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MEG visual tutorial: Single subject

Authors: Francois Tadel, Elizabeth Bock.

The aim of this tutorial is to reproduce in the Brainstorm environment the analysis described in the SPM tutorial "Multimodal, Multisubject data fusion". The data processed here consists in simultaneous MEG/EEG recordings of 19 subjects performing simple visual task on a large number of famous, unfamiliar and scrambled faces.

The analysis is split in two tutorial pages: the present tutorial describes the detailed analysis of one single subject and another one that describes the batch processing and group analysis of the 19 subjects.

Note that the operations used here are not detailed, the goal of this tutorial is not to teach Brainstorm to a new inexperienced user. For in depth explanations of the interface and the theory, please refer to the introduction tutorials.

Contents

License
Presentation of the experiment
Download and installation
Import the anatomy
Access the recordings
Pre-processing
Artifact detection
Epoching and averaging
Source estimation
Time-frequency analysis
Scripting

License

This dataset was obtained from the OpenfMRI project (http://www.openfmri.org), accession #ds117. It is made available under the Creative Commons Attribution 4.0 International Public License. Please cite the following reference if you use these data:
Wakeman DG, Henson RN, A multi-subject, multi-modal human neuroimaging dataset, Scientific Data (2015)

Any questions, please contact: rik.henson@mrc-cbu.cam.ac.uk

Presentation of the experiment

Experiment

19 subjects (three are excluded from the group analysis for various reasons)
6 acquisition runs (aka sessions) of approximately 10mins for each subject
Presentation of series of images: familiar faces, unfamiliar faces, phase-scrambled faces
The subject has to judge the left-right symmetry of each stimulus
Total of nearly 300 trials for each of the 3 conditions

MEG acquisition

Acquisition at 1100Hz with an Elekta-Neuromag VectorView system (simultaneous MEG+EEG).
Recorded channels (404):
- 102 magnetometers
- 204 planar gradiometers
- 70 EEG electrodes recorded with a nose reference.
MEG data have been "cleaned" using Signal-Space Separation as implemented in MaxFilter 2.2.
A Polhemus device was used to digitize three fiducial points and a large number of other points across the scalp, which can be used to coregister the M/EEG data with the structural MRI image.
Stimulation triggers: The triggers related with the visual presentation are saved in the STI101 channel, with the following event codes (bit 3 = face, bit 4 = unfamiliar, bit 5 = scrambled):
- Famous faces: 5 (00101), 6 (00110), 7 (00111)
- Unfamiliar faces: 13 (01101), 14 (01110), 15 (01111)
- Scrambled images: 17 (10001), 18 (10010), 19 (10011)
- Delays between the trigger in STI101 and the actual presentation of stimulus: 34.5ms
The distribution includes MEG noise recordings acquired around the dates of the experiment, processed with MaxFilter 2.2 in the same way as the experimental data.

Subject anatomy

MRI data acquired on a 3T Siemens TIM Trio: 1x1x1mm T1-weighted structural MRI.
The face has been removed from the strucural images for anonymization purposes.
Processed with FreeSurfer 5.3.

Download and installation

First, make sure you have enough space on your hard drive, at least 350Gb:
- The downloaded raw files: 100Gb
- The database after processing: 250Gb
The data is hosted on the OpenfMRI website: https://openfmri.org/dataset/ds000117/
Download all the files available from this website (approximately 160Gb):
ds117_metadata.tgz, ds117_sub001_raw.tgz, ..., ds117_sub019_raw.tgz
Unzip all the .tgz files in the same folder.
The FreeSurfer segmentations of the T1 images are not part of the OpenfMRI distribution. You can either process them by yourself, or download the result of the segmentation from the Brainstorm website.
Go to the Download page, and download the file: sample_group_freesurfer.zip
Unzip this file in the same folder as the other files.
We will use the following files from this distribution:
- /anatomy/subXXX/: Segmentation folders generated with FreeSurfer.
- /emptyroom/090707_raw_st.fif: MEG empty room measurements, processed with MaxFilter.
- /ds117/subXXX/MEG/*_sss.fif: MEG and EEG recordings, processed with MaxFilter.
- /README: License and dataset description.
Reminder: Do not put the downloaded files in the Brainstorm folders (program or database folders).
Start Brainstorm (Matlab scripts or stand-alone version). For help, see the Installation page.
Select the menu File > Create new protocol. Name it "TutorialVisual" and select the options:
- "No, use individual anatomy",
- "No, use one channel file per condition".

Import the anatomy

This page explains how to import and process subject #002 only. Subject #001 was not the best example because it will be later excluded from the EEG group analysis (incorrect electrodes positions).

Switch to the "anatomy" view.
Right-click on the TutorialVisual folder > New subject > sub002
- Leave the default options you defined for the protocol.
Right-click on the subject node > Import anatomy folder:
- Set the file format: "FreeSurfer folder"
- Select the folder: anatomy/freesurfer/sub002 (from sample_group_freesurfer.zip)
- Number of vertices of the cortex surface: 15000 (default value)
The two sets of fiducials we usually have to define interactively are here automatically set.
- NAS/LPA/RPA: The file Anatomy/Sub01/fiducials.m contains the definition of the nasion, left and right ears. The anatomical points used by the authors are the same as the ones we recommend in the Brainstorm coordinates systems page.
- AC/PC/IH: Automatically identified using the SPM affine registration with an MNI template.
- If you want to double-check that all these points were correctly marked after importing the anatomy, right-click on the MRI > Edit MRI.
At the end of the process, make sure that the file "cortex_15000V" is selected (downsampled pial surface, that will be used for the source estimation). If it is not, double-click on it to select it as the default cortex surface. Do not worry about the big holes in the head surface, parts of MRI have been remove voluntarily for anonymization purposes.
All the anatomical atlases generated by FreeSurfer were automatically imported: the cortical atlases (Desikan-Killiany, Mindboggle, Destrieux, Brodmann) and the sub-cortical regions (ASEG atlas).

Access the recordings

Link the recordings

We need to attach the continuous .fif files containing the recordings to the database.

Switch to the "functional data" view.
Right-click on the subject folder > Review raw file.
- Select the file format: "MEG/EEG: Neuromag FIFF (*.fif)"
- Select the first file in the MEG folder for subject 002: sub002/MEEG/run_01_sss.fif
Events: Ignore. We will read the stimulus triggers later.
Refine registration now? NO
The head points that are available in the FIF files contain all the points that were digitized during the MEG acquisition, including the ones corresponding to the parts of the face that have been removed from the MRI. If we run the fitting algorithm, all the points around the nose will not match any close points on the head surface, leading to a wrong result. We will first remove the face points and then run the registration manually.

