',    0, ...
         'regnoise',     1, ...
         'magreg',       0.1, ...
         'gradreg',      0.1, ...
         'depth',        1, ...
         'weightexp',    0.5, ...
         'weightlimit',  10), ...
    'sensortypes', 'MEG, MEG MAG, MEG GRAD, EEG', ...
    'output', 2);  % Kernel only: one per file
% Process: Z-score normalization: [-100ms,-2ms]
sFilesSrcZscore = bst_process('CallProcess', 'process_baseline_norm', sFilesSrcDeviant01, [], ...
    'baseline',   [-0.100, -0.002], ...
    'source_abs', 0, ...
    'method',     'zscore');   % Z-score transformation:    x_std = (x - μ) / σ

% Process: Snapshot: Sources (one time)
bst_process('CallProcess', 'process_snapshot', sFilesSrcDeviant01, [], ...
    'target',    8, ...  % Sources (one time)
    'orient',    1, ...  % left
    'time',      0.09, ...
    'threshold', 30, ...
    'Comment',   'Current density map (Constrained)');
bst_process('CallProcess', 'process_snapshot', sFilesSrcDspm, [], ...
    'target',    8, ...  % Sources (one time)
    'orient',    1, ...  % left
    'time',      0.09, ...
    'threshold', 60, ...
    'Comment',   'dSPM');
bst_process('CallProcess', 'process_snapshot', sFilesSrcSloreta, [], ...
    'target',    8, ...  % Sources (one time)
    'orient',    1, ...  % left
    'time',      0.09, ...
    'threshold', 60, ...
    'Comment',   'sLORETA');
bst_process('CallProcess', 'process_snapshot', sFilesSrcZscore, [], ...
    'target',    8, ...  % Sources (one time)
    'orient',    1, ...  % left
    'time',      0.09, ...
    'threshold', 60, ...
    'Comment',   'Z-score');
bst_process('CallProcess', 'process_snapshot', sFilesSrcUnconst, [], ...
    'target',         8, ...  % Sources (one time)
    'orient',         1, ...  % left
    'time',           0.0917, ...
    'threshold',      0, ...
    'Comment',        'Current density map (Unconstrained)');

% === DELETE EXPERIMENTS ===
% Process: Delete constrained example
bst_process('CallProcess', 'process_delete', [sFilesSrcDeviant01, sFilesSrcDspm, sFilesSrcSloreta, sFilesSrcZscore, sFilesSrcUnconst], [], ...
    'target', 1);  % Delete selected files


% === SHARED KERNEL ===
% Constrained minnorm
InverseOptions.Comment        = 'MN: MEG';
InverseOptions.InverseMeasure = 'amplitude';
InverseOptions.SourceOrient   = {'fixed'};
% Process: Compute sources [2018]
sFilesAvgSrc = bst_process('CallProcess', 'process_inverse_2018', sFilesAvg, [], ...
    'output',  1, ...  % Kernel only: shared
    'inverse', InverseOptions);


% === AVERAGE SOURCES ACROSS RUNS ===
% Process: Average: By trial group (subject average)
sFilesIntraSrc = bst_process('CallProcess', 'process_average', sFilesAvgSrc, [], ...
    'avgtype',         6, ...  % By trial group (subject average)
    'avg_func',        1, ...  % Arithmetic average:  mean(x)
    'weighted',        1, ...
    'scalenormalized', 0);
% Process: Low-pass:40Hz
sFilesIntraSrcLow = bst_process('CallProcess', 'process_bandpass', sFilesIntraSrc, [], ...
    'highpass',    0, ...
    'lowpass',     40, ...
    'attenuation', 'strict', ...  % 60dB
    'mirror',      0, ...
    'overwrite',   0);
% Process: Z-score normalization: [-100ms,-2ms]
sFilesIntraZscore = bst_process('CallProcess', 'process_baseline_norm', sFilesIntraSrcLow, [], ...
    'baseline',   [-0.100, -0.0017], ...
    'source_abs', 0, ...
    'method',     'zscore');  % Z-score transformation:    x_std = (x - μ) / σ

% Process: Delete intermediate results
bst_process('CallProcess', 'process_delete', sFilesIntraSrcLow, [], ...
    'target', 1);  % Delete selected files
% Screen captures
bst_process('CallProcess', 'process_snapshot', sFilesIntraZscore, [], ...
    'target',    8, ...  % Sources (one time)
    'orient',    1, ...  % left
    'time',      0.09, ...
    'threshold', 40, ...
    'Comment',   'Average across runs (left)');
bst_process('CallProcess', 'process_snapshot', sFilesIntraZscore, [], ...
    'target',    8, ...  % Sources (one time)
    'orient',    2, ...  % right
    'time',      0.09, ...
    'threshold', 40, ...
    'Comment',   'Average across runs (right)');
bst_process('CallProcess', 'process_snapshot', sFilesIntraZscore(1), [], ...
    'target',         9, ...  % Sources (contact sheet)
    'orient',         1, ...  % left
    'contact_time',   [0, 0.35], ...
    'contact_nimage', 15, ...
    'threshold',      20, ...
    'Comment',        'Average across runs');


%% ===== TUTORIAL #23: SCOUTS ========================================================
%  ===================================================================================
disp([10 'DEMO> Tutorial #23: Scouts' 10]);
% Load surface file
sCortex = in_tess_bst(CortexFile);
% Add new scouts in first atlas
sCortex.iAtlas = find(strcmpi({sCortex.Atlas.Name}, 'Destrieux'));
% Save file
bst_save(file_fullpath(CortexFile), sCortex, 'v7');
% Unload everything
bst_memory('UnloadAll', 'Forced');
% Find scouts indices to display: {'A1L', 'A1R', 'IFGL', 'M1L'}
[tmp,iScouts,tmp] = intersect({sCortex.Atlas(sCortex.iAtlas).Scouts.Label}, {'G_temp_sup-G_T_transv L', 'G_temp_sup-G_T_transv R', 'G_front_inf-Opercular L', 'G_precentral L'});

% View cortex
hFigSurf1 = view_surface(CortexFile, [], [], 'NewFigure');
hFigSurf2 = view_surface(CortexFile, [], [], 'NewFigure');
figure_3d('SetStandardView', hFigSurf1, 'left');
figure_3d('SetStandardView', hFigSurf2, 'right');
panel_surface('SetSurfaceSmooth', hFigSurf1, 1, .6, 0);
panel_surface('SetSurfaceSmooth', hFigSurf2, 1, .6, 0);
panel_surface('SetShowSulci', hFigSurf1, 1, 1);
panel_surface('SetShowSulci', hFigSurf2, 1, 1);
% Configure scouts display
panel_scout('SetScoutsOptions', 0, 1, 1, 'all', 0.7, 1, 1, 0);
% View scouts
hFigScouts = view_scouts({sFilesIntraZscore.FileName}, iScouts);
hLegend = findobj(hFigScouts, 'Type', 'legend');
if ~isempty(hLegend) && ishandle(hLegend(1))
    set(hLegend(1), 'Units', 'pixels');
    pos = get(hLegend(1), 'Position');
    set(hLegend(1), 'Position', [1, 1, pos(3), pos(4)]);
end
% Save figures
bst_report('Snapshot', hFigScouts, sFilesIntraZscore(1).FileName, 'Scouts', [100 100 670 250]);
% Close all
pause(1);
bst_memory('UnloadAll', 'Forced');



%% ===== TUTORIAL #24: TIME-FREQUENCY ================================================
%  ===================================================================================
disp([10 'DEMO> Tutorial #24: Time-frequency' 10]);
% Process: Simulate generic signals
sSim = bst_process('CallProcess', 'process_simulate_matrix', [], [], ...
    'subjectname', 'Test', ...
    'condition',   'Simulation', ...
    'samples',     6000, ...
    'srate',       1000, ...
    'matlab',      ['f1 = 2; f2 = 20; f3 = 50;' 10 'i =2000:6000;' 10 'Data(1,i) = sin(f1*2*pi*t(i)) + 0.4 * cos(f2*2*pi*t(i));' 10 'Data = Data + 0.2 * sin(f3*2*pi*t) + 0.4 * rand(1,6000);']);

% Time-frequency options
TfOptions = struct(...
    'Comment',         'Power,1-60Hz', ...
    'TimeBands',       [], ...
    'Freqs',           [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60], ...
    'MorletFc',        1, ...
    'MorletFwhmTc',    3, ...
    'ClusterFuncTime', 'none', ...
    'Measure',         'power', ...
    'Output',          'average', ...
    'SaveKernel',       0);
% Process: Time-frequency (Morlet wavelets)
sSimTf1 = bst_process('CallProcess', 'process_timefreq', sSim, [], ...
    'edit',      TfOptions, ...
    'normalize', 'multiply');  % 1/f compensation: Multiply output values by frequency

% === NORMALIZATION ===
% Process: Time-frequency (Morlet wavelets)
sSimTf2 = bst_process('CallProcess', 'process_timefreq', sSim, [], ...
    'edit',      TfOptions, ...
    'normalize', 'none');  % None: Save non-standardized time-frequency maps
% Process: Spectral flattening
sSimTf2Multi = bst_process('CallProcess', 'process_tf_norm', sSimTf2, [], ...
    'normalize', 'multiply', ...  % 1/f compensation (multiple by frequency)
    'overwrite', 0);
% Process: Event-related perturbation (ERS/ERD): [750ms,1250ms]
sSimTf2Ersd = bst_process('CallProcess', 'process_baseline_norm', sSimTf2, [], ...
    'baseline',  [0.75, 1.25], ...
    'method',    'ersd', ...  % Event-related perturbation (ERS/ERD):    x_std = (x - μ) / μ * 100
    'overwrite', 0);

% Process: Snapshot: Time-frequency maps
bst_process('CallProcess', 'process_snapshot', sSimTf2, [], ...
    'target',   14, ...  % Time-frequency maps
    'Comment',  'Not normalized');
% Process: Snapshot: Time-frequency maps
bst_process('CallProcess', 'process_snapshot', sSimTf2Multi, [], ...
    'target',   14, ...  % Time-frequency maps
    'Comment',  'Spectral flattening: 1/f compensation');
% Process: Snapshot: Time-frequency maps
bst_process('CallProcess', 'process_snapshot', sSimTf2Ersd, [], ...
    'target',   14, ...  % Time-frequency maps
    'Comment',  'ERS/ERD');
% Spectrum/Time series
hFigTf1 = view_spectrum(sSimTf2.FileName, 'Spectrum');
hFigTf2 = view_spectrum(sSimTf2.FileName, 'TimeSeries');
panel_time('SetCurrentTime', 0.5);
panel_freq('SetCurrentFreq', 2, 0);
bst_report('Snapshot', [hFigTf1 hFigTf2], sSimTf2.FileName, 'Not normalized: 2s/20Hz', [200, 200, 400, 250]);
panel_time('SetCurrentTime', 2.02);
panel_freq('SetCurrentFreq', 20, 0);
bst_report('Snapshot', [hFigTf1 hFigTf2], sSimTf2.FileName, 'Not normalized: 2s/20Hz', [200, 200, 400, 250]);
bst_memory('UnloadAll', 'Forced');


% === HILBERT TRANSFORM ===
% Process: Hilbert transform
sSimHilbert = bst_process('CallProcess', 'process_hilbert', sSim, [], ...
    'edit', struct(...
         'Comment',         'Power', ...
         'TimeBands',       [], ...
         'Freqs',           {{'delta', '2, 4', 'mean'; 'theta', '5, 7', 'mean'; 'alpha', '8, 12', 'mean'; 'beta', '15, 29', 'mean'; 'gamma1', '30, 59', 'mean'; 'gamma2', '60, 90', 'mean'}}, ...
         'ClusterFuncTime', 'none', ...
         'Measure',         'power', ...
         'Output',          'all', ...
         'SaveKernel',      0), ...
    'normalize', 'none', ...  % None: Save non-standardized time-frequency maps
    'mirror',    0);
% Process: Spectral flattening
sSimHilbertMulti = bst_process('CallProcess', 'process_tf_norm', sSimHilbert, [], ...
    'normalize', 'multiply', ...  % 1/f compensation (multiple by frequency)
    'overwrite', 0);
% Process: Event-related perturbation (ERS/ERD): [750ms,1250ms]
sSimHilbertErsd = bst_process('CallProcess', 'process_baseline_norm', sSimHilbert, [], ...
    'baseline',  [0.75, 1.25], ...
    'method',    'ersd', ...  % Event-related perturbation (ERS/ERD):    x_std = (x - μ) / μ * 100
    'overwrite', 0);

% Process: Snapshot: Time-frequency maps
bst_process('CallProcess', 'process_snapshot', sSimHilbert, [], ...
    'target',   14, ...  % Time-frequency maps
    'Comment',  'Not normalized');
% Process: Snapshot: Time-frequency maps
bst_process('CallProcess', 'process_snapshot', sSimHilbertMulti, [], ...
    'target',   14, ...  % Time-frequency maps
    'Comment',  'Spectral flattening: 1/f compensation');
% Process: Snapshot: Time-frequency maps
bst_process('CallProcess', 'process_snapshot', sSimHilbertErsd, [], ...
    'target',   14, ...  % Time-frequency maps
    'Comment',  'ERS/ERD');

% === SINGLE TRIALS ===
TfOptions.Comment = 'Avg,Power,1-150Hz';
TfOptions.Freqs   = [1, 2, 3.1, 4.2, 5.4, 6.7, 8, 9.5, 11, 12.6, 14.3, 16.1, 18.1, 20.1, 22.3, 24.6, 27, 29.6, 32.4, 35.3, 38.4, 41.6, 45.1, 48.8, 52.7, 56.9, 61.3, 66, 70.9, 76.2, 81.8, 87.7, 94, 100.6, 107.7, 115.2, 123.1, 131.6, 140.5, 150];
% Process: Time-frequency (Morlet wavelets)
sEpochsAvgTf = bst_process('CallProcess', 'process_timefreq', sFilesEpochs1, [], ...
    'sensortypes', 'MEG, EEG', ...
    'edit',        TfOptions, ...
    'normalize',   'none');  % None: Save non-standardized time-frequency maps
% Process: Event-related perturbation (ERS/ERD): [-75ms,0ms]
sEpochsAvgTfErsd = bst_process('CallProcess', 'process_baseline_norm', sEpochsAvgTf, [], ...
    'baseline',  [-0.075, 0], ...
    'method',    'ersd', ...  % Event-related perturbation (ERS/ERD):    x_std = (x - μ) / μ * 100
    'overwrite', 0);

% === DISPLAY ===
% View time-frequency file
hFigTf1 = view_timefreq(sEpochsAvgTfErsd.FileName, 'SingleSensor');
% Configure display
sOptions = panel_display('GetDisplayOptions');
sOptions.HideEdgeEffects = 1;
sOptions.HighResolution = 1;
panel_display('SetDisplayOptions', sOptions);
% Other display modes
hFigTf2 = view_timefreq(sEpochsAvgTfErsd.FileName, 'AllSensors');
hFigTf3 = view_timefreq(sEpochsAvgTfErsd.FileName, '2DLayout');
hFigTf4 = view_timefreq(sEpochsAvgTfErsd.FileName, '2DLayoutOpt');
bst_colormaps('SetColormapName', 'stat2', 'jet');
bst_colormaps('SetColormapAbsolute', 'stat2', 1);
bst_report('Snapshot', hFigTf1, sEpochsAvgTfErsd.FileName, 'Time-frequency', [200, 200, 400, 250]);
bst_report('Snapshot', [hFigTf2 hFigTf3 hFigTf4], sEpochsAvgTfErsd.FileName, 'Time-frequency', [200, 200, 750, 400]);
close([hFigTf1 hFigTf2 hFigTf3 hFigTf4]);
% Image [channel x time]
hFigTf5 = view_erpimage(sEpochsAvgTfErsd.FileName, 'trialimage');
% Topographies
hFigTf6 = view_topography(sEpochsAvgTfErsd.FileName, 'MEG', '3DSensorCap');
hFigTf7 = view_topography(sEpochsAvgTfErsd.FileName, 'MEG', '2DDisc');
hFigTf8 = view_topography(sEpochsAvgTfErsd.FileName, 'MEG', '2DSensorCap');
hFigTf9 = view_topography(sEpochsAvgTfErsd.FileName, 'MEG', '2DLayout');
panel_time('SetCurrentTime', 0.175);
panel_freq('SetCurrentFreq', 8, 0);
bst_report('Snapshot', [hFigTf5 hFigTf6 hFigTf7 hFigTf8 hFigTf9], sEpochsAvgTfErsd.FileName, 'Time-frequency: 8Hz', [200, 200, 400, 250]);
close([hFigTf5 hFigTf6 hFigTf7 hFigTf8 hFigTf9]);


% === AVERAGE FOR SCOUTS ===
% Select all sources for the single deviant epochs
sFilesEpochDeviantSrc = bst_process('CallProcess', 'process_select_files_results', [], [], ...
    'subjectname', SubjectName, ...
    'condition',   '', ...
    'includebad',  0);
sFilesEpochDeviantSrc = bst_process('CallProcess', 'process_select_tag', sFilesEpochDeviantSrc, [], ...
    'tag',    'deviant', ...
    'search', 1, ...  % Search the file names
    'select', 1);     % Select only the files with the tag
sFilesEpochDeviantSrc = bst_process('CallProcess', 'process_select_tag', sFilesEpochDeviantSrc, [], ...
    'tag',    'average', ...
    'search', 1, ...  % Search the file names
    'select', 2);     % Exclude the files with the tag

% Process: Time-frequency (Morlet wavelets)
sFilesTfScout = bst_process('CallProcess', 'process_timefreq', sFilesEpochDeviantSrc, [], ...
    'clusters',   {'Destrieux', {'G_temp_sup-G_T_transv L', 'G_temp_sup-G_T_transv R', 'G_front_inf-Opercular L', 'G_precentral L'}}, ...
    'scoutfunc',  1, ...  % Mean
    'edit', struct(...
         'Comment',         'Deviant: Scouts,Avg,Power,1-150Hz', ...
         'TimeBands',       [], ...
         'Freqs',           [1, 2, 3.1, 4.2, 5.4, 6.7, 8, 9.5, 11, 12.6, 14.3, 16.1, 18.1, 20.1, 22.3, 24.6, 27, 29.6, 32.4, 35.3, 38.4, 41.6, 45.1, 48.8, 52.7, 56.9, 61.3, 66, 70.9, 76.2, 81.8, 87.7, 94, 100.6, 107.7, 115.2, 123.1, 131.6, 140.5, 150], ...
         'MorletFc',        1, ...
         'MorletFwhmTc',    3, ...
         'ClusterFuncTime', 'after', ...
         'Measure',         'power', ...
         'Output',          'average', ...
         'SaveKernel',      0), ...
    'normalize',  'none');   % None: Save non-standardized time-frequency maps)
% Process: Event-related perturbation (ERS/ERD): [-75ms,0ms]
sFilesTfScoutErsd = bst_process('CallProcess', 'process_baseline_norm', sFilesTfScout, [], ...
    'baseline',  [-0.075, 0], ...
    'method',    'ersd', ...  % Event-related perturbation (ERS/ERD):    x_std = (x - μ) / μ * 100
    'overwrite', 0);
% Process: Snapshot: Time-frequency maps
bst_process('CallProcess', 'process_snapshot', sFilesTfScoutErsd, [], ...
    'target',   14, ...  % Time-frequency maps
    'Comment',  'ERS/ERD');


% === FULL CORTEX / HILBERT ===
% Process: Hilbert transform
sFilesHilbertCortex = bst_process('CallProcess', 'process_hilbert', sFilesEpochDeviantSrc, [], ...
    'clusters',  {}, ...
    'scoutfunc', 1, ...  % Mean
    'edit',      struct(...
         'Comment',         'Deviant: Avg,Magnitude', ...
         'TimeBands',       [], ...
         'Freqs',           {{'delta', '2, 4', 'mean'; 'theta', '5, 7', 'mean'; 'alpha', '8, 12', 'mean'; 'beta', '15, 29', 'mean'; 'gamma1', '30, 59', 'mean'; 'gamma2', '60, 90', 'mean'}}, ...
         'ClusterFuncTime', 'none', ...
         'Measure',         'power', ...
         'Output',          'average', ...
         'RemoveEvoked',    0, ...
         'SaveKernel',      0), ...
    'normalize', 'none', ...  % None: Save non-standardized time-frequency maps
    'mirror',    0);
% Process: Event-related perturbation (ERS/ERD): [-75ms,0ms]
sFilesHilbertCortexErsd = bst_process('CallProcess', 'process_baseline_norm', sFilesHilbertCortex, [], ...
    'baseline',  [-0.075, 0], ...
    'method',    'ersd', ...  % Event-related perturbation (ERS/ERD):    x_std = (x - μ) / μ * 100
    'overwrite', 0);

% View results
hFigTf1 = view_surface_data([], sFilesHilbertCortexErsd.FileName);
hFigTf2 = view_timefreq(sFilesHilbertCortexErsd.FileName, 'SingleSensor', 362);
figure_3d('SetStandardView', hFigTf1, 'left');
panel_surface('SetDataThreshold', hFigTf1, 1, 0.5);
panel_time('SetCurrentTime', 0.175);
panel_freq('SetCurrentFreq', 3);
bst_colormaps('RestoreDefaults', 'stat2');
bst_report('Snapshot', [hFigTf1 hFigTf2], sFilesHilbertCortexErsd.FileName, 'Hilbert transform: Alpha band', [200, 200, 400, 250]);
bst_memory('UnloadAll', 'Forced');


%% ===== TUTORIAL #25: DIFFRERENCE ===================================================
%  ===================================================================================
disp([10 'DEMO> Tutorial #25: Difference' 10]);

% ===== SELECT TRIALS (DATA) =====
% Process: Select recordings in: Subject01/*
sFilesAll = bst_process('CallProcess', 'process_select_files_data', [], [], ...
    'subjectname', SubjectName, ...
    'condition',   '', ...
    'includebad',  0);
% Process: Select file names with tag: deviant_trial
sEpochDeviant = bst_process('CallProcess', 'process_select_tag', sFilesAll, [], ...
    'tag',    'deviant_trial', ...
    'search', 1, ...  % Search the file names
    'select', 1);     % Select only the files with the tag
% Process: Select file names with tag: standard_trial
sEpochStandard = bst_process('CallProcess', 'process_select_tag', sFilesAll, [], ...
    'tag',    'standard_trial', ...
    'search', 1, ...  % Search the file names
    'select', 1);     % Select only the files with the tag

% ===== SELECT TRIALS (SOURCE) =====
% Process: Select recordings in: Subject01/*
sFilesAllSrc = bst_process('CallProcess', 'process_select_files_results', [], [], ...
    'subjectname', SubjectName, ...
    'condition',   '', ...
    'includebad',  0);
% Process: Select file names with tag: deviant_trial
sEpochDeviantSrc = bst_process('CallProcess', 'process_select_tag', sFilesAllSrc, [], ...
    'tag',    'deviant_trial', ...
    'search', 1, ...  % Search the file names
    'select', 1);     % Select only the files with the tag
% Process: Select file names with tag: standard_trial
sEpochStandardSrc = bst_process('CallProcess', 'process_select_tag', sFilesAllSrc, [], ...
    'tag',    'standard_trial', ...
    'search', 1, ...  % Search the file names
    'select', 1);     % Select only the files with the tag

% ===== ABSOLUTE DIFFERENCE ======
% Process: Difference: A-B, abs
sDiffSrc = bst_process('CallProcess', 'process_diff_ab', sFilesIntraSrc(1).FileName, sFilesIntraSrc(2).FileName, ...
    'source_abs', 1);
% Process: Set comment: deviant|abs - standard|abs
sDiffSrc = bst_process('CallProcess', 'process_set_comment', sDiffSrc, [], ...
    'tag',     'deviant|abs - standard|abs', ...
    'isindex', 1);
% Process: Low-pass:40Hz
sDiffSrcZscore = bst_process('CallProcess', 'process_bandpass', sDiffSrc, [], ...
    'highpass',    0, ...
    'lowpass',     40, ...
    'attenuation', 'strict', ...  % 60dB
    'mirror',      0, ...
    'overwrite',   1);
% Process: Z-score transformation: [-100ms,-2ms]
sDiffSrcZscore = bst_process('CallProcess', 'process_baseline_norm', sDiffSrcZscore, [], ...
    'baseline',   [-0.1, -0.002], ...
    'source_abs', 0, ...
    'method',     'zscore', ...  % Z-score transformation:    x_std = (x - μ) / σ
    'overwrite',  0);
% Process: Snapshot: Sources (contact sheet)
bst_process('CallProcess', 'process_snapshot', sDiffSrcZscore, [], ...
    'target',         9, ...  % Sources (contact sheet)
    'modality',       1, ...  % MEG (All)
    'orient',         1, ...  % left
    'contact_time',   [0, 0.35], ...
    'contact_nimage', 15, ...
    'threshold',      30, ...
    'Comment',        'Difference deviant - standard (absolute)');

% ===== RELATIVE DIFFERENCE =====
% Process: Difference: A-B
sDiffSrcRel = bst_process('CallProcess', 'process_diff_ab', sFilesIntraSrc(1).FileName, sFilesIntraSrc(2).FileName, ...
    'source_abs', 0);
% Process: Set comment: deviant - standard
sDiffSrcRel = bst_process('CallProcess', 'process_set_comment', sDiffSrcRel, [], ...
    'tag',     'deviant - standard', ...
    'isindex', 1);
% Process: Low-pass:40Hz
sDiffSrcRelZscore = bst_process('CallProcess', 'process_bandpass', sDiffSrcRel, [], ...
    'highpass',    0, ...
    'lowpass',     40, ...
    'attenuation', 'strict', ...  % 60dB
    'mirror',      0, ...
    'overwrite',   1);
% Process: Z-score transformation: [-100ms,-2ms]
sDiffSrcRelZscore = bst_process('CallProcess', 'process_baseline_norm', sDiffSrcRelZscore, [], ...
    'baseline',   [-0.1, -0.002], ...
    'source_abs', 0, ...
    'method',     'zscore', ...  % Z-score transformation:    x_std = (x - μ) / σ
    'overwrite',  0);
% Configure colormap: hot/absolute
bst_colormaps('SetColormapName', 'stat2', 'hot');
bst_colormaps('SetColormapAbsolute', 'stat2', 1);
% Process: Snapshot: Sources (contact sheet)
bst_process('CallProcess', 'process_snapshot', sDiffSrcRelZscore, [], ...
    'target',         9, ...  % Sources (contact sheet)
    'modality',       1, ...  % MEG (All)
    'orient',         1, ...  % left
    'contact_time',   [0, 0.35], ...
    'contact_nimage', 15, ...
    'threshold',      30, ...
    'Comment',        'Difference deviant - standard (relative)');
% Restore colormap: rwb/relative
bst_colormaps('SetColormapName', 'stat2', 'cmap_rbw');
bst_colormaps('SetColormapAbsolute', 'stat2', 0);