Channel classification

A few non-EEG channels are mixed in with the EEG channels, we need to change this before applying any operation on the EEG channels.

Right-click on the channel file > Edit channel file. Double-click on a cell to edit it.
Change the type of EEG062 to EOG (electrooculogram).
Change the type of EEG063 to ECG (electrocardiogram).
Change the type of EEG061 and EEG064 to NOSIG. Close the window and save the modifications.

MRI registration

At this point, the registration MEG/MRI is based only on the three anatomical landmarks NAS/LPA/RPA. All the MRI scans were anonymized (defaced) and for some subjects the nasion could not be defined properly. We will try to refine this registration using the additional head points that were digitized (only the points above the nasion).

Right-click on the channel file > Digitized head points > Remove points below nasion.
Right-click on the channel file > MRI registration > Refine using head points.
MEG/MRI registration, before (left) and after (right) this automatic registration procedure:
Right-click on the channel file > MRI registration > EEG: Edit...
Click on [Project electrodes on surface], then close the figure to save the modifications.

Read stimulus triggers

We need to read the stimulus markers from the STI channels. The following tasks can be done in an interactive way with menus in the Record tab, as in the introduction tutorials. We will illustrate here how to do this with the pipeline editor, it will be easier to batch it for all the runs and all the subjects.

In Process1, select the "Link to raw file", click on [Run].
Select process Events > Read from channel, Channel: STI101, Detection mode: Bit.
Do not execute the process, we will add other processes to classify the markers.
We want to create three categories of events, based on their numerical codes:
- Famous faces: 5 (00101), 6 (00110), 7 (00111) => Bit 3 only
- Unfamiliar faces: 13 (01101), 14 (01110), 15 (01111) => Bit 3 and 4
- Scrambled images: 17 (10001), 18 (10010), 19 (10011) => Bit 5 only
- We will start by creating the category "Unfamiliar" (combination of events "3" and "4") and remove the remove the initial events. Then we just have to rename the remaining "3" in "Famous", and all the "5" in "Scrambled".
Add process Events > Group by name: "Unfamiliar=3,4", Delay=0, Delete original events
Add process Events > Rename event: 3 => Famous
Add process Events > Rename event: 5 => Scrambled
Add process Events > Add time offset to correct for the presentation delays:
Event names: "Famous, Unfamiliar, Scrambled", Time offset = 34.5ms
Finally run the script. Double-click on the recordings to make sure the labels were detected correctly. You can delete the unwanted events that are left in the recordings (1,2,9,13):

Pre-processing

Spectral evaluation

Keep the "Link to raw file" in Process1.
Run process Frequency > Power spectrum density (Welch) with the options illustrated below.
Right-click on the PSD file > Power spectrum.
Observations:
- Three groups of sensors, from top to bottom: EEG, MEG gradiometers, MEG magnetometers.
- Power lines: 50 Hz and harmonics
- Alpha peak around 10 Hz
- Artifacts due to Elekta electronics: 293Hz, 307Hz, 314Hz, 321Hz, 328Hz.
- Suspected bad EEG channels: EEG016
Close all the windows.

Remove line noise

Keep the "Link to raw file" in Process1.
Select process Pre-process > Notch filter to remove the line noise (50-200Hz).
Add the process Frequency > Power spectrum density (Welch).
Double-click on the PSD for the new continuous file to evaluate the quality of the correction.
Close all the windows (use the [X] button at the top-right corner of the Brainstorm window).

EEG reference and bad channels

Right-click on link to the processed file ("Raw | notch(50Hz ...") > EEG > Display time series.
Select channel EEG016 and mark it as bad (using the popup menu or pressing the Delete key).
In the Record tab, menu Artifacts > Re-reference EEG > "AVERAGE".
At the end, the window "select active projectors" is open to show the new re-referencing projector. Just close this window. To get it back, use the menu Artifacts > Select active projectors.

Artifact detection

Heartbeats: Detection

Empty the Process1 list (right-click > Clear list).
Drag and drop the continuous processed file ("Raw | notch(50Hz...)") to the Process1 list.
Run process Events > Detect heartbeats: Channel name=EEG063, All file, Event name=cardiac
In many of the other tutorials, we detect the blinks and remove them with SSP. In this experiment, we are particularly interested in the subject's response to seeing the stimulus. Therefore we will exclude from the analysis all the recordings contaminated with blinks or other eye movements.
Run process Artifacts > Detect events above threshold:
Event name=blink_BAD, Channel=EEG062, All file, Maximum threshold=100, Threshold units=uV, Filter=[0.30,20.00]Hz, Use absolute value of signal.
Inspect visually the two new cateogries of events: cardiac and blink_BAD.
Close all the windows (using the [X] button).

Heartbeats: Correction with SSP

Keep the "Raw | notch" file selected in Process1.
Select process Artifacts > SSP: Heartbeats > Sensor type: MEG MAG
Add process Artifacts > SSP: Heartbeats > Sensor type: MEG GRAD - Run the execution
Double-click on the continuous file to show all the MEG sensors.
In the Record tab, select sensors "Left-temporal".
Menu Artifacts > Select active projectors.
- In category cardiac/MEG MAG: Select component #1 and view topography.
- In category cardiac/MEG GRAD: Select component #1 and view topography.
Make sure that selecting the two components removes the cardiac artifact. Then click [Save].

Additional bad segments

Process1>Run: Select the process "Events > Detect other artifacts". This should be done separately for MEG and EEG to avoid confusion about which sensors are involved in the artifact.
Display the MEG sensors. Review the segments that were tagged as artifact, determine if each event represents an artifact and then mark the time of the artifact as BAD. This can be done by selecting the time window around the artifact, then right-click > Reject time segment. Note that this detection process marks 1-second segments but the artifact can be shorter.
Once all the events in the two categories are reviewed and bad segments are marked, the two categories (1-7Hz and 40-240Hz) can be deleted.
Do this detection and review again for the EEG.

SQUID jumps

MEG signals recorded with Elekta-Neuromag systems frequently contain SQUID jumps (more information). These sharp steps followed by a change of baseline value are easy to identify visually but more complicated to detect automatically.