% ===== DIFFERENCE OF MEANS =====
% Process: Select uniform number of files  [uniform]
[sEpochDeviantUni, sEpochStandardUni] = bst_process('CallProcess', 'process_select_uniform2', sEpochDeviant, sEpochStandard, ...
    'nfiles', 0, ...
    'method', 4);  % Uniformly distributed
% Process: Difference of means [mean]
sDiffMean = bst_process('CallProcess', 'process_diff_mean', sEpochDeviantUni, sEpochStandardUni, ...
    'avg_func', 1, ...  % Arithmetic average mean(A) - mean(B)
    'weighted', 0);
% Process: Snapshot: Recordings time series
bst_process('CallProcess', 'process_snapshot', sDiffMean, [], ...
    'target',     5, ...  % Recordings time series
    'modality',   1, ...  % MEG (All)
    'Comment',    'Difference of means');



%% ===== TUTORIAL #26: STATISTICS ====================================================
%  ===================================================================================
disp([10 'DEMO> Tutorial #26: Statistics' 10]);
% ===== HISTOGRAMS =====
% Process: Extract values: [160ms] MLP57
sHistoDeviant = bst_process('CallProcess', 'process_extract_values', sEpochDeviant, [], ...
    'timewindow',  [0.16, 0.16], ...
    'sensortypes', 'MLP57', ...
    'isabs',       0, ...
    'avgtime',     0, ...
    'avgrow',      0, ...
    'dim',         2, ...  % Concatenate time (dimension 2)
    'Comment',     '');
% Process: Extract values: [160ms] MLP57
sHistoStandard = bst_process('CallProcess', 'process_extract_values', sEpochStandard, [], ...
    'timewindow',  [0.16, 0.16], ...
    'sensortypes', 'MLP57', ...
    'isabs',       0, ...
    'avgtime',     0, ...
    'avgrow',      0, ...
    'dim',         2, ...  % Concatenate time (dimension 2)
    'Comment',     '');
% Display histograms
hFigHisto = view_histogram({sHistoDeviant.FileName, sHistoStandard.FileName});
bst_report('Snapshot', hFigHisto, sHistoDeviant.FileName, 'Histograms for MLP57/160ms');
close(hFigHisto);

% ===== EXEMPLE #1: PARAMETRIC/DATA =====
% Process: t-test [equal] [-100ms,500ms]          H0:(A-B = 0)
sTestParamData = bst_process('CallProcess', 'process_test_parametric2', sEpochDeviant, sEpochStandard, ...
    'timewindow',    [-0.1, 0.5], ...
    'sensortypes',   '', ...
    'isabs',         0, ...
    'avgtime',       0, ...
    'avgrow',        0, ...
    'Comment',       '', ...
    'test_type',     'ttest_equal', ...  % Student's t-test   (equal variance) t = (mean(A)-mean(B)) / (Sx * sqrt(1/nA + 1/nB))Sx = sqrt(((nA-1)*var(A) + (nB-1)*var(B)) / (nA+nB-2)) df = nA + nB - 2
    'tail',          'two');  % Two-tailed
% Set display properties
StatThreshOptions = bst_get('StatThreshOptions');
StatThreshOptions.pThreshold = 0.05;
StatThreshOptions.Correction = 'fdr';
StatThreshOptions.Control    = [1 2 3];
bst_set('StatThreshOptions', StatThreshOptions);
% Process: Snapshot: Recordings time series
bst_process('CallProcess', 'process_snapshot', sTestParamData, [], ...
    'target',     5, ...  % Recordings time series
    'modality',   1, ...  % MEG (All)
    'time',       0.16, ...
    'Comment',    'Parametric t-test (p<0.05, FDR)');
% Process: Snapshot: Recordings topography (one time)
bst_process('CallProcess', 'process_snapshot', sTestParamData, [], ...
    'target',         6, ...  % Recordings topography (one time)
    'modality',       1, ...  % MEG (All)
    'time',           0.16, ...
    'Comment',        'Parametric t-test (p<0.05, FDR)');

% ===== EXEMPLE #2: NON-PARAMETRIC/DATA =====
% Process: Perm t-test equal [-100ms,500ms MEG]          H0:(A=B), H1:(A<>B)
sTestPermData = bst_process('CallProcess', 'process_test_permutation2', sEpochDeviant, sEpochStandard, ...
    'timewindow',     [-0.1, 0.5], ...
    'sensortypes',    'MEG', ...
    'isabs',          0, ...
    'avgtime',        0, ...
    'avgrow',         0, ...
    'iszerobad',      1, ...
    'Comment',        '', ...
    'test_type',      'ttest_equal', ...  % Student's t-test   (equal variance) t = (mean(A)-mean(B)) / (Sx * sqrt(1/nA + 1/nB))Sx = sqrt(((nA-1)*var(A) + (nB-1)*var(B)) / (nA+nB-2))
    'randomizations', 1000, ...
    'tail',           'two');  % Two-tailed
% Process: Snapshot: Recordings time series
bst_process('CallProcess', 'process_snapshot', sTestPermData, [], ...
    'target',     5, ...  % Recordings time series
    'modality',   1, ...  % MEG (All)
    'time',       0.16, ...
    'Comment',    'Non-parametric t-test (p<0.05, FDR)');
% Process: Snapshot: Recordings topography (one time)
bst_process('CallProcess', 'process_snapshot', sTestPermData, [], ...
    'target',         6, ...  % Recordings topography (one time)
    'modality',       1, ...  % MEG (All)
    'time',           0.16, ...
    'Comment',        'Non-parametric t-test (p<0.05, FDR)');

% ===== EXEMPLE #3: CLUSTER/DATA =====
% Process: FT t-test unequal cluster [-100ms,500ms MEG]          H0:(A=B), H1:(A<>B)
sTestClustData = bst_process('CallProcess', 'process_ft_timelockstatistics', sEpochDeviant, sEpochStandard, ...
    'sensortypes',    'MEG', ...
    'timewindow',     [-0.1, 0.5], ...
    'isabs',          0, ...
    'avgtime',        0, ...
    'avgchan',        0, ...
    'randomizations', 1000, ...
    'statistictype',  1, ...      % Independent t-test
    'tail',           'two', ...  % Two-tailed
    'correctiontype', 2, ...      % cluster
    'minnbchan',      0, ...
    'clusteralpha',   0.05);
% Process: Snapshot: Recordings time series
bst_process('CallProcess', 'process_snapshot', sTestClustData, [], ...
    'target',    5, ...  % Recordings time series
    'time',      0.16, ...
    'modality',  1, ...  % MEG (All)
    'Comment',   'Cluster-based permutation test');
% Process: Snapshot: Recordings topography (one time)
bst_process('CallProcess', 'process_snapshot', sTestClustData, [], ...
    'target',    6, ...  % Recordings topography (one time)
    'modality',  1, ...  % MEG (All)
    'time',      0.16, ...
    'Comment',   'Cluster-based permutation test');

% ===== EXAMPLE #4: PARAMETRIC/SOURCES =====
% Process: t-test [equal] [-100ms,500ms]          H0:(A-B = 0)
sTestParamSrc = bst_process('CallProcess', 'process_test_parametric2', sEpochDeviantSrc, sEpochStandardSrc, ...
    'timewindow',    [-0.1, 0.5], ...
    'scoutsel',      {}, ...
    'scoutfunc',     1, ...  % Mean
    'isnorm',        0, ...
    'avgtime',       0, ...
    'Comment',       '', ...
    'test_type',     'ttest_equal', ...  % Student's t-test   (equal variance) t = (mean(A)-mean(B)) / (Sx * sqrt(1/nA + 1/nB))Sx = sqrt(((nA-1)*var(A) + (nB-1)*var(B)) / (nA+nB-2)) df = nA + nB - 2
    'tail',          'two');  % Two-tailed
% Process: Difference of means [abs(mean)]
sDiffMeanSrc = bst_process('CallProcess', 'process_diff_mean', sEpochDeviantSrc, sEpochStandardSrc, ...
    'avg_func',   2, ...  % Absolute value of average abs(mean(A)) - abs(mean(B))
    'weighted',   0);
% Process: Apply statistic threshold: p<0.05 (FDR:1,2,3)
sDiffMeanSrcThresh = bst_process('CallProcess', 'process_extract_pthresh2', sTestParamSrc, sDiffMeanSrc, ...
    'pthresh',    0.05, ...
    'correction', 3, ...  % False discovery rate (FDR)
    'control1',   1, ...
    'control2',   1, ...
    'control3',   1);
% Process: Snapshot: Sources (one time)
bst_process('CallProcess', 'process_snapshot', sTestParamSrc, [], ...
    'target',    8, ...  % Sources (one time)
    'orient',    1, ...  % left
    'time',      0.148, ...
    'threshold', 40, ...
    'Comment',   'Parametric t-test (p<0.05, FDR)');
% Process: Snapshot: Sources (one time)
bst_process('CallProcess', 'process_snapshot', sDiffMeanSrc, [], ...
    'target',    8, ...  % Sources (one time)
    'orient',    1, ...  % left
    'time',      0.148, ...
    'threshold', 40, ...
    'Comment',   'abs(average(deviant)) - abs(average(standard))');
% Process: Snapshot: Sources (one time)
bst_process('CallProcess', 'process_snapshot', sDiffMeanSrcThresh, [], ...
    'target',    8, ...  % Sources (one time)
    'orient',    1, ...  % left
    'time',      0.148, ...
    'threshold', 0, ...
    'Comment',   'Different of mean thresholded with t-test results');

% ===== EXAMPLE #5: PARAMETRIC/SCOUTS =====
% Process: t-test equal [-100ms,500ms]          H0:(A=B), H1:(A<>B)
sTestParamScout = bst_process('CallProcess', 'process_test_parametric2', sEpochDeviantSrc, sEpochStandardSrc, ...
    'timewindow',    [-0.1, 0.5], ...
    'scoutsel',      {'Destrieux', {'G_front_inf-Opercular L', 'G_precentral L', 'G_temp_sup-G_T_transv L'}}, ...
    'scoutfunc',     1, ...  % Mean
    'isnorm',        0, ...
    'avgtime',       0, ...
    'Comment',       '', ...
    'test_type',     'ttest_equal', ...  % Student's t-test   (equal variance) t = (mean(A)-mean(B)) / (Sx * sqrt(1/nA + 1/nB))Sx = sqrt(((nA-1)*var(A) + (nB-1)*var(B)) / (nA+nB-2)) df = nA + nB - 2
    'tail',          'two');  % Two-tailed
% Process: Apply statistic threshold: p<0.05 (FDR:1,2,3)
sTestParamScoutThresh = bst_process('CallProcess', 'process_extract_pthresh', sTestParamScout, [], ...
    'pthresh',    0.05, ...
    'correction', 3, ...  % False discovery rate (FDR)
    'control1',   1, ...
    'control2',   1, ...
    'control3',   1);
% Process: Compute head model
bst_process('CallProcess', 'process_headmodel', sTestParamScoutThresh, [], ...
    'sourcespace', 1, ...  % Cortex surface
    'meg',         3);     % Overlapping spheres
% Process: Simulate recordings from scouts
sSimulData = bst_process('CallProcess', 'process_simulate_recordings', sTestParamScoutThresh, [], ...
    'scouts',      {'Destrieux', {'G_front_inf-Opercular L', 'G_precentral L', 'G_temp_sup-G_T_transv L'}}, ...
    'savesources', 1);

% Get corresponding source file
[sStudy,iStudy,iRes] = bst_get('ResultsForDataFile', sSimulData.FileName);
sSimulSrc = sStudy.Result(iRes).FileName;
% Reset visualization filters
panel_filter('SetFilters', 0, [], 0, [], 0, [], 0, 0);
% Process: Snapshot: Recordings time series
bst_process('CallProcess', 'process_snapshot', sTestParamScout, [], ...
    'target',    5, ...  % Recordings time series
    'time',      0.148, ...
    'Comment',   'Parametric t-test (p<0.05, FDR)');
% Process: Snapshot: Sources (one time)
bst_process('CallProcess', 'process_snapshot', sSimulSrc, [], ...
    'target',    8, ...  % Sources (one time)
    'orient',    1, ...  % left
    'time',      0.148, ...
    'threshold', 0, ...
    'Comment',   'Simulated sources');
% Process: Snapshot: Recordings time series
bst_process('CallProcess', 'process_snapshot', sSimulData, [], ...
    'target',    5, ...  % Recordings time series
    'modality',  1, ...  % MEG (All)
    'time',      0.148, ...
    'Comment',   'Simulated MEG recordings');


% ===== EXAMPLE #6: NON-PARAMETRIC/TIMEFREQ =====
TfOptions.Output = 'all';
% Process: Time-frequency (Morlet wavelets) / DEVIANT
sEpochDeviantTf = bst_process('CallProcess', 'process_timefreq', sEpochDeviant, [], ...
    'sensortypes', 'MLP57', ...
    'edit',        TfOptions, ...
    'normalize',   'none');  % None: Save non-standardized time-frequency maps
% Process: Time-frequency (Morlet wavelets) / STANDARD
sEpochStandardTf = bst_process('CallProcess', 'process_timefreq', sEpochStandard, [], ...
    'sensortypes', 'MLP57', ...
    'edit',        TfOptions, ...
    'normalize',   'none');  % None: Save non-standardized time-frequency maps
% Process: Perm t-test equal [-100ms,500ms 1-150Hz]          H0:(A=B), H1:(A<>B)
sTestTf = bst_process('CallProcess', 'process_test_permutation2', sEpochDeviantTf, sEpochStandardTf, ...
    'timewindow',     [-0.1, 0.5], ...
    'freqrange',      [1, 150], ...
    'rows',           '', ...
    'isabs',          0, ...
    'avgtime',        0, ...
    'avgrow',         0, ...
    'avgfreq',        0, ...
    'matchrows',      0, ...
    'iszerobad',      1, ...
    'Comment',        '', ...
    'test_type',      'ttest_equal', ...  % Student's t-test   (equal variance) t = (mean(A)-mean(B)) / (Sx * sqrt(1/nA + 1/nB))Sx = sqrt(((nA-1)*var(A) + (nB-1)*var(B)) / (nA+nB-2))
    'randomizations', 1000, ...
    'tail',           'two');  % Two-tailed
% Set stat threshold
StatThreshOptions.pThreshold = 0.05;
StatThreshOptions.Correction = 'none';
bst_set('StatThreshOptions', StatThreshOptions);
% Process: Snapshot: Time-frequency maps
bst_process('CallProcess', 'process_snapshot', sTestTf, [], ...
    'target',   14, ...  % Time-frequency maps
    'Comment',  'Non-parametric t-test (p<0.05, Uncorrected)');
% Process: Delete intermediate results
bst_process('CallProcess', 'process_delete', [sEpochDeviantTf, sEpochStandardTf], [], ...
    'target', 1);  % Delete selected files



%% ===== SAVE REPORT =====
% Save and display report
ReportFile = bst_report('Save', []);
if ~isempty(reports_dir) && ~isempty(ReportFile)
    bst_report('Export', ReportFile, reports_dir);
else
    bst_report('Open', ReportFile);
end

disp([10 'DEMO> Done.' 10]);

For an example of a script illustrating how to create loops, look at the tutorial MEG visual: single subject. brainstorm3/toolbox/script/tutorial_visual_single.m

function tutorial_visual_single(bids_dir, reports_dir)
% TUTORIAL_VISUAL_SINGLE: Runs the Brainstorm/SPM group analysis pipeline (single subject, BIDS version).
%
% ONLINE TUTORIALS: https://neuroimage.usc.edu/brainstorm/Tutorials/VisualSingle
%
% INPUTS:
%    - bids_dir: Path to folder ds000117 (https://openneuro.org/datasets/ds000117)
%       |- derivatives/freesurfer/sub-XX                               : Segmentation folders generated with FreeSurfer
%       |- derivatives/meg_derivatives/sub-XX/ses-meg/meg/*.fif        : MEG+EEG recordings (processed with MaxFilter's SSS)
%       |- derivatives/meg_derivatives/sub-emptyroom/ses-meg/meg/*.fif : Empty room measurements
%    - reports_dir: If defined, exports all the reports as HTML to this folder

% @=============================================================================
% This function is part of the Brainstorm software:
% https://neuroimage.usc.edu/brainstorm
% 
% Copyright (c) University of Southern California & McGill University
% This software is distributed under the terms of the GNU General Public License
% as published by the Free Software Foundation. Further details on the GPLv3
% license can be found at http://www.gnu.org/copyleft/gpl.html.
% 
% FOR RESEARCH PURPOSES ONLY. THE SOFTWARE IS PROVIDED "AS IS," AND THE
% UNIVERSITY OF SOUTHERN CALIFORNIA AND ITS COLLABORATORS DO NOT MAKE ANY
% WARRANTY, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO WARRANTIES OF
% MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE, NOR DO THEY ASSUME ANY
% LIABILITY OR RESPONSIBILITY FOR THE USE OF THIS SOFTWARE.
%
% For more information type "brainstorm license" at command prompt.
% =============================================================================@
%
% Author: Francois Tadel, Elizabeth Bock, 2016-2018


%% ===== SCRIPT VARIABLES =====
% Full list of subjects to process
SubjectNames = {'sub-01', 'sub-02', 'sub-03', 'sub-04', 'sub-05', 'sub-06', 'sub-07', 'sub-08', ...
                'sub-09', 'sub-10', 'sub-11', 'sub-12', 'sub-13', 'sub-14', 'sub-15', 'sub-16'};
% Empty-room dates for each subject (so that we can match automatically recordings with empty-room)
EmptyRoomSubj = 'sub-emptyroom';
AcquisitionDates = {'09-Apr-2009', '06-May-2009', '11-May-2009', '18-May-2009', '15-May-2009', '15-May-2009', '15-May-2009', '15-May-2009', ...
                    '15-May-2009', '15-May-2009', '01-Jun-2009', '01-Jun-2009', '01-Jun-2009', '26-Nov-2009', '08-Dec-2009', '08-Dec-2009'};
% Bad channels {iSubj} = {Run01, Run02, Run03, Run04, Run05, Run06}
BadChannels{1}  = {'EEG016', 'EEG070', 'EEG050',{'EEG008','EEG050'}, [], []};
BadChannels{2}  = {{'EEG027', 'EEG030', 'EEG038'}, 'EEG010', 'EEG010', 'EEG010', 'EEG010', 'EEG010'};
BadChannels{3}  = {{'EEG008','EEG017'}, {'EEG008','EEG017'}, {'EEG008','EEG017'}, {'EEG008','EEG017'}, {'EEG008','EEG017','EEG001'}, {'EEG008','EEG017','EEG020'}};
BadChannels{4}  = {{'EEG038'}, {'EEG038','EEG001','EEG016'}, {'EEG038','EEG001','EEG016'}, {'EEG038','EEG001'}, {'EEG038','EEG001','EEG016'}, {'EEG038','EEG001','EEG016'}};
BadChannels{5}  = {'EEG001', 'EEG001', [], [], [], []};
BadChannels{6}  = {'EEG068', [], 'EEG004', [], [], []};
BadChannels{7}  = {[], [], {'EEG004','EEG008'}, {'EEG004','EEG008'},{'EEG004','EEG008','EEG043','EEG045','EEG047'}, {'EEG004','EEG008'}};
BadChannels{8}  = {[], [], [], [], [], []};
BadChannels{9}  = {[], 'EEG004', 'EEG004', [], 'EEG004', 'EEG004'};
BadChannels{10} = {[], [], [], [], [], []};
BadChannels{11} = {{'EEG010','EEG050'}, 'EEG050', 'EEG050', 'EEG050', 'EEG050', 'EEG050'};
BadChannels{12} = {{'EEG024','EEG057'}, {'EEG024','EEG057'}, {'EEG024','EEG057'}, {'EEG024','EEG057','EEG070'}, {'EEG024','EEG057'}, {'EEG024','EEG057','EEG070'}};
BadChannels{13} = {'EEG009', 'EEG009', {'EEG009','EEG057','EEG69'}, 'EEG009', {'EEG009','EEG044'}, {'EEG009','EEG044'}};
BadChannels{14} = {'EEG029', 'EEG029', 'EEG029', {'EEG004','EEG008','EEG016','EEG029'}, {'EEG004','EEG008','EEG016','EEG029'}, {'EEG004','EEG008','EEG016','EEG029'}};
BadChannels{15} = {'EEG038', 'EEG038', 'EEG038', 'EEG038', {'EEG054','EEG038'}, 'EEG038'};
BadChannels{16} = {'EEG008', 'EEG008', 'EEG008', 'EEG008', 'EEG008', 'EEG008'};
% SSP components to remove {iSubj} = {sRun01, sRun02, sRun03, sRun03, sRun04, sRun05, sRun06},   sRun0X={ECG_GRAD,ECG_MAG}
SspSelect{1}  = {{1,1},         {1,1},   {1,1},   {1,1},   {1,1},   {1,1}}; 
SspSelect{2}  = {{1,1},         {1,1},   {1,1},   {1,1},   {3,1},   {1,1}};
SspSelect{3}  = {{[],1},        {1,1},   {1,1},   {1,1},   {1,1},   {1,1}};
SspSelect{4}  = {{[],1},        {[],1},  {[],1},  {[],1},  {[],1},  {[],1}};
SspSelect{5}  = {{2,1},         {1,1},   {1,1},   {[],1},  {1,1},   {1,1}};
SspSelect{6}  = {{2,1},         {2,1},   {1,1},   {2,1},   {1,1},   {2,1}};
SspSelect{7}  = {{1,1},         {1,1},   {1,1},   {1,1},   {1,1},   {1,1}};
SspSelect{8}  = {{1,1},         {1,1},   {[],1},  {2,1},   {1,1},   {2,1}};
SspSelect{9}  = {{1,1},         {1,1},   {[],1},  {[],1},  {1,1},   {1,1}};
SspSelect{10} = {{1,1},         {1,1},   {1,1},   {1,1},   {1,1},   {1,1}};
SspSelect{11} = {{[],1},        {[],1},  {[],1},  {[],1},  {[],[]}, {[],[]}};
SspSelect{12} = {{[1,2],[1,2]}, {1,1},   {1,1},   {1,1},   {1,1},   {1,1}};
SspSelect{13} = {{[],[]},       {[],[]}, {[],[]}, {[],[]}, {[],[]}, {[],[]}};
SspSelect{14} = {{1,1},         {1,1},   {1,1},   {1,1},   {1,1},   {1,1}};
SspSelect{15} = {{1,1},         {1,1},   {1,1},   {1,1},   {1,1},   {1,1}};
SspSelect{16} = {{1,1},         {1,1},   {1,1},   {2,1},   {1,1},   {1,1}};


%% ===== CREATE PROTOCOL =====
% Start brainstorm without the GUI
if ~brainstorm('status')
    brainstorm nogui
end
% Output folder for reports
if (nargin < 2) || isempty(reports_dir) || ~isdir(reports_dir)
    reports_dir = [];
end
% You have to specify the folder in which the tutorial dataset is unzipped
if (nargin < 1) || isempty(bids_dir) || ~file_exist(bids_dir) || ~file_exist(bst_fullfile(bids_dir, 'derivatives')) || ~file_exist(bst_fullfile(bids_dir, 'dataset_description.json'))
    error('The first argument must be the full path to the tutorial folder.');
end
% The protocol name has to be a valid folder name (no spaces, no weird characters...)
ProtocolName = 'TutorialVisual';
% Delete existing protocol
gui_brainstorm('DeleteProtocol', ProtocolName);
% Create new protocol
gui_brainstorm('CreateProtocol', ProtocolName, 0, 0);
% Set visualization filters: 40Hz low-pass, no high-pass
panel_filter('SetFilters', 1, 40, 0, [], 0, [], 0, 0);
% Set colormap: local color scale
bst_colormaps('SetMaxMode', 'meg', 'local');
bst_colormaps('SetMaxMode', 'eeg', 'local');


%% ===== PRE-PROCESS AND IMPORT =====
for iSubj = 1:16
    % Start a new report (one report per subject)
    bst_report('Start');
    disp(sprintf('\n===== IMPORT: SUBJECT #%d =====\n', iSubj));
    
    % If subject already exists: delete it
    [sSubject, iSubject] = bst_get('Subject', SubjectNames{iSubj});
    if ~isempty(sSubject)
        db_delete_subjects(iSubject);
    end
    
    % ===== FILES TO IMPORT =====
    % Build the path of the files to import
    AnatDir    = fullfile(bids_dir, 'derivatives', 'freesurfer', SubjectNames{iSubj}, 'ses-mri', 'anat');
    DataDir    = fullfile(bids_dir, 'derivatives',  'meg_derivatives', SubjectNames{iSubj}, 'ses-meg', 'meg');
    % Check if the folder contains the required files
    if ~file_exist(AnatDir)
        error(['The folder "' AnatDir '" does not exist.']);
    end
    if ~file_exist(DataDir)
        error(['The folder "' DataDir '" does not exist.']);
    end

    % ===== ANATOMY =====
    % Process: Import anatomy folder
    bst_process('CallProcess', 'process_import_anatomy', [], [], ...
        'subjectname', SubjectNames{iSubj}, ...
        'mrifile',     {AnatDir, 'FreeSurfer'}, ...
        'nvertices',   15000);
    
    % ===== PROCESS EACH RUN =====
    for iRun = 1:6
        % Files to import
        FifFile = bst_fullfile(DataDir, sprintf('%s_ses-meg_task-facerecognition_run-%02d_proc-sss_meg.fif', SubjectNames{iSubj}, iRun));

        % ===== LINK CONTINUOUS FILE =====
        % Process: Create link to raw file
        sFileRaw = bst_process('CallProcess', 'process_import_data_raw', [], [], ...
            'subjectname',    SubjectNames{iSubj}, ...
            'datafile',       {FifFile, 'FIF'}, ...
            'channelreplace', 1, ...
            'channelalign',   0);
        % Set acquisition date
        panel_record('SetAcquisitionDate', sFileRaw.iStudy, AcquisitionDates{iSubj});

        % ===== PREPARE CHANNEL FILE =====
        % Process: Set channels type
        bst_process('CallProcess', 'process_channel_settype', sFileRaw, [], ...
            'sensortypes', 'EEG061, EEG064', ...
            'newtype',     'NOSIG');
        bst_process('CallProcess', 'process_channel_settype', sFileRaw, [], ...
            'sensortypes', 'EEG062', ...
            'newtype',     'EOG');
        bst_process('CallProcess', 'process_channel_settype', sFileRaw, [], ...
            'sensortypes', 'EEG063', ...
            'newtype',     'ECG');

        % Process: Remove head points
        sFileRaw = bst_process('CallProcess', 'process_headpoints_remove', sFileRaw, [], ...
            'zlimit', 0);
        % Process: Refine registration
        sFileRaw = bst_process('CallProcess', 'process_headpoints_refine', sFileRaw, []);
        % Process: Project electrodes on scalp
        sFileRaw = bst_process('CallProcess', 'process_channel_project', sFileRaw, []);

        % Process: Snapshot: Sensors/MRI registration
        bst_process('CallProcess', 'process_snapshot', sFileRaw, [], ...
            'target',   1, ...  % Sensors/MRI registration
            'modality', 1, ...  % MEG (All)
            'orient',   1, ...  % left
            'Comment',  sprintf('MEG/MRI Registration: Subject #%d, Run #%d', iSubj, iRun));
        bst_process('CallProcess', 'process_snapshot', sFileRaw, [], ...
            'target',   1, ...  % Sensors/MRI registration
            'modality', 4, ...  % EEG
            'orient',   1, ...  % left
            'Comment',  sprintf('EEG/MRI Registration: Subject #%d, Run #%d', iSubj, iRun));