The process "Detect other artifacts" usually detects most of them in the category "1-7Hz". If you observe that some are skipped, you can try re-running it with a higher sensitivity. It is important to review all the sensors and all the time in each run to be sure these events are marked as bad segments.

Epoching and averaging

Import epochs

Keep the "Raw | notch" file selected in Process1.
Select process: Import recordings > Import MEG/EEG: Events (do not run immediately)
Event names "Famous, Unfamiliar, Scrambled", All file, Epoch time=[-500,1200]ms
Add process: Pre-process > Remove DC offset: Baseline=[-500,-0.9]ms - Run execution.

Average by run

In Process1, select all the imported trials.
Run process: Average > Average files: By trial groups (folder average)

Review EEG ERP

EEG evoked response (famous, scrambled, unfamiliar):
Open the Cluster tab and create a cluster with the channel EEG065 (button [NEW IND]).
Select the cluster, select the three average files, right-click > Clusters time series (Overlay:Files).
Basic observations for EEG065 (right parieto-occipital electrode):
- Around 170ms (N170): greater negative deflection for Famous than Scrambled faces.
- After 250ms: difference between Famous and Unfamiliar faces.

Source estimation

MEG noise covariance: Empty room recordings

The minimum norm model we will use next to estimate the source activity can be improved by modeling the the noise contaminating the data. The section shows how to estimate the noise covariance in different ways for EEG and MEG. For the MEG recordings we will use the empty room measurements we have, and for the EEG we will compute it from the pre-stimulus baselines we have in all the imported epochs.

Create a new subject: emptyroom
Right-click on the new subject > Review raw file.
Select file: sample_group/emptyroom/090707_raw_st.fif
Do not apply default transformation, Ignore event channel.
Select this new file in Process1 and run process Pre-process > Notch filter: 50 100 150 200Hz. When using empty room measurements to compute the noise covariance, they must be processed exactly in the same way as the other recordings.
Right-click on the filtered noise recordings > Noise covariance > Compute from recordings:
Right-click on the Noise covariance > Copy to other subjects

EEG noise covariance: Pre-stimulus baseline

In folder sub002/run_01_sss_notch, select all the imported the imported trials, right-click > Noise covariance > Compute from recordings, Time=[-500,-0.9]ms, EEG only, Merge.
This computes the noise covariance only for EEG, and combines it with the existing MEG information.

BEM layers

We will compute a BEM forward model to estimate the brain sources from the EEG recordings. For this, we need some layers defining the separation between the different tissues of the head (scalp, inner skull, outer skull).

Go to the anatomy view (first button above the database explorer).
Right-click on the subject folder > Generate BEM surfaces: The number of vertices to use for each layer depends on your computing power and the accuracy you expect. You can try for instance with 1082 vertices (scalp) and 642 vertices (outer skull and inner skull).

Forward model: EEG and MEG

Go back to the functional view (second button above the database explorer).
Model used: Overlapping spheres for MEG, OpenMEEG BEM for EEG (more information).
In folder sub002/run_01_sss_notch, right-click on the channel file > Compute head model.
Keep all the default options . Expect this to take a while...

Inverse model: Minimum norm estimates

Right-click on the new head model > Compute sources [2016]: MEG MAG + GRAD (default options)
Right-click on the new head model > Compute sources [2016]: EEG (default bad channels).
At the end we have two inverse operators, that are shared for all the files of the run (single trials and averages). If we wanted to look at the run-level source averages, we could normalize the source maps with a Z-score wrt baseline. In this tutorial, we will first average across runs and normalize the subject-level averages. This will be done in the next tutorial (group analysis).

Time-frequency analysis

We will compute the time-frequency decomposition of each trial using Morlet wavelets, and average the power of the Morlet coefficients for each condition and each run separately.

In Process1, select the imported trials "Famous" for run#01.
Run process Frequency > Time-frequency (Morlet wavelets)
Sensor types=MEG,EEG, Not normalized, Frequency=Log(6:40:80), Measure=Power, Save average
Double-click on the file to display it.
Repeat this operation for other conditions (Scrambled and Unfamiliar) and the other runs (2-6). There is no way with this process to compute all the averages at once, as we did with the process "Average files". This operation will be easier to run from a script.
If we wanted to look at the run-level source averages, we could normalize these time-frequency maps. In this tutorial, we will first average across runs and normalize the subject-level averages. This will be done in the next tutorial (group analysis).

Scripting

We have now all the files we need for the group analysis (next tutorial). We need to repeat the same operations for all the runs and all the subjects. Some of these steps are fully automatic and take a lot of time (filtering, computing the forward model), they should be executed from a script.

However, we recommend you always review manually some of the pre-processing steps (selection of the bad segments and bad channels, SSP/ICA components). Do not trust blindly any fully automated cleaning procedure.