        % ===== IMPORT TRIGGERS =====
        % Process: Read from channel
        bst_process('CallProcess', 'process_evt_read', sFileRaw, [], ...
            'stimchan',  'STI101', ...
            'trackmode', 2, ...  % Bit: detect the changes for each bit independently
            'zero',      0);
        % Process: Group by name
        bst_process('CallProcess', 'process_evt_groupname', sFileRaw, [], ...
            'combine', 'Unfamiliar=3,4', ...
            'dt',      0, ...
            'delete',  1);
        % Process: Rename event
        bst_process('CallProcess', 'process_evt_rename', sFileRaw, [], ...
            'src',  '3', ...
            'dest', 'Famous');
        % Process: Rename event
        bst_process('CallProcess', 'process_evt_rename', sFileRaw, [], ...
            'src',  '5', ...
            'dest', 'Scrambled');
        % Process: Add time offset
        bst_process('CallProcess', 'process_evt_timeoffset', sFileRaw, [], ...
            'info',      [], ...
            'eventname', 'Famous, Unfamiliar, Scrambled', ...
            'offset',    0.0345);
        % Process: Delete events
        bst_process('CallProcess', 'process_evt_delete', sFileRaw, [], ...
            'eventname', '1,2,6,7,8,9,10,11,12,13,14,15,16');
        % Process: Detect cHPI activity (Elekta):STI201
        bst_process('CallProcess', 'process_evt_detect_chpi', sFileRaw, [], ...
            'eventname',   'chpi_bad', ...
            'channelname', 'STI201', ...
            'method',      'off');  % Mark as bad when the HPI coils are OFF
        
        % ===== FREQUENCY FILTERS =====
        % Process: Notch filter: 50Hz 100Hz 150Hz 200Hz
        sFileClean = bst_process('CallProcess', 'process_notch', sFileRaw, [], ...
            'freqlist',    [50, 100, 150, 200], ...
            'sensortypes', 'MEG, EEG', ...
            'read_all',    0);
        % Process: Power spectrum density (Welch)
        sFilesPsd = bst_process('CallProcess', 'process_psd', [sFileRaw, sFileClean], [], ...
            'timewindow',  [], ...
            'win_length',  4, ...
            'win_overlap', 50, ...
            'sensortypes', 'MEG, EEG', ...
            'edit', struct(...
                 'Comment',         'Power', ...
                 'TimeBands',       [], ...
                 'Freqs',           [], ...
                 'ClusterFuncTime', 'none', ...
                 'Measure',         'power', ...
                 'Output',          'all', ...
                 'SaveKernel',      0));
        % Process: Snapshot: Frequency spectrum
        bst_process('CallProcess', 'process_snapshot', sFilesPsd, [], ...
            'target',   10, ...  % Frequency spectrum
            'Comment',  sprintf('Power spctrum: Subject #%d, Run #%d', iSubj, iRun));

        % ===== BAD CHANNELS =====
        if ~isempty(BadChannels{iSubj}{iRun})
            % Process: Set bad channels
            bst_process('CallProcess', 'process_channel_setbad', sFileClean, [], ...
                'sensortypes', BadChannels{iSubj}{iRun});
        end

        % ===== EEG REFERENCE =====
        % Process: Re-reference EEG
        bst_process('CallProcess', 'process_eegref', sFileClean, [], ...
            'eegref',      'AVERAGE', ...
            'sensortypes', 'EEG');
    
        % ===== DETECT ARTIFACTS ======
        % Process: Detect heartbeats
        bst_process('CallProcess', 'process_evt_detect_ecg', sFileClean, [], ...
            'channelname', 'EEG063', ...
            'timewindow',  [], ...
            'eventname',   'cardiac');
        % Different amplitude thresholds for different subjects
        if strcmpi(SubjectNames{iSubj}, 'sub-05')
            thresholdMAX = 50;
        else
            thresholdMAX = 100;
        end
        % Process: Detect: blink_BAD    -   Detects all events where the amplitude exceeds 100uV
        bst_process('CallProcess', 'process_evt_detect_threshold', sFileClean, [], ...
            'eventname',    'blink_BAD', ...
            'channelname',  'EEG062', ...
            'timewindow',   [], ...
            'thresholdMAX', thresholdMAX, ...
            'units',        3, ...  % uV (10^-6)
            'bandpass',     [0.3, 20], ...
            'isAbsolute',   1, ...
            'isDCremove',   0);

        % ===== SSP COMPUTATION =====
        % Process: SSP ECG: cardiac
        bst_process('CallProcess', 'process_ssp_ecg', sFileClean, [], ...
            'eventname',   'cardiac', ...
            'sensortypes', 'MEG GRAD', ...
            'usessp',      1, ...
            'select',      SspSelect{iSubj}{iRun}{1}); 
        bst_process('CallProcess', 'process_ssp_ecg', sFileClean, [], ...
            'eventname',   'cardiac', ...
            'sensortypes', 'MEG MAG', ...
            'usessp',      1, ...
            'select',      SspSelect{iSubj}{iRun}{2});
        % Process: Snapshot: SSP projectors
        bst_process('CallProcess', 'process_snapshot', sFileClean, [], ...
            'target',   2, ...
            'Comment',  sprintf('Subject #%d, Run #%d', iSubj, iRun));   % SSP projectors
        
        % ===== IMPORT BAD EVENTS =====
        % Get bad segments: this is typically done manually, not from a script
        BadSegments = GetBadSegments(iSubj, iRun);
        % Process: Import from file
        bst_process('CallProcess', 'process_evt_import', sFileClean, [], ...
            'evtfile', {BadSegments, 'ARRAY-TIMES'}, ...
            'evtname', 'BAD');
        
        % ===== IMPORT TRIALS =====
        % Process: Import MEG/EEG: Events
        sFilesEpochs = bst_process('CallProcess', 'process_import_data_event', sFileClean, [], ...
            'subjectname', SubjectNames{iSubj}, ...
            'condition',   '', ...
            'eventname',   'Famous, Scrambled, Unfamiliar', ...
            'timewindow',  [], ...
            'epochtime',   [-0.5, 1.2], ...
            'createcond',  0, ...
            'ignoreshort', 1, ...
            'usectfcomp',  1, ...
            'usessp',      1, ...
            'freq',        [], ...
            'baseline',    [-0.5, -0.0009]);
        
        % ===== AVERAGE: RUN =====
        % Process: Average: By trial group (folder average)
        sFilesAvg = bst_process('CallProcess', 'process_average', sFilesEpochs, [], ...
            'avgtype',    5, ...  % By trial group (folder average)
            'avg_func',   1, ...  % Arithmetic average:  mean(x)
            'weighted',   0, ...
            'keepevents', 0);
        % Process: Snapshot: Recordings time series
        bst_process('CallProcess', 'process_snapshot', sFilesAvg, [], ...
            'target',   5, ...  % Recordings time series
            'modality', 4, ...  % EEG
            'time',     0.11, ...
            'Comment',  sprintf('Subject #%d, Run #%d', iSubj, iRun));
        % Process: Snapshot: Recordings topography
        bst_process('CallProcess', 'process_snapshot', sFilesAvg, [], ...
            'target',   6, ...  % Recordings topography (one time)
            'modality', 4, ...  % EEG
            'time',     0.11, ...
            'Comment',  sprintf('Subject #%d, Run #%d', iSubj, iRun));

        % ===== COMPUTE NOISECOV: EEG =====
        % Process: Compute covariance (noise or data)
        bst_process('CallProcess', 'process_noisecov', sFilesEpochs, [], ...
            'baseline',       [-0.5, -0.0009], ...
            'sensortypes',    'EEG', ...
            'target',         1, ...  % Noise covariance     (covariance over baseline time window)
            'dcoffset',       1, ...  % Block by block, to avoid effects of slow shifts in data
            'identity',       0, ...
            'copycond',       0, ...
            'copysubj',       0, ...
            'replacefile',    1);  % Replace
    end
    
    % Save report
    ReportFile = bst_report('Save', []);
    if ~isempty(reports_dir) && ~isempty(ReportFile)
        bst_report('Export', ReportFile, bst_fullfile(reports_dir, ['report_' ProtocolName '_' SubjectNames{iSubj} '.html']));
    end
end


%% ===== EMPTY ROOM RECORDINGS =====
disp(sprintf('\n===== IMPORT: EMPTY-ROOM =====\n'));
% Loop on all the noise sessions
NoiseFiles = {};
for ses = {'20090409', '20090506', '20090511', '20090515', '20090518', '20090601', '20091126', '20091208'}
    NoiseFiles{end+1} = fullfile(bids_dir, 'derivatives', 'meg_derivatives', EmptyRoomSubj, ['ses-' ses{1}], 'meg', ['sub-emptyroom_ses-' ses{1} '_task-noise_proc-sss_meg.fif']);
end
% Process: Create link to raw file
sFilesNoise = bst_process('CallProcess', 'process_import_data_raw', [], [], ...
    'subjectname',    EmptyRoomSubj, ...
    'datafile',       {NoiseFiles, 'FIF'}, ...
    'channelreplace', 1, ...
    'channelalign',   0);
% Process: Notch filter: 50Hz 100Hz 150Hz 200Hz
sFileNoiseClean = bst_process('CallProcess', 'process_notch', sFilesNoise, [], ...
        'freqlist',    [50, 100, 150, 200], ...
        'sensortypes', 'MEG, EEG', ...
        'read_all',    0);
% Process: Compute noise covariance
bst_process('CallProcess', 'process_noisecov', sFileNoiseClean, [], ...
    'baseline',    [], ...
    'sensortypes', 'MEG', ...
    'target',      1, ...  % Noise covariance     (covariance over baseline time window)
    'dcoffset',    1, ...  % Block by block, to avoid effects of slow shifts in data
    'identity',    0, ...
    'copycond',    1, ...
    'copysubj',    1, ...
    'copymatch',   1, ...
    'replacefile', 2);  % Merge
       

%% ===== SOURCE ESTIMATION =====
% Start a new report (one report for the source estimation of all the subjects)
bst_report('Start');
% Loop on the subjects: This loop is separated from the previous one, because we should 
% compute the BEM surfaces after importing all the runs, so that the registration is done 
% using the high resolution head surface, instead of the smooth scalp BEM layer.
for iSubj = 1:length(SubjectNames)
    disp(sprintf('\n===== SOURCES: SUBJECT #%d =====\n', iSubj));
    
    % ===== BEM SURFACES =====
    % Process: Generate BEM surfaces
    bst_process('CallProcess', 'process_generate_bem', [], [], ...
        'subjectname', SubjectNames{iSubj}, ...
        'nscalp',      1082, ...
        'nouter',      642, ...
        'ninner',      642, ...
        'thickness',   4, ...
        'method',      'brainstorm');

    % ===== SELECT ALL AVERAGES =====
    % Process: Select data files in: */*
    sFilesAvg = bst_process('CallProcess', 'process_select_files_data', [], [], ...
        'subjectname',   SubjectNames{iSubj});
    % Process: Select file comments with tag: Avg
    sFilesAvg = bst_process('CallProcess', 'process_select_tag', sFilesAvg, [], ...
        'tag',    'Avg');  % Select only the files with the tag

    % ===== COMPUTE HEAD MODELS =====
    % Process: Compute head model (only for the first run of the subject)
    bst_process('CallProcess', 'process_headmodel', sFilesAvg(1), [], ...
        'sourcespace', 1, ...  % Cortex surface
        'meg',         3, ...  % Overlapping spheres
        'eeg',         3, ...  % OpenMEEG BEM
        'ecog',        1, ...  % 
        'seeg',        1, ...  % 
        'openmeeg',    struct(...
             'BemSelect',    [1, 1, 1], ...
             'BemCond',      [1, 0.0125, 1], ...
             'BemNames',     {{'Scalp', 'Skull', 'Brain'}}, ...
             'BemFiles',     {{}}, ...
             'isAdjoint',    0, ...
             'isAdaptative', 1, ...
             'isSplit',      0, ...
             'SplitLength',  4000));
    % Get all the runs for this subject (ie the list of the study indices)
    iStudyOther = setdiff(unique([sFilesAvg.iStudy]), sFilesAvg(1).iStudy);
    % Copy the forward model file to the other runs
    sHeadmodel = bst_get('HeadModelForStudy', sFilesAvg(1).iStudy);
    for iStudy = iStudyOther
        db_add(iStudy, sHeadmodel.FileName);
    end
    
    % ===== COMPUTE SOURCES: MEG =====
    % Process: Compute sources [2018]
    sAvgSrcMeg = bst_process('CallProcess', 'process_inverse_2018', sFilesAvg, [], ...
        'output',  1, ...  % Kernel only: shared
        'inverse', struct(...
             'Comment',        'MN: MEG ALL', ...
             'InverseMethod',  'minnorm', ...
             'InverseMeasure', 'amplitude', ...
             'SourceOrient',   {{'fixed'}}, ...
             'Loose',          0.2, ...
             'UseDepth',       1, ...
             'WeightExp',      0.5, ...
             'WeightLimit',    10, ...
             'NoiseMethod',    'reg', ...
             'NoiseReg',       0.1, ...
             'SnrMethod',      'fixed', ...
             'SnrRms',         1e-06, ...
             'SnrFixed',       3, ...
             'ComputeKernel',  1, ...
             'DataTypes',      {{'MEG GRAD', 'MEG MAG'}}));
    % Process: Snapshot: Sources (one time)   -   Loop only to get a correct comment for the report
    for i = 1:length(sAvgSrcMeg)
        bst_process('CallProcess', 'process_snapshot', sAvgSrcMeg(i), [], ...
            'target',         8, ...  % Sources (one time)
            'orient',         4, ...  % bottom
            'time',           0.11, ...
            'threshold',      20, ...
            'Comment',        ['MEG sources: ' sFilesAvg(i).FileName]);
    end
    
    % ===== COMPUTE SOURCES: EEG =====
    % Process: Compute sources [2018]
    sAvgSrcEeg = bst_process('CallProcess', 'process_inverse_2018', sFilesAvg, [], ...
        'output',  1, ...  % Kernel only: shared
        'inverse', struct(...
             'Comment',        'MN: EEG', ...
             'InverseMethod',  'minnorm', ...
             'InverseMeasure', 'amplitude', ...
             'SourceOrient',   {{'fixed'}}, ...
             'Loose',          0.2, ...
             'UseDepth',       1, ...
             'WeightExp',      0.5, ...
             'WeightLimit',    10, ...
             'NoiseMethod',    'reg', ...
             'NoiseReg',       0.1, ...
             'SnrMethod',      'fixed', ...
             'SnrRms',         1e-06, ...
             'SnrFixed',       3, ...
             'ComputeKernel',  1, ...
             'DataTypes',      {{'EEG'}}));
    % Process: Snapshot: Sources (one time)   -   Loop only to get a correct comment for the report
    for i = 1:length(sAvgSrcEeg)
        bst_process('CallProcess', 'process_snapshot', sAvgSrcEeg(i), [], ...
            'target',         8, ...  % Sources (one time)
            'orient',         4, ...  % bottom
            'time',           0.11, ...
            'threshold',      10, ...
            'Comment',        ['EEG sources: ' sFilesAvg(i).FileName]);
    end
end
% Save report
ReportFile = bst_report('Save', []);
if ~isempty(reports_dir) && ~isempty(ReportFile)
    bst_report('Export', ReportFile, bst_fullfile(reports_dir, ['report_' ProtocolName '_sources.html']));
end


%% ===== TIME-FREQUENCY =====
% Start a new report (one report for the time-frequency of all the subjects)
bst_report('Start');
% List of conditions to process separately
AllConditions = {'Famous', 'Scrambled', 'Unfamiliar'};
% Channels to display in the screen capture, by order of preference (if the first channel is bad, use the following)
SelChannel = {'EEG070','EEG060','EEG065','EEG050','EEG003'};
% Compute one separate time-frequency average for each subject/run/condition
for iSubj = 1:length(SubjectNames)
    disp(sprintf('\n===== TIME-FREQUENCY: SUBJECT #%d =====\n', iSubj));
    for iRun = 1:6
        % Process: Select data files in: Subject/Run
        sTrialsAll = bst_process('CallProcess', 'process_select_files_data', [], [], ...
            'subjectname',   SubjectNames{iSubj}, ...
            'condition',     sprintf('sub-%02d_ses-meg_task-facerecognition_run-%02d_proc-sss_meg_notch', iSubj, iRun));
        % Loop on the conditions
        for iCond = 1:length(AllConditions)
            % Comment describing this average
            strComment = [SubjectNames{iSubj}, ' / ', sprintf('run_%02d', iRun), ' / ', AllConditions{iCond}];
            disp(['BST> ' strComment]);
            % Find the first good channel in the display list
            if isempty(BadChannels{iSubj}{iRun})
                iSel = 1;
            else 
                iSel = find(~ismember(SelChannel,BadChannels{iSubj}{iRun}), 1);
            end
            % Process: Select file comments with tag: Avg
            sTrialsCond = bst_process('CallProcess', 'process_select_tag', sTrialsAll, [], ...
                'tag',    [AllConditions{iCond}, '_trial'], ...
                'search', 1, ...  % Search the file names
                'select', 1);  % Select only the files with the tag
            % Process: Time-frequency (Morlet wavelets), averaged across trials
            sTimefreq = bst_process('CallProcess', 'process_timefreq', sTrialsCond, [], ...
                'sensortypes', 'MEG MAG, EEG', ...
                'edit',        struct(...
                     'Comment',         ['Avg: ' AllConditions{iCond} ', Power, 6-60Hz'], ...
                     'TimeBands',       [], ...
                     'Freqs',           [6, 6.8, 7.6, 8.6, 9.7, 11, 12.4, 14, 15.8, 17.9, 20.2, 22.8, 25.7, 29, 32.7, 37, 41.7, 47.1, 53.2, 60], ...
                     'MorletFc',        1, ...
                     'MorletFwhmTc',    3, ...
                     'ClusterFuncTime', 'none', ...
                     'Measure',         'power', ...
                     'Output',          'average', ...
                     'RemoveEvoked',    0, ...
                     'SaveKernel',      0), ...
                'normalize',   'none');  % None: Save non-standardized time-frequency maps
            % Process: Extract time: [-200ms,900ms]
            sTimefreq = bst_process('CallProcess', 'process_extract_time', sTimefreq, [], ...
                'timewindow', [-0.2, 0.9], ...
                'overwrite',  1);
            % Screen capture of one sensor
            hFigTf = view_timefreq(sTimefreq.FileName, 'SingleSensor', SelChannel{iSel});
            bst_report('Snapshot', hFigTf, strComment, 'Time-frequency', [200, 200, 400, 250]);
            close(hFigTf);
        end
    end
end
% Save report
ReportFile = bst_report('Save', []);
if ~isempty(reports_dir) && ~isempty(ReportFile)
    bst_report('Export', ReportFile, bst_fullfile(reports_dir, ['report_' ProtocolName '_timefreq.html']));
end
end




%% ===== SUPPORT FUNCTIONS =====
function BadSeg = GetBadSegments(iSubj, iRun)
     BadSegments{1} = {...
         [247.867 248.185; 598.999 598.999; 598.999 598.999; 611.999 611.999; 612.999 612.999; 613.999 613.999; 616.999 616.999; 617.999 617.999; 623.999 623.999; 715.209 715.467], ...
         [84.791 85.166], ...
         [79.183 80.167], ...
         [64.309 65.185], ...
         [90.958 91.167; 178.005 178.355; 293.282 295.919; 312.298 316.479; 353.835 357.716], ...
         [60.292 66.802; 69.975 71.210; 105.233 107.586; 108.822 109.506; 376.225 376.325]};
     BadSegments{2} = {...
         [223.806 224.199; 279.772 279.895; 453.241 455.108; 692.423 692.593], ...
         [65.298 66.194; 304.727 306.178; 399.165 400.732], ...
         [203.141 205.085; 281.579 287.883; 420.395 421.128], ...
         [387.118 388.229; 440.318 441.900; 554.825 558.744], ...
         [71.000 80.999; 82.750 87.367; 149.528 149.667; 264.747 267.995; 368.415 371.973; 376.263 378.763; 398.334 401.551; 537.410 541.645], ...
         [38.000 47.999; 47.825 50.046; 61.298 61.384; 249.653 253.379; 282.917 283.820; 286.135 287.616; 298.167 300.196; 328.254 329.511; 335.957 337.817; 478.277 480.707]};
     BadSegments{3} = {...
         [406.312 407.207; 727.055 728.714], ...
         [84.894 85.156; 152.028 152.946; 297.835 298.915; 418.272 421.845; 554.084 554.794], ...
         [73.758 74.159; 378.212 378.536; 406.065 407.099; 470.541 471.698; 488.900 491.168; 529.596 530.453], ...
         [94.874 95.152; 317.385 321.374; 325.696 327.055; 439.220 439.829; 454.473 455.175; 486.196 486.829; 518.660 522.015; 524.400 525.249; 562.417 570.325], ...
         [96.208 97.181; 98.942 99.096; 135.005 135.754; 143.990 144.599; 250.139 250.247; 300.459 300.559; 338.265 339.322; 545.913 546.067], ...
         [91.415 92.156; 284.843 286.525; 297.886 298.404; 317.046 317.163; 332.698 332.791; 358.946 359.402; 428.405 428.775; 478.374 478.690; 549.866 550.128]};
     BadSegments{4} = {...
         [22.967 22.967; 50.036 50.098; 52.058 52.058; 156.653 156.653; 171.565 173.386; 239.544 242.105; 268.162 270.175; 268.992 268.992; 316.032 316.032; 338.283 339.000; 357.959 361.909; 370.871 370.871; 381.579 383.677; 437.731 437.731; 463.482 468.505; 476.135 479.838; 486.652 488.272; 504.860 508.999], ...
         [309.493 311.707; 342.681 344.525; 354.019 357.321; 390.023 391.225; 393.926 395.855; 404.221 405.069; 432.522 435.932; 459.048 460.715; 471.763 478.529; 549.387 551.999; 591.087 594.143; 608.541 611.079; 624.847 626.615; 649.648 651.570], ...
         [57.411 58.198; 88.346 88.955; 200.761 202.335; 227.016 227.688; 257.726 258.054; 356.798 359.005; 404.260 411.003], ...
         [46.000 54.823; 61.000 70.332; 203.005 207.125; 275.875 278.121; 313.500 314.824; 337.973 338.636; 422.505 426.239], ...
         [58.000 62.479; 78.250 85.166; 89.955 91.360; 116.322 117.888; 130.013 131.987; 149.509 150.489; 174.650 175.823; 182.030 183.334; 196.758 197.384; 204.458 204.697; 205.236 208.663; 311.028 316.383; 320.700 327.181; 332.437 335.354; 344.205 346.133; 374.208 374.865; 385.519 386.214; 441.942 444.241; 453.957 456.997; 486.039 487.004; 501.238 504.185; 512.962 514.675; 553.398 556.215], ...
         [41.406 45.743; 58.681 59.144; 108.086 108.896; 140.633 143.750; 196.110 199.474; 210.778 210.971; 234.649 235.143; 258.081 259.632; 339.101 340.805; 390.277 390.609; 438.935 442.122; 528.221 534.031]};
     BadSegments{5} = {...
         [265.539 265.778; 266.334 266.495; 268.479 268.965; 367.428 367.636; 439.655 442.779; 453.497 453.853; 504.997 505.329; 519.513 519.683; 595.674 595.982; 602.000 602.463], ...
         [121.113 121.499; 124.971 126.213; 253.735 254.075; 272.232 272.464; 272.895 273.104; 346.368 346.645; 368.812 369.052; 406.382 406.605; 452.920 453.113; 454.903 455.112; 507.655 507.840; 508.766 509.013; 584.853 585.030; 594.831 595.656; 597.261 602.249], ...
         [37.251 37.497; 38.825 39.056; 40.615 41.849; 43.624 44.758; 53.333 53.641; 54.698 55.076; 57.668 59.196; 79.129 79.360; 81.475 81.714; 122.658 123.375; 284.787 285.296; 288.754 288.993; 345.790 346.022; 421.212 421.459; 481.428 482.207; 503.408 503.685; 504.272 504.449; 524.451 524.714; 526.913 531.499], ...
         [87.322 88.178; 91.085 91.325; 95.121 95.491; 114.174 114.397; 129.874 130.113; 151.220 151.544; 281.689 281.959; 532.966 533.345], ...
         [59.176 60.218; 74.854 75.317; 308.180 309.877; 380.705 381.059], ...
         [182.382 183.245; 196.220 196.736; 276.018 276.327; 292.490 294.086; 370.755 370.847; 435.644 436.624; 467.535 468.460; 522.838 525.847]};
     BadSegments{6} = {...
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         [62.300 63.148; 266.254 266.639; 409.920 410.221], ...
         [54.048 55.214; 330.893 331.255], ...
         [185.616 186.495; 331.411 331.796; 386.843 387.028; 387.999 387.999; 389.999 389.999; 434.575 434.875; 519.802 519.995], ...
         [44.720 45.167; 211.446 211.964; 368.955 369.172]};
     BadSegments{7} = {...
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         [88.774 89.167; 107.999 107.999; 109.999 109.999; 110.999 110.999; 112.999 112.999; 113.999 113.999; 114.999 114.999; 119.999 119.999; 121.999 121.999; 124.223 125.465; 137.011 138.871], ...
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         [135.997 137.232; 383.565 383.657; 418.763 418.955]};
     BadSegments{9} = {...
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     BadSegments{10} = {...
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         [41.266 41.675; 53.905 55.240; 159.159 159.345; 220.255 220.394], ...
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         [105.369 106.172; 141.468 142.178; 199.008 199.818; 201.269 201.716; 352.851 353.083; 449.865 452.041; 459.508 459.802], ...
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     BadSegments{11} = {...
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         [65.331 66.156], ...
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     BadSegments{12} = {...
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         [66.900 72.232; 75.510 78.688; 543.495 545.999]};
     BadSegments{13} = {...
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         [171.506 172.301; 172.999 172.999; 430.999 430.999; 432.999 432.999; 434.999 434.999; 448.999 448.999; 479.999 479.999; 489.999 489.999; 490.999 490.999; 491.999 491.999], ...
         [92.059 93.209], ...
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         [87.308 87.639; 107.039 108.189; 427.943 428.035; 437.965 438.027; 439.973 440.026; 491.275 492.571]};
     BadSegments{14} = {...
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         [71.132 72.227], ...
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         [53.557 54.175; 148.851 149.136], ...
         [43.681 44.214; 361.255 361.587; 409.462 411.021], ...
         [90.868 91.130; 410.935 411.044], ...
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     BadSegments{16} = {...
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         [49.726 50.251; 58.675 65.465; 263.918 264.088; 321.795 322.195; 522.881 523.081], ...
         [61.245 62.325; 65.628 65.905; 315.686 317.275; 335.184 336.449; 387.870 388.117; 389.892 390.062], ...
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         [71.665 72.513; 76.347 77.181; 127.134 128.862], ...
         [46.190 47.209; 53.461 55.752; 194.317 194.510; 213.101 213.240; 216.356 216.442; 225.962 226.286; 245.163 245.464; 252.041 253.422; 254.648 254.856; 292.966 294.340; 340.928 341.067; 346.298 346.452]};
    BadSeg = BadSegments{iSubj}{iRun}';
end


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 figures, 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.