For the strict reproducibility of this analysis, we provide a script that processes all the 19 subjects: 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} = {...
         [141.690 142.424; 157.070 157.417; 355.138 356.025; 423.999 423.999; 424.999 424.999; 426.999 426.999; 427.999 427.999; 486.430 488.151; 493.999 493.999; 501.511 501.619; 501.549 501.549; 501.585 501.585; 502.999 502.999; 503.999 503.999; 540.999 540.999; 541.999 541.999; 555.999 555.999; 556.999 556.999; 561.999 561.999; 563.999 563.999; 564.999 564.999; 565.999 565.999; 567.999 567.999], ...
         [64.898 65.161; 71.700 72.718; 226.185 226.740; 324.124 324.425; 329.062 329.301; 486.143 486.975], ...
         [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} = {...
         [154.966 155.144; 551.639 551.855], ...
         [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], ...
         [81.627 82.136; 377.953 381.046], ...
         [241.136 242.171; 543.849 544.196; 596.639 598.553; 600.227 601.075; 603.999 603.999; 605.999 605.999; 609.999 609.999; 611.305 612.809; 614.999 614.999; 615.803 616.937; 623.694 625.877; 653.999 653.999; 655.055 655.756; 663.999 663.999], ...
         [68.852 69.481; 74.034 75.200; 78.426 78.600; 104.497 105.963; 253.951 254.034; 256.964 257.038; 257.915 258.048; 323.254 324.156; 365.880 368.131; 369.952 370.060; 371.728 372.999; 430.931 431.965; 535.521 542.999], ...
         [70.271 71.205; 94.441 96.445; 98.613 99.126; 112.318 112.749; 131.686 132.265; 148.935 150.615; 161.120 161.600; 205.325 208.214; 215.035 215.863; 217.403 218.818; 286.178 287.171; 399.075 404.853]};
     BadSegments{8} = {...
         [238.505 238.546; 256.354 257.224; 316.116 316.167; 341.519 341.558; 356.493 356.566; 380.095 380.170; 391.906 392.048; 448.457 448.562; 469.931 470.028; 530.488 530.575; 555.180 558.136; 562.152 562.245; 588.403 588.502; 625.205 625.662; 638.019 638.438; 649.982 650.008; 650.691 651.357; 651.925 652.061; 665.445 665.472; 695.923 696.015; 706.528 706.720; 729.706 732.534], ...
         [99.546 106.114; 113.245 114.199; 150.885 154.989; 277.486 278.075; 333.645 335.574; 339.358 340.646; 371.860 375.394; 497.459 499.156], ...
         [49.008 50.205; 231.446 233.406; 329.590 329.659; 355.101 356.019; 360.733 360.973; 372.955 374.891; 389.283 392.068; 453.610 455.431; 464.632 465.265; 489.209 489.996; 514.777 515.295], ...
         [178.368 179.232; 293.865 294.521; 418.252 418.314; 450.124 450.209; 480.236 480.445; 492.725 493.975; 495.170 497.624; 500.382 500.459; 504.247 504.402; 628.017 628.079; 628.827 628.905; 630.209 630.332], ...
         [100.616 101.172; 107.691 108.539; 188.814 188.875; 193.119 193.281; 207.964 208.033; 432.254 432.385; 489.834 489.911; 517.960 518.037; 518.955 519.040; 520.938 521.062; 521.945 522.037; 523.959 524.052; 525.942 526.057; 526.945 527.015; 528.958 529.059; 531.138 531.192; 531.979 532.048; 532.951 533.028; 533.962 534.024], ...
         [135.997 137.232; 383.565 383.657; 418.763 418.955]};
     BadSegments{9} = {...
         [215.107 216.187; 262.388 262.388; 287.519 287.635; 289.895 290.135; 311.999 311.999; 350.161 351.179; 526.154 527.033; 564.999 564.999; 584.935 585.059; 587.999 587.999; 601.999 601.999; 603.999 603.999; 608.999 608.999; 612.999 612.999], ...
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         [82.889 83.198; 206.143 207.424; 403.873 404.096; 406.790 407.485; 413.936 414.075; 415.912 416.112; 536.621 537.532], ...
         [182.999 182.999; 182.999 182.999; 183.999 183.999; 195.999 195.999; 208.999 208.999; 209.999 209.999; 278.601 278.955; 413.913 414.067; 415.250 417.792; 419.940 420.087; 420.999 420.999; 512.999 512.999; 514.363 516.254; 521.917 522.134; 522.999 522.999; 523.915 524.101; 533.999 533.999; 538.999 538.999; 539.892 540.062; 543.999 543.999; 548.874 549.089; 549.900 550.062; 552.755 554.075; 585.999 585.999; 587.957 588.073; 588.999 588.999; 594.999 594.999; 603.971 604.056; 604.928 605.097; 615.986 618.495; 623.999 623.999], ...
         [53.215 54.203; 164.227 168.965; 201.433 202.775; 292.973 295.889; 303.961 304.817; 309.354 311.236; 313.123 313.639; 322.547 323.017; 331.141 334.852; 356.637 356.985; 367.855 368.079; 369.995 373.459; 377.849 378.142; 406.072 407.306; 436.164 436.665; 458.033 458.905; 516.889 518.656; 517.812 518.684], ...
         [113.945 115.225; 198.570 198.863; 264.162 264.795; 383.705 385.641; 396.477 397.064; 399.706 406.457; 452.261 453.179; 486.338 487.102; 498.869 499.579; 507.968 508.925; 546.266 547.285; 558.825 560.128]};
     BadSegments{10} = {...
         [235.104 236.184; 324.272 325.028; 330.799 331.401; 541.062 541.826; 564.747 565.072], ...
         [100.581 101.229; 285.644 285.644; 285.644 285.644; 297.979 298.033; 298.999 298.999; 300.949 301.026; 301.999 301.999; 303.999 303.999; 304.999 304.999; 306.999 306.999; 307.999 307.999; 310.999 310.999; 311.999 311.999; 313.971 314.025; 314.999 314.999; 316.995 317.035; 317.999 317.999; 319.999 319.999; 320.923 321.046; 326.971 327.048; 327.999 327.999; 329.999 329.999; 330.999 330.999; 332.974 333.028; 333.999 333.999; 335.971 336.025; 336.999 336.999; 338.973 339.035; 339.999 339.999; 341.951 342.044; 343.999 343.999; 344.964 345.033; 346.999 346.999; 347.999 347.999; 452.015 453.411; 458.735 459.776; 467.974 468.089], ...
         [41.266 41.675; 53.905 55.240; 159.159 159.345; 220.255 220.394], ...
         [66.751 67.190; 201.674 201.812; 294.119 295.168; 303.188 303.528; 316.992 317.037; 317.999 317.999; 317.999 317.999; 319.999 319.999; 320.980 321.042; 322.999 322.999; 325.999 325.999; 326.955 327.048; 328.999 328.999; 333.999 333.999; 334.999 334.999; 342.999 342.999; 343.999 343.999; 351.915 352.038; 352.999 352.999; 407.979 408.056; 409.999 409.999; 411.999 411.999; 415.405 416.154; 435.999 435.999; 436.962 437.046; 468.885 469.100; 469.999 469.999; 470.999 470.999; 516.210 516.357], ...
         [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], ...
         [229.948 230.033; 230.999 230.999; 230.999 230.999; 259.588 259.990; 343.970 345.798; 361.999 361.999; 362.970 363.046; 364.999 364.999; 366.999 366.999; 367.962 368.031; 372.521 374.179; 405.999 405.999; 409.999 409.999; 411.999 411.999; 412.999 412.999; 434.999 434.999; 435.970 436.031; 516.999 516.999; 519.999 519.999]};
     BadSegments{11} = {...
         [179.045 180.225; 323.999 323.999; 323.999 323.999; 324.999 324.999; 327.978 328.025; 328.966 329.028; 330.999 330.999; 365.999 365.999; 367.999 367.999; 370.999 370.999; 371.999 371.999; 373.999 373.999; 375.965 376.042; 377.999 377.999], ...
         [52.107 53.156; 521.077 521.155], ...
         [65.331 66.156], ...
         [81.579 82.143; 108.565 108.565; 108.566 108.566; 112.176 112.176; 122.377 122.377; 122.565 122.565; 213.882 213.882; 215.305 215.305; 224.851 224.851; 224.919 224.919; 255.815 255.815; 257.893 257.893; 359.952 359.952; 361.630 361.754; 370.285 370.285; 376.853 376.853; 511.600 511.600; 513.631 513.631; 513.988 513.988; 518.215 518.215], ...
         [63.205 64.154; 170.951 171.036; 176.841 176.841; 176.842 176.842; 177.282 177.282; 223.886 223.971; 259.963 259.963; 261.547 261.547; 341.185 341.302; 368.999 368.999; 370.999 370.999; 374.999 374.999; 382.971 383.033; 383.999 383.999; 386.999 386.999; 388.958 389.035; 390.999 390.999; 391.955 392.031; 394.955 395.040; 396.999 396.999; 398.999 398.999; 400.999 400.999; 402.999 402.999; 404.963 405.033; 406.999 406.999; 429.829 429.829; 430.346 430.346; 465.184 465.184; 470.290 470.290], ...
         [50.195 51.145; 158.850 159.012]};
     BadSegments{12} = {...
         [193.435 194.175; 500.000 501.000; 08.988 509.868; 528.999 528.999; 528.999 528.999; 529.999 529.999; 531.999 531.999; 547.999 547.999], ...
         [133.484 134.185; 134.911 135.065; 553.999 553.999; 553.999 553.999; 554.999 554.999; 557.999 557.999; 558.999 558.999; 564.999 564.999; 565.977 566.046; 568.999 568.999; 569.951 570.028; 571.999 571.999; 579.959 580.028; 580.977 581.055; 583.971 584.033; 583.999 583.999; 585.999 585.999; 586.999 586.999; 588.980 589.026; 590.999 590.999; 591.958 592.044; 595.999 595.999], ...
         [46.028 47.216; 129.557 130.236; 200.999 200.999; 200.999 200.999; 201.975 202.075; 203.999 203.999; 204.967 205.053; 213.627 214.515; 218.958 219.044; 222.959 228.252; 309.999 309.999; 311.897 317.537; 311.999 311.999; 351.968 353.234; 399.999 399.999; 446.508 451.454; 487.999 487.999; 512.278 512.926], ...
         [82.535 84.062; 102.247 102.355; 134.999 134.999; 134.999 134.999; 136.999 136.999; 138.973 139.042; 147.999 147.999; 148.999 148.999; 149.999 149.999; 193.999 193.999; 194.999 194.999; 224.984 226.095; 241.954 242.077; 243.948 244.087; 280.999 280.999; 281.999 281.999; 305.000 305.451; 310.945 311.068; 328.999 328.999; 352.999 352.999; 353.999 353.999; 397.025 397.411; 415.944 418.096; 415.999 415.999; 470.999 470.999; 477.971 478.041; 483.997 484.737; 492.958 493.089; 493.999 493.999; 494.999 494.999; 522.999 522.999; 523.999 523.999; 524.955 525.039], ...
         [175.183 176.155; 246.112 246.991; 412.005 413.340; 483.915 484.061; 485.959 486.028; 497.360 498.880; 594.457 594.944; 596.641 598.006; 615.277 618.710], ...
         [66.900 72.232; 75.510 78.688; 543.495 545.999]};
     BadSegments{13} = {...
         [307.246 308.179; 627.881 628.089; 629.941 630.049], ...
         [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], ...
         [52.791 53.231; 54.999 54.999; 65.985 68.138; 91.240 91.386; 92.137 92.207; 105.346 105.493; 121.120 121.398; 146.546 146.955; 194.025 197.652; 242.985 243.571; 247.101 247.556; 269.889 270.083; 270.946 271.070; 274.408 275.866; 294.565 295.267; 319.370 319.879; 365.999 365.999; 375.913 376.044; 376.869 377.055; 377.999 377.999; 390.130 393.225; 403.965 404.035; 404.953 405.075; 419.265 419.565; 427.015 427.154; 427.879 428.118; 427.999 427.999; 428.913 429.067; 480.945 482.095; 484.285 484.571; 516.929 517.099; 517.963 518.079], ...
         [115.921 116.060; 117.999 117.999; 120.999 120.999; 126.896 127.058; 130.955 131.039; 131.957 132.043; 132.961 133.015; 134.999 134.999; 135.950 136.043; 139.999 139.999; 143.999 143.999; 144.850 145.066; 145.999 145.999; 146.999 146.999; 160.929 161.045; 161.999 161.999; 189.086 189.310; 193.939 194.063; 195.975 196.045; 199.961 200.045; 201.952 202.059; 202.939 205.084; 210.999 210.999; 211.738 213.629; 217.954 218.061; 218.987 219.049; 219.975 220.028; 258.953 259.037; 259.952 260.045; 260.963 261.032; 261.958 262.043; 262.954 263.054; 262.999 262.999; 288.999 288.999; 295.477 295.655; 298.976 299.054; 299.979 300.018; 300.975 301.005; 336.952 337.044; 337.977 338.032; 338.988 339.027; 338.999 338.999; 339.991 340.045; 340.975 341.029; 341.978 342.016; 343.976 344.054; 361.961 362.045; 365.942 366.050; 370.999 370.999; 384.961 385.045; 409.977 410.047; 415.984 416.030; 426.944 427.059; 430.999 430.999; 436.999 436.999; 448.999 448.999; 449.500 452.131; 481.962 482.055; 482.973 483.027; 484.975 485.060; 503.999 503.999; 504.999 504.999; 507.967 508.028; 508.955 509.032; 510.999 510.999; 512.999 512.999; 518.865 520.292; 522.958 523.043; 523.999 523.999; 524.948 525.034; 525.959 526.044; 526.946 527.055; 527.942 528.027; 528.961 529.053; 529.945 530.022; 535.999 535.999; 536.957 537.058; 538.967 539.036; 539.963 540.040; 541.976 542.045; 543.999 543.999; 551.956 552.034; 552.990 553.028; 553.977 554.024; 555.968 556.022; 556.204 557.454; 557.963 558.032; 559.969 560.023; 563.973 564.035; 563.999 563.999], ...
         [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} = {...
         [365.982 367.194; 368.999 368.999; 368.999 368.999; 369.999 369.999; 371.999 371.999; 373.999 373.999; 375.942 376.050; 376.999 376.999; 378.999 378.999; 380.999 380.999; 382.999 382.999; 383.999 383.999; 386.999 386.999; 387.999 387.999; 418.999 418.999; 444.999 444.999; 690.178 693.218], ...
         [101.796 102.183; 218.999 218.999; 219.999 219.999; 221.999 221.999; 222.999 222.999; 227.999 227.999; 229.999 229.999; 230.999 230.999; 232.999 232.999; 233.999 233.999; 235.999 235.999; 237.999 237.999; 238.985 239.031; 240.999 240.999; 242.999 242.999; 243.999 243.999; 246.999 246.999; 247.999 247.999; 253.999 253.999; 258.999 258.999; 259.999 259.999; 260.227 261.123; 264.999 264.999; 265.999 265.999; 268.999 268.999; 269.999 269.999; 274.999 274.999; 276.999 276.999; 277.999 277.999; 280.999 280.999; 282.966 283.021; 284.999 284.999; 285.999 285.999; 360.999 360.999; 362.973 363.026; 364.999 364.999; 365.999 365.999; 367.999 367.999; 368.985 369.024; 370.999 370.999; 371.999 371.999; 378.999 378.999; 379.999 379.999; 381.999 381.999; 383.999 383.999; 385.999 385.999; 387.999 387.999; 388.999 388.999; 398.999 398.999; 415.999 415.999; 417.999 417.999; 418.999 418.999; 421.999 421.999; 422.999 422.999], ...
         [89.285 90.187], ...
         [85.991 87.179; 88.999 88.999; 89.999 89.999; 92.966 93.027; 93.999 93.999; 107.999 107.999; 110.999 110.999; 111.971 112.025; 118.999 118.999; 121.999 121.999; 122.999 122.999; 166.999 166.999; 168.957 169.035; 173.999 173.999; 174.999 174.999; 175.957 176.035; 198.999 198.999; 199.999 199.999; 202.999 202.999; 203.999 203.999; 205.999 205.999; 207.999 207.999; 208.999 208.999; 210.974 211.044; 211.999 211.999; 212.999 212.999; 232.984 233.023; 234.999 234.999; 235.999 235.999; 236.999 236.999; 239.958 240.044; 240.999 240.999], ...
         [71.132 72.227], ...
         [98.134 98.827; 110.497 110.814; 114.000 117.272; 279.999 279.999; 279.999 279.999; 280.999 280.999; 282.999 282.999; 283.999 283.999; 284.999 284.999; 286.999 286.999; 287.999 287.999; 288.999 288.999; 289.999 289.999; 291.966 292.044; 292.999 292.999; 346.999 346.999; 437.608 437.646; 518.988 519.104; 522.969 523.015; 534.999 534.999; 536.967 537.029; 562.725 563.426]};
     BadSegments{15} = {...
         [66.755 67.172; 257.988 258.042; 258.714 259.385; 260.958 261.044; 265.931 266.062; 267.968 268.022; 268.978 269.033; 270.992 271.031; 272.964 273.017; 273.968 274.030; 275.975 276.059; 276.970 277.046; 288.976 289.038; 289.984 290.015; 295.994 296.025; 296.982 297.013; 300.966 301.152; 364.730 364.800], ...
         [46.531 46.555; 65.156 66.148; 87.535 87.642; 183.896 183.934; 294.342 294.375; 330.004 330.528; 333.785 333.815; 345.177 345.203; 399.544 399.573; 480.964 480.980], ...
         [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], ...
         [64.786 65.141; 132.385 132.786; 177.735 178.252; 278.747 278.902; 313.959 314.028; 314.970 315.031]};
     BadSegments{16} = {...
         [38.958 44.335; 305.280 312.826; 324.778 326.067; 393.183 398.367; 403.410 422.854], ...
         [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], ...
         [56.020 57.255; 60.711 61.096; 64.353 64.924; 73.202 73.757; 76.134 76.844; 95.625 96.859; 171.436 172.625; 178.359 178.715; 212.828 213.415; 352.425 352.617; 367.755 369.097; 488.400 489.426], ...
         [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


You should note that this is not the result of a fully automated procedure. The bad channels were identified manually and are defined for each run in the script. The bad segments were detected automatically, confirmed manually for each run and saved in external files distributed with the Brainstorm package sample_group_freesurfer.zip (sample_group/brainstorm/bad_segments/*.mat).

All the process calls (bst_process) were generated automatically using with the script generator (menu Generate .m script in the pipeline editor). Everything else was added manually (loops, bad channels, file copies).

-  ⇤ ← Revision 19 as of 2015-07-23 17:22:39 → 
  Size: 9209
  Editor: FrancoisTadel
  Comment:
+   ← Revision 196 as of 2016-07-06 23:36:19 → ⇥
  Size: 26356
  Editor: FrancoisTadel
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-The aim of this tutorial is to reproduce in the Brainstorm environment the analysis described in the SPM tutorial "[[ftp://ftp.mrc-cbu.cam.ac.uk/personal/rik.henson/wakemandg_hensonrn/Publications/SPM12_manual_chapter.pdf|Multimodal, Multisubject data fusion]]". The data processed here consists in simulateneous MEG/EEG recordings of 16 subjects performing simple visual task on a large number of famous, unfamiliar and scrambled faces.

The analysis is split in two tutorial pages: the present tutorial describes the detailed analysis of one single subject and another one that the describes the batch processing and [[Tutorials/VisualGroup|group analysis of the 16 subjects]].

Note that the operations used here are not detailed, the goal of this tutorial is not to teach Brainstorm to a new inexperienced user. For in depth explanations of the interface and the theory, please refer to the introduction tutorials.
+The aim of this tutorial is to reproduce in the Brainstorm environment the analysis described in the SPM tutorial "[[ftp://ftp.mrc-cbu.cam.ac.uk/personal/rik.henson/wakemandg_hensonrn/Publications/SPM12_manual_chapter.pdf|Multimodal, Multisubject data fusion]]". The data processed here consists in simultaneous MEG/EEG recordings of 19 subjects performing simple visual task on a large number of famous, unfamiliar and scrambled faces.

The analysis is split in two tutorial pages: the present tutorial describes the detailed analysis of one single subject and another one that describes the batch processing and [[Tutorials/VisualGroup|group analysis of the 19 subjects]].

Note that the operations used here are not detailed, the goal of this tutorial is not to teach Brainstorm to a new inexperienced user. For in depth explanations of the interface and the theory, please refer to the [[http://neuroimage.usc.edu/brainstorm/Tutorials#Get_started|introduction tutorials]].
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-These  data are provided freely for research purposes only (as part of their  Award of the BioMag2010 Data Competition). If you wish to publish any of  these data, please acknowledge Daniel Wakeman and Richard Henson. The  best single reference is: Wakeman DG, Henson RN, [[http://www.nature.com/articles/sdata20151|A  multi-subject, multi-modal human neuroimaging dataset]], Scientific Data (2015)
+This dataset was obtained from the OpenfMRI project (http://www.openfmri.org), accession #ds117. It is made available under the Creative Commons Attribution 4.0 International Public License. Please cite the following reference if you use these data: <<BR>>Wakeman DG, Henson RN, [[http://www.nature.com/articles/sdata20151|A  multi-subject, multi-modal human neuroimaging dataset]], Scientific Data (2015)
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- * 16 subjects
 * 6 runs (sessions) of approximately 10mins for each subject
+ * 19 subjects (three are excluded from the group analysis for [[http://neuroimage.usc.edu/brainstorm/Tutorials/VisualSingle#Bad_subjects|various reasons]])
 * 6 acquisition runs (aka sessions) of approximately 10mins for each subject
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- * Total of nearly 300 trials in total for each of the 3 conditions
+ * Total of nearly 300 trials for each of the 3 conditions
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- * MEG data have been "cleaned" using Signal-Space Separation as implemented in MaxFilter 2.1.
 * A  Polhemus digitizer was used to digitise three fiducial points and a  large number of other points across the scalp, which can be used to  coregister the M/EEG data with the structural MRI image.
+ * MEG data have been "cleaned" using Signal-Space Separation as implemented in MaxFilter 2.2.
 * A  Polhemus device was used to digitize three fiducial points and a  large number of other points across the scalp, which can be used to  coregister the M/EEG data with the structural MRI image.
 * Stimulation triggers: The triggers related with the visual presentation are saved in the STI101 channel, with the following event codes (bit 3 = face, bit 4 = unfamiliar, bit 5 = scrambled):
  * Famous faces: 5 (00101), 6 (00110), 7 (00111)
  * Unfamiliar faces: 13 (01101), 14 (01110), 15 (01111)
  * Scrambled images: 17 (10001), 18 (10010), 19 (10011)
  * Delays between the trigger in STI101 and the actual presentation of stimulus: '''34.5ms'''
 * The distribution includes MEG noise recordings acquired around the dates of the experiment, processed with MaxFilter 2.2 in the same way as the experimental data.
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- * MRI data acquired on a 3T Siemens TIM Trio: 1x1x1mm T1-weighted structural MRI
 * Processed with FreeSurfer 5.3
+ * MRI data acquired on a 3T Siemens TIM Trio: 1x1x1mm T1-weighted structural MRI.
 * The face has been removed from the strucural images for anonymization purposes.
 * Processed with FreeSurfer 5.3.
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- * '''Requirements''':  You have already followed all the introduction tutorials  and you have a  working copy of Brainstorm installed on your computer.
 * The data is hosted on this FTP site (use an FTP client such as FileZilla, not your web browser): <<BR>>ftp://ftp.mrc-cbu.cam.ac.uk/personal/rik.henson/wakemandg_hensonrn/
 * Download only the following folders (about 75Gb):
  * '''EmptyRoom''': Contains 3 sub-directories of empty-room recordings of 3-5mins acquired at roughly the same time of year (spring 2009) as the 16 subjects. The sub-directory names are Year (first 2 digits), Month (second 2 digits) and Day (third 2 digits). Inside each are 2 raw *.fif files: one for which basic SSS has been applied by maxfilter in a similar manner to the subject data above, and one (*-noSSS.fif) for which SSS has not been applied (though the data have been passed through maxfilter just to convert to float format).
  * '''Publications''': Reference publications related with this dataset.
  * '''SubXX/MEEG''': MEG and EEG recordings in FIF format and triggers definition files.
  * '''README.TXT''': License and dataset description.
 * The output of the FreeSurfer segmentation of the T1 structural images are not part of this package. You can either process them by yourself, or download the result of the segmentation on this website. Go to the [[http://neuroimage.usc.edu/bst/download.php|Download]] page, and download the file: '''sample_group_anat.zip'''<<BR>>Unzip this file in the same folder where you downloaded all the datasets.
 * Do not put these downloaded files in any of the Brainstorm  folders (program     folder or database folder). This is really  important that you always     keep your original data files in a  separate folder: the program  folder    can be deleted when updating the  software, and the contents of  the    database folder is supposed to be  manipulated only by the  program    itself.
 * Start Brainstorm (Matlab scripts or stand-alone version), see the [[Installation]] page.
+ * First, make sure you have enough space on your hard drive, at least '''350Gb''':
  * The downloaded raw files: '''100Gb'''
  * The database after processing: '''250Gb'''
 * The data is hosted on the OpenfMRI website: https://openfmri.org/dataset/ds000117/
 * Download all the files available from this website (approximately 160Gb):<<BR>>'''ds117_metadata.tgz''', '''ds117_sub001_raw.tgz''', ..., '''ds117_sub019_raw.tgz'''

 * Unzip all the .tgz files in the same folder.
 * The FreeSurfer segmentations of the T1 images are not part of the OpenfMRI distribution. You can either process them by yourself, or download the result  of the segmentation from the Brainstorm website. <<BR>>Go to the [[http://neuroimage.usc.edu/bst/download.php|Download]] page, and download the file: '''sample_group_freesurfer.zip'''<<BR>>Unzip this file in the same folder as the other files.
 * We will use the following files from this distribution:
  * '''/anatomy/subXXX/''': Segmentation folders generated with FreeSurfer.
  * '''/emptyroom/090707_raw_st.fif''': MEG empty room measurements, processed with MaxFilter.
  * '''/ds117/subXXX/MEG/*_sss.fif''': MEG and EEG recordings, processed with MaxFilter.
  * '''/README''': License and dataset description.
 * Reminder: Do not put the downloaded files in the Brainstorm  folders (program or database folders).
 * Start Brainstorm (Matlab scripts or stand-alone version). For help, see the [[Installation]] page.
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+This page explains how to import and process subject '''#002''' only. Subject #001 was not the best example because it will be later excluded from the EEG group analysis (incorrect electrodes positions).
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- * Right-click on the TutorialAuditory folder > New subject > '''Sub01'''
  * Leave the default options you set for the protocol
+ * Right-click on the TutorialVisual folder > New subject > '''sub002'''
  * Leave the default options you defined for the protocol.
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-  * Select the folder: '''Anatomy/Sub01'''  (from sample_group_anat.zip)
+  * Select the folder: '''anatomy/freesurfer/sub002'''  (from sample_group_freesurfer.zip)
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- * The two sets of fiducials we usually have to define interactively are here set automatically.
+ * The two sets of fiducials we usually have to define interactively are here automatically set.
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-  * '''AC/PC/IH''': Identified automatically using the SPM affine registration with an MNI template.
+  * '''AC/PC/IH''': Automatically identified using the SPM affine registration with an MNI template.
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+ * All the anatomical atlases [[Tutorials/LabelFreeSurfer|generated by FreeSurfer]] were automatically imported: the cortical atlases (Desikan-Killiany, Mindboggle, Destrieux, Brodmann) and the sub-cortical regions (ASEG atlas). <<BR>><<BR>> {{attachment:anatomy_atlas.gif||height="211",width="550"}}
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+We need to attach the continuous .fif files containing the recordings to the database.
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-  * Select all the FIF files in: ''''''Sub01'''/MEEG''' <<BR>><<BR>>SCREEN CAPTURE
 * Events:  SCREEN CAPTURES
 * Refine registration now? '''NO'''
  * The head points that are available in the FIF files contain all the points that were digitized during the MEG acquisition, including the ones corresponding to the parts of the face that have been removed from the MRI. If we run the fitting algorithm, all the points around the nose will not match any close points on the head surface, leading to a wrong result.
  * We will import a set of head points from which the face points have been removed and will run the automatic registration on those new points.
 *

=== Multiple runs and head position ===
 * The  two AEF runs 01 and 02 were acquired successively, the position of the  subject's head in the MEG helmet was estimated twice, once at the  beginning of each run. The subject might have moved between the two  runs. To evaluate visually the displacement between the two runs, select  at the same time all the channel files you want to compare (the ones  for run 01 and 02), right-click > Display sensors > MEG.

  {{http://neuroimage.usc.edu/brainstorm/Tutorials/Auditory?action=AttachFile&do=get&target=raw3.gif|raw3.gif|height="220",width="441",class="attachment"}}
 * Typically,  we would like to group the trials coming from multiple runs by  experimental conditions. However, because of the subject's movements  between runs, it's not possible to directly compare the sensor values  between runs because they probably do not capture the brain activity  coming from the same regions of the brain.
 * You have three options if you consider grouping information from multiple runs:
  * '''Method 1''':  Process all the runs separately and average between runs at the source  level: The more accurate option, but requires a lot more work,  computation time and storage.
  * '''Method 2''':  Ignore movements between runs: This can be acceptable for commodity if  the displacements are really minimal, less accurate but much faster to  process and easier to manipulate.
  * '''Method 3''':  Co-register properly the runs using the process Standardize >  Co-register MEG runs: Can be a good option for displacements under 2cm.
 Warning: This method has not be been fully evaluated on our side, to use  at your own risk. Also, it does not work correctly if you have  different SSP  projectors calculated for multiple runs.
 * In this tutorial, we will illustrate only method 1: runs are not co-registered.
+  * Select the first file in the MEG folder for subject 002: '''sub002/MEEG/run_01_sss.fif''' <<BR>><<BR>> {{attachment:review_raw.gif||height="202",width="542"}}
 * Events:''' Ignore'''. We will read the stimulus triggers later.<<BR>><<BR>> {{attachment:review_ignore.gif||height="186",width="330"}}
 * Refine registration now? '''NO''' <<BR>>The head points that are available in the FIF files contain all the points that were digitized during the MEG acquisition, including the ones corresponding to the parts of the face that have been removed from the MRI. If we run the fitting algorithm, all the points around the nose will not match any close points on the head surface, leading to a wrong result. We will first remove the face points and then run the registration manually.

=== Channel classification ===
A few non-EEG channels are mixed in with the EEG channels, we need to change this before applying any operation on the EEG channels.

 * Right-click on the channel file > '''Edit channel file'''. Double-click on a cell to edit it.
 * Change the type of '''EEG062''' to '''EOG '''(electrooculogram).
 * Change the type of '''EEG063 '''to '''ECG '''(electrocardiogram).
 * Change the type of '''EEG061''' and '''EEG064''' to '''NOSIG'''. Close the window and save the modifications. <<BR>><<BR>> {{attachment:channel_edit.gif||height="252",width="561"}}

=== MRI registration ===
At this point, the registration MEG/MRI is based only on the three anatomical landmarks NAS/LPA/RPA. All the MRI scans were anonymized (defaced) and for some subjects the nasion could not be defined properly. We will try to refine this registration using the additional head points that were digitized (only the points above the nasion).

 * Right-click on the channel file > '''Digitized head points > Remove points below nasion'''.<<BR>><<BR>> {{attachment:channel_remove.gif||height="194",width="329"}}
 * Right-click on the channel file > '''MRI registration > Refine using head points'''.<<BR>><<BR>> {{attachment:channel_refine.gif||height="173",width="294"}}
 * MEG/MRI registration, before (left) and after (right) this automatic registration procedure:  <<BR>><<BR>> {{attachment:registration.gif||height="209",width="236"}} {{attachment:registration_final.gif||height="208",width="237"}}
 * Right-click on the channel file > MRI registration > EEG: Edit...<<BR>>Click on ['''Project electrodes on surface'''], then close the figure to save the modifications.<<BR>><<BR>> {{attachment:channel_project.gif||height="207",width="477"}}

=== Read stimulus triggers ===
We need to read the stimulus markers from the STI channels. The following tasks can be done in an interactive way with menus in the Record tab, as in the introduction tutorials. We will illustrate here how to do this with the pipeline editor, it will be easier to batch it for all the runs and all the subjects.

 * In Process1, select the "Link to raw file", click on [Run].
 * Select process '''Events > Read from channel''', Channel: '''STI101''', Detection mode: '''Bit'''.<<BR>>Do not execute the process, we will add other processes to classify the markers.<<BR>><<BR>> {{attachment:raw_read_events.gif||height="290",width="499"}}

 * We want to create three categories of events, based on their numerical codes:
  * '''Famous