-  ⇤ ← Revision 2 as of 2015-02-03 22:22:50 → 
  Size: 6406
  Editor: FrancoisTadel
  Comment:
+   ← Revision 48 as of 2016-07-21 21:22:52 → ⇥
  Size: 25129
  Editor: FrancoisTadel
  Comment:
-Deletions are marked like this.
+Additions are marked like this.
 Line 1:
-= Tutorial 20: Advanced scripting =
+= Tutorial 28: Scripting =
'''[TUTORIAL UNDER DEVELOPMENT: NOT READY FOR PUBLIC USE] '''
-Line 4:
+Line 6:
+The previous tutorials explained how to use Brainstorm in an interactive way to process one subject with two acquisition runs. In the context of a typical neuroimaging study, you may have tens or hundreds of subjects to process in the same way, it is unrealistic to do everything manually. Some parts of the analysis can be processed in batches with no direct supervision, others require more attention. This tutorial introduces tools and tricks that will help you assemble an efficient analysis pipeline.

Requirements: You need a license for the Matlab environment in order to use these tools and execute custom scripts. If you are running the compiled version of Brainstorm with the MCR library, the only custom code you can run is through the menu File > Matlab console and the process "Run Matlab command".
-Line 6:
+Line 12:
-= From CTF =
The main window includes a graphical batching  interface that directly benefits from the database explorer: files are  organized as a tree of subjects and conditions, and simple drag-and-drop  operations readily select files for subsequent batch processing. Most  of the Brainstorm features are available through this interface,  including pre-processing of the recordings, averaging, time-frequency  decompositions, and computing statistics. A full analysis pipeline can  be created in a few minutes, saved in the user’s preferences and  reloaded in one click, executed directly or exported as a Matlab script.

The available processes are organized in a  plug-in structure. Any Matlab   script that is added to the plug-in  folder  (brainstorm3/toolbox/process/functions/) and has the right  format  will  be automatically detected and made available in the GUI.  This  mechanism  makes the contribution from other developers to  Brainstorm  very easy.

== Creating a pipeline ==

=== List of processes ===
 1. Click  on Run. The Process selection window appears, with which you can create  an analysis pipeline (ie. a list of process that are applied on the  selected files one after the other). The first button in the toolbar  shows the list of processed that are currently available. If you click  on a menu, it's added to the list.

  {{http://neuroimage.usc.edu/brainstorm/Tutorials/TutProcesses?action=AttachFile&do=get&target=pipeline1.gif|pipeline1.gif|class="attachment"}}
 1. Some  menus appear in grey (example: Sources > Spatial smoothing). This  means that they are not meant to be applied to the type of data that you  have in input, or at the end of the current pipeline. The "spatial  smoothing" process may only be run on source files.
 1. When you select a process, a list of options specific to this process is shown in the window.
  * To delete a process: Select it and press the ''Delete'' key, or the big cross in the toolbar.
  * With the "up arrow" and "down arrow" buttons in the toolbar, you can move up/down a process in the pipeline.
 1. Now add the following processes, and set their options:
  * '''Pre-process > Band-pass filter''': 2Hz - 30Hz
   * In  some processes, you can specify the type(s) of sensors on which you   want to apply the process. This way you can for instance apply different   filters on the EEG and the MEG, if you have both in the same files.
  * '''Extract > Extract time''': 40.00ms - 49.60ms, overwrite initial file
   * This will extract from each file a small time window around the main response peak.
   * Selecting  the overwrite option will replace the previous file (bandpass), with  the output of this process (bandpass+extract). This option is usually  unselected for the first process in the list, then selected  automatically.
  * '''Average > Average over time''': Overwrite initial file
   * Compute the average over this small time window.

  {{http://neuroimage.usc.edu/brainstorm/Tutorials/TutProcesses?action=AttachFile&do=get&target=pipeline2.gif|pipeline2.gif|class="attachment"}}
 1. Save your pipeline: Click on the last button in the toolbar > Save > New... > Type "process_avg45".

=== Saving/exporting a pipeline ===
The last button in the the toolbar offers a list of menus to save, load and export the pipelines.

 . {{http://neuroimage.usc.edu/brainstorm/Tutorials/TutProcesses?action=AttachFile&do=get&target=pipeline3.gif|pipeline3.gif|class="attachment"}}

 * '''Load''': List of processes that are saved in the user preferences
 * '''Load from file''': Import a pipeline from a pipeline_...mat file (previously saved with the menu "Save as Matlab matrix")
 * '''Save''': Save the pipeline in the user preferences, to be able to access it really fast after
 * '''Save as Matlab matrix''':  Exports the pipeline for a Matlab structure in a .mat file. Allows  different users to exchange their analysis pipelines (or a single user  between different computers).
 * '''Generate .m script''': This option generates a human-readable Matlab script that can be re-used for other purposes or modified.
 * '''Delete''': Remove a pipeline that is saved in the user preferences.
 * '''Reset options''':  Brainstorm saves automatically for each user the options of all the  processes. This menu removes all the saved options, and set them back to  the default values.

Here is the Matlab script that is generated automatically for this pipeline.

 . {{http://neuroimage.usc.edu/brainstorm/Tutorials/TutProcesses?action=AttachFile&do=get&target=script.gif|script.gif|class="attachment"}}
 .

Click  on Ok, in the pipeline window. After a few seconds, you will see  two  new files in the database, and the "Report  viewer" window.

=== Report viewer ===
Each time  the pipeline editor is used to executed to run a list of processes, a  report is generated and saved in the user home folder  (/home/username/reports/). The report viewer shows as an HTML page some  of the information saved in this report structure: the date and duration  of execution, the list of processes, the input and output files. It  reports all the warning and errors that happen during the execution.

The  report viewer does not necessarily appear automatically at the end of  the last process: it is shown only when more than one processes were  executed, or when any of the processes returned an error or a warning.

When  running processes manually from a script, the calls bst_report(Start,  Save, Open) explicitly indicate when the logging of the events should  start and stop.

You can add images to the reports for quality control using the process "File > Save snapshot".

{{http://neuroimage.usc.edu/brainstorm/Tutorials/TutProcesses?action=AttachFile&do=get&target=report.gif|output.gif|class="attachment"}}

After you close the report window, you can re-open the last report with the main menu of the Brainstorm window: '''File > Report viewer'''.

With the buttons in the toolbar, you can go back to the previous reports saved from the same protocol.

<<EmbedContent("http://neuroimage.usc.edu/bst/get_prevnext.php?prev=Tutorials/Connectivity")>>
+== Starting a new script ==
The easiest way to get started with a new Brainstorm script is to use the script generator, already introduced in the tutorial [[http://neuroimage.usc.edu/brainstorm/Tutorials/PipelineEditor#Saving_a_pipeline|Select files and run processes]]. Select some files in the Process1 or Process2 tabs, select a list of processes, and use the menu '''Generate .m script'''. The example below should work on the the protocol "TutorialIntroduction" created during the introduction tutorials.

 * In the Process1 tab, leave the selection box empty and click on [Run]. Instead of selecting the files from the Brainstorm interface, we will select them directly from the database using a script.
 * Select process '''File > Select files: Recordings''' (do not execute immediately)<<BR>>Subject='''Subject01''', Condition='''[Empty]''', File comment='''Avg: deviant''' (the space is important). <<BR>><<BR>> {{attachment:start1.gif||height="334",width="400"}}
 * This process selects all the recordings with a comment including the string "Avg: deviant", from all the folders in Subject01 (except "Intra-subject" and "Common files"). We expect to get two files: the averages of the deviant condition for both runs.
 * Add process '''Pre-process > Band-pass filter''': Lower cutoff='''0Hz''', Upper cutoff='''30Hz''', Mirror. <<BR>> '''Add process File > Save snapshot''': Recordings time series, Sensors=MEG. <<BR>> <<BR>> {{attachment:start2.gif||height="294",width="518"}}
 * This will apply a low-pass filter at 30Hz and save a screen capture of the signals in the report.
 * Do not run the pipeline, select the menu '''Generate .m script '''instead. It saves a new .m file and opens it in the Matlab editor. Close the pipeline editor window and look at the script.<<BR>><<BR>> {{attachment:start3.gif||height="272",width="673"}}
 * The script you just generated can be the starting point to your own  custom script. The following sections explain line by line how they work  and how to edit them.

== Line by line: Header ==
{{{
% Script generated by Brainstorm (19-Jul-2016)
}}}
All the lines starting with a "%" are comments, they are never executed.

{{{
% Input files
sFiles = [];
SubjectNames = {...
    'Subject01'};
}}}
These lines define the script inputs:

 * '''sFiles''':  The list of files in input. Currently empty because we did not select  anything in input in the Process1 list. If we had selected files, it  would contain a cell array of strings with relative file paths.
 * '''SubjectNames''': List of subject names that are used in the script. Most of the times, the generated script would contain only one entry, but it written as a cell array so that it is easier to extend it to multiple subjects with a loop (described further in this tutorial).

{{{
% Start a new report
bst_report('Start', sFiles);
}}}
Start a new report of activity: Clears all the previous logs and gets ready to record new messages. The report will collect all the messages that are generated during the execution of the script by the various processes. You can explicitely add screen captures and additional messages to the current report with the function bst_report. This report will remain open until the function bst_report('Start') is called again. To display the current report, use the menu [[http://neuroimage.usc.edu/brainstorm/Tutorials/PipelineEditor#Report_viewer|File > Report viewer]].

The syntax '''function_name'''(''''SubFunction'''', arguments) is used a lot in Brainstorm, to call a subfunction available inside a .m file. This line above calls the function Report() in the file brainstorm3/toolbox/process/bst_report.m. This is made possible with the use of the short script "macro_methodcall" you will see at the top of many files. Many of the Brainstorm .m files are actually libraries of functions, rather than simple "scripts" or "functions".

== Line by line: Body ==
{{{
% Process: Select data files in: Subject01/*/Avg: deviant
sFiles = bst_process('CallProcess', 'process_select_files_data', sFiles, [], ...
    'subjectname',   SubjectNames{1}, ...
    'condition',     '', ...
    'tag',           'Avg: deviant', ...
    'includebad',    0, ...
    'includeintra',  0, ...
    'includecommon', 0);

% Process: Low-pass:30Hz
sFiles = bst_process('CallProcess', 'process_bandpass', sFiles, [], ...
    'highpass',    0, ...
    'lowpass',     30, ...
    'mirror',      1, ...
    'sensortypes', 'MEG', ...
    'overwrite',   0);

% Process: Snapshot: Recordings time series
sFiles = bst_process('CallProcess', 'process_snapshot', sFiles, [], ...
    'target',         5, ...  % Recordings time series
    'modality',       1, ...  % MEG (All)
    'orient',         4, ...  % bottom
    'time',           0.11, ...
    'contact_time',   [0, 0.1], ...
    'contact_nimage', 12, ...
    'threshold',      20, ...
    'Comment',        'Run');
}}}
You will find one block per process you selected in the pipeline editor. They all have the same syntax: <<BR>> output_files = '''bst_process'''('CallProcess', process_name, input_files_A, input_files_B, options_list);

 * '''process_name''':  String indicating the function corresponding to the process to execute.  To know from the pipeline editor what is the path to the process  function: hover your mouse over the selected process, as illustrated in [[http://neuroimage.usc.edu/brainstorm/Tutorials/PipelineEditor#Saving_a_pipeline|this tutorial]].
 * '''input_files_A''':  List of input files in Process1, or FilesA in Process2. It can be a  cell array of files names (full path, or relative path from the protocol  folder), or an array of structures describing the files in the database  (returned by a previous call to bst_process).
 * '''input_files_B''': Empty for Process1, or FilesB in Process2. Cell array of strings or array of struct.
 * '''options_list''': Pairs of (option_name, option_values), one for each option of the process.
 * '''output_files''':  Array of structures describing the files in output of the process. If  the process created new files, this variable contains the new files. If  the process didn't create new files or was modifying exiting files, most  of the time this variable would contain the same files as the input  list.

== Line by line: Footer ==
{{{
% Save and display report
ReportFile = bst_report('Save', sFiles);
}}}
Closes the current report and saves it in the user Brainstorm report folder ($HOME/.brainstorm/reports). These reports are in .mat format and contain all the information necessary to re-run the execution exactly in the same way, but they not easy to read.

The parameter "sFiles" is optional, it indicates what are the files that are considered as the final results of the script. You can remove it without breaking your script: ReportFile = bst_report('Save');

{{{
bst_report('Open', ReportFile);
}}}
Opens the report viewer to display what happened during the execution. This is equivalent to using the menu [[http://neuroimage.usc.edu/brainstorm/Tutorials/PipelineEditor#Report_viewer|File > Report viewer]]. You can comment this line (ie. add a "%" at the beginning of the line) if you don't want to show the report at the end of the execution.

{{{
% bst_report('Export', ReportFile, ExportDir);
}}}
This function exports the report in readable format, as an HTML that includes all the screen captures embedded in it. It is disabled by default. If you want to use this feature: remove the "%" at the beginning of the line, and define the variable ExportDir.

ExportDir must be a string that defines where to save the HTML report. It can be wither the absolute path to a HTML file (eg. 'C:\Users\myuser\Documents\report_example.html') or just a folder (eg. 'C:\Users\myuser\Documents'). If you enter only a path to a folder, a default file name including the protocol name and a date tag is generated (report_ProtocolName_YYMMDD_HHMMSS.html).

== Simplify the calls ==
This script you generated is like any Matlab script: you can edit it, rename the variables, add tests and loops, etc. The first important thing to understand is how to edit the options or change the inputs/outputs of a process. The script generator uses only one variable for all the file lists ('''sFiles''') and the output process is always the input of the following process. This is usually too restrictive to write a full analysis script: we commonly need to have multiple lists of files or to run two different operations on the same file.

Let's consider the first process call, which  selects the averages for the Deviant condition in both runs.

{{{
sFiles = bst_process('CallProcess', 'process_select_files_data', sFiles, [], ...
    'subjectname',   SubjectNames{1}, ...
    'condition',     '', ...
    'tag',           'Avg: deviant', ...
    'includebad',    0, ...
    'includeintra',  0, ...
    'includecommon', 0);
}}}
There is no need to set the parameter sFiles, because there is no input, you can replace it with an empty matrix []. You can therefore remove the line "sFiles = [];". We can also rename the output variable "sAvgData", to be a bit more specific.

{{{
sAvgData = bst_process('CallProcess', 'process_select_files_data', [], [], ...
}}}
You can omit all the options that are not defined, not used, or kept to their default values:

{{{
sAvgData = bst_process('CallProcess', 'process_select_files_data', [], [], ...
    'subjectname',   SubjectNames{1}, ...
    'tag',           'Avg: deviant');
}}}
Edit the call to the low-pass filter: Change the input to sAvgData and the output to sAvgDataLow, this way we will be able to keep track of the two files if we need to use them independently later in the script.

{{{
sAvgDataLow = bst_process('CallProcess', 'process_bandpass', sAvgData, [], ...
    'highpass',    0, ...
    'lowpass',     30, ...
    'sensortypes', 'MEG');
}}}
Edit the call to the snapshot process: Change the input to sAvgDataLow, and remove the output parameter (we are not expecting output from it).

{{{
bst_process('CallProcess', 'process_snapshot', sAvgDataLow, [], ...
    'target',   5, ...  % Recordings time series
    'modality', 1);     % MEG (All)
}}}
Replace the last lines with the following, to export  the report instead of opening in the report viewer (edit the file path  to point at your own user folder instead).

{{{
ReportFile = bst_report('Save');
bst_report('Export', ReportFile, 'C:\Users\franc\Documents\report_test.html');
}}}
== Interaction with Matlab ==
Select the code for the first process in the Matlab editor, right-click > Evaluate selection (or press F9).

{{attachment:edit1.gif}}

If you haven't executed your script yet, you will get the following error in the Matlab command window:

{{{
Undefined variable "SubjectNames" or class "SubjectNames".
}}}
The variable SubjectNames is not defined yet: Execute the first two lines "SubjectNames = {'Subject01'}", then try again. You should now have a new variable in your Matlab workspace, which points at the two average files. Type "sAvgData(1)" in your command window to display the first element:

{{{
>> sAvgData(1)
ans =
          iStudy: 6
           iItem: 1
        FileName: 'Subject01/S01_AEF_20131218_01_600Hz_notch/data_deviant_average_160513_1329.mat'
        FileType: 'data'
         Comment: 'Avg: deviant (39 files)'
       Condition: 'S01_AEF_20131218_01_600Hz_notch'
     SubjectFile: 'Subject01/brainstormsubject.mat'
     SubjectName: 'Subject01'
        DataFile: ''
     ChannelFile: 'Subject01/S01_AEF_20131218_01_600Hz_notch/channel_ctf_acc1.mat'
    ChannelTypes: {'ADC A'  'ADC V'  'DAC'  'ECG'  'EOG'  'FitErr'  'HLU'  'MEG'  'MEG REF' ...}
}}}
The field "sAvgData(1).FileName" contains the relative path to the to the Deviant average in the first run. This structure sAvgData contains also a lot of information that can be helpful in your script:

 * '''iStudy / iItem''': Reference of the file in the database (described later in this tutorial).
 * '''FileType''': 'raw' (continuous files), 'data' (recordings), 'results' (sources), 'timefreq' (time-frequency, spectrum and connectivity), or 'matrix' (any time series extracted from other files).
 * '''Comment''': Comment field of the file (what is displayed in the database explorer)
 * '''Condition''': Name of the condition/folder in which the file is located
 * '''SubjectFile''': Relative path to the subject file (brainstormsubject.mat)
 * '''SubjectName''': Name of the subject
 * '''DataFile''': For types 'results' or 'timefreq', path of the parent file in the database explorer
 * '''ChannelFile''': Relative path to the channel file
 * '''ChannelTypes''': Cell array of channel types available for the input file

== Naming conventions ==
To help you navigate in the Brainstorm code, here are some naming conventions:

 * '''Structures''': Name starting with a "s" followed by a capital letter (eg. sFiles).
 * '''Indices''': Either loop variables or array indices, name starting with a "i" (eg. iSubject, iStudy, iTime).
 * '''Counts''': Number of elements in a group, name starting with a "n" (eg. nAvg, nTrials, nSubjects).
 * '''File names''': Scripts and functions, only lower case, separation with "_" (eg. process_average)
 * '''Sub-functions''': Inside a .m file, name starting with a capital, camel-case (eg. CallProcess, Start).

== Running the script ==
The simplified script looks like this:

{{{
% Input files
SubjectNames = {'Subject01'};
% Start a new report
bst_report('Start');

% Process: Select data files in: Subject01/*/Avg: Deviant
sAvgData = bst_process('CallProcess', 'process_select_files_data', [], [], ...
    'subjectname',   SubjectNames{1}, ...
    'tag',           'Avg: deviant');
% Process: Low-pass:30Hz
sAvgDataLow = bst_process('CallProcess', 'process_bandpass', sAvgData, [], ...
    'highpass',    0, ...
    'lowpass',     30, ...
    'sensortypes', 'MEG');
% Process: Snapshot: Recordings time series
bst_process('CallProcess', 'process_snapshot', sAvgDataLow, [], ...
    'target',   5, ...  % Recordings time series
    'modality', 1);     % MEG (All)

% Save and display report
ReportFile = bst_report('Save');
bst_report('Export', ReportFile, 'C:\Users\franc\Documents\report_test.html');
}}}
You have three ways to execute it:

 * Select all the lines (Ctrl+A) and evaluate it in Matlab (F9).
 * In the Editor toolbar of the Matlab environment, click on the button [Run].
 * Save the file, go to this folder with Matlab (or add it to your path) and type the name of the script in the command window (without the ".m" at the end).

At the end of the execution, nothing happens, because we indicated we wanted to just export the report instead of opening it. To check out the report of execution: use the menu '''File > Report viewer''' from the Brainstorm window, or open the '''report_test.html''' that was saved somewhere on your computer.

{{attachment:report1.gif}}

== Starting Brainstorm ==
 . - gui / nogui - selecting protocol - delete existing protocol

== Selecting files ==
- Inputs / outputs

- Select processes

- Adding tags to help with the file selection later

== Database requests ==
bst_get / bst_set

 * '''iStudy''': Index of the study, get the structure with sStudy=bst_get('Study', sInput.iStudy)
 * '''iItem''': Index of file in its category (example for recordings: sStudy.Data(sInput.iItem))

sStudy = bst_get('Study', sAvgData(1).iStudy)

sStudy.Data(sAvgData(1).iItem)

== File manipulation ==
 * Modify a structure manually: Export to Matlab/Import from Matlab
 * File manipulation: file_short, file_fullpath, in_bst_*...
 * Documentation of all file structures: point at the appropriate tutorials
 * Select files from the database (with bst_get and processes)

== Loop over subject and runs ==
Creating loops is not supported yet by the script generator, but relatively easy to do from a script without having to know too much about Matlab programming.

1) Fill the cell array SubjectNames with all your subjects names, with  the same dimensions as the list of input raw files (sFiles)

 . 2) Add a "for" loop that includes all the bst_process() calls (leave the bst_report() calls and input definition outside)
 3) Inside the loop, replace SubjectNames with SubjectNames{i} and sFiles with sFiles(i)

== How to process many subjects ==
This section proposes a standard workflow for processing a full group study with Brainstorm. It contains the same steps of analysis as the introduction tutorials, but separating what can be done automatically from what should be done manually. This workflow can be adapted to most ERP studies (stimulus-based).

 * '''Prototype''': Start by processing one or two subjects completely '''interactively''' (exactly like in the introduction tutorials). Use the few pilot subjects that you have for your study to prototype the analysis pipeline and check manually all the intermediate stages. Take notes of what you're doing along the way, so that you can later write a script that reproduces the same operations.
 * '''Anatomical fiducials''': Set NAS/LPA/RPA and compute the MNI transformation for each subject.
  * '''Segmentation''': Run FreeSurfer/BrainSuite to get surfaces and atlases for all the subjects.
  * '''File > Batch MRI fiducials''': This menu prompts for the selection of the fiducials for all the subjects and saves a file __fiducials.m__ in each segmentation folder. You will not have to redo this even if you have to start over your analysis from the beginning.
  * '''Script''': Write a loop that calls the process "Import anatomy folder" for all the subjects.
  * '''Alternatives''': Create and import the subjects one by one and set the fiducials at the import time. Or use the default anatomy for all the subjects (or use  [[Tutorials/TutWarping|warped templates]]).

 * '''Script #1''': Pre-processing: Loop on the subjects and the acquisition runs.
  * '''Create link to raw files''': Link the subject and noise recordings to the database.
  * '''Event markers''': Read and group triggers from digital and analog channel, fix stimulation delays
  * '''Evaluation''': Power spectrum density of the recordings to evaluate their quality.
  * '''Pre-processing''': Notch filter, sinusoid removal, band-pass filter.
  * '''Evaluation''': Power spectrum density of the recordings to make sure the filters worked well.
  * '''Cleanup''': Delete the links to the original files (the filtered ones are copied in the database).
  * '''Detect artifacts''': Detect heartbeats, Detect eye blinks, Remove simultaneous.
  * '''Compute SSP''': Heartbeats, Blinks (this selects the first component of each decomposition)
  * '''Compute ICA''': If you have some artifacts you'd like to remove with ICA (no default selection).
  * '''Screenshots''': Check the MRI/sensors registration, PSD before and after corrections, SSP.
  * '''Export the report''': One report per subject, or one report for all the subjects, saved in HTML.
 * '''Manual inspection #1''':
  * '''Check the reports''': Information messages (number of events, errors and warnings) and screen captures (registration problems, obvious noisy channels, incorrect SSP topographies).
  * '''Mark bad channels''': Open the recordings, select the channels and mark them as bad. Or use the process "Set bad channels" to mark the same bad channels in multiple files.
  * '''Fix the SSP/ICA''': For the suspicious runs: Open the file, adjust the list of blink and cardiac events, remove and recompute the SSP decompositions, manually select the components.
  * '''Detect other artifacts''': Run the process on all the runs of all the subjects at once (select all the files in Process1 and run the process, or generate the equivalent script).
  * '''Mark bad segments''': Review the artifacts detected in 1-7Hz and 40-240Hz, keep only the ones you really want to remove, then mark the event categories as bad. Review quickly the rest of the file and check that there are no other important artifacts.
  * '''Additional SSP''': If you find one type of artifact that repeats (typically saccades and SQUID jumps), you can create additional SSP projectors, either with the process "SSP: Generic" or directly from a topography figure (right-click on the figure > Snapshot> Use as SSP projector).
 * '''Script #2''': Subject-level analysis: Epoching, averaging, sources, time-frequency.
  * '''Importing''': Process "Import MEG/EEG: Events" and "Pre-process > Remove DC offset".
  * '''Averaging''': Average trials by run, average runs by subject (registration problem in MEG).
  * '''Noise covariance''': Compute from empty room or resting recordings, copy to other folders.
  * '''Head model''': Compute for each run, or compute once and copy if the runs are co-registered.
  * '''Sources''': Compute for each run, average across runs and subjects in source space for MEG.
  * '''Time-frequency''': Computation with Hilbert transform or Morlet wavelets, then normalize.
  * '''Screenshots''': Check the quality of all the averages (time series, topographies, sources).
  * '''Export the report''': One report per subject, or one report for all the subjects, saved in HTML.
 * '''Manual inspection #2''':
  * '''Check the reports''': Check the number of epochs imported and averaged in each condition, check the screen capture of the averages (all the primary responses should be clearly visible).
  * '''Regions of interest''': If not using predefined regions from an atlas, define the scouts on the anatomy of each subject (or on the template and then project them to the subjects).
 * '''Script #3''': Group analysis, ROI-based analysis, etc.
  * '''Averaging''': Group averages for the sensor data, the sources and the time-frequency maps.
  * '''Statistics''': Contrast between conditions or groups of subjects.
  * '''Regions of interest''': Any operation that involve scouts.

== Final script ==
The following script from the Brainstorm distribution reproduces the introduction tutorials ("Get started"): '''brainstorm3/toolbox/script/tutorial_introduction.m'''

<<HTML(<div  style="border:1px solid black;  background-color:#EEEEFF; width:720px;  height:500px; overflow:scroll;  padding:10px; font-family:  Consolas,Menlo,Monaco,Lucida  Console,Liberation Mono,DejaVu Sans  Mono,Bitstream Vera Sans  Mono,Courier New,monospace,sans-serif;  font-size: 13px; white-space: pre;">)>><<EmbedContent("http://neuroimage.usc.edu/bst/viewcode.php?f=tutorial_introduction.m")>><<HTML(</div >)>>

<<BR>>For an example of a script illustrating how to create loops, look at the tutorial [[Tutorials/VisualSingle|MEG visual: single subject]]. '''brainstorm3/toolbox/script/tutorial_visual_single.m'''

<<HTML(<div  style="border:1px solid black;  background-color:#EEEEFF; width:720px;  height:500px; overflow:scroll;  padding:10px; font-family:  Consolas,Menlo,Monaco,Lucida  Console,Liberation Mono,DejaVu Sans  Mono,Bitstream Vera Sans  Mono,Courier New,monospace,sans-serif;  font-size: 13px; white-space: pre;">)>><<EmbedContent("http://neuroimage.usc.edu/bst/viewcode.php?f=tutorial_visual_single.m")>><<HTML(</div >)>>

== 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  figures, 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.

<<EmbedContent("http://neuroimage.usc.edu/bst/get_prevnext.php?prev=Tutorials/Workflows")>>

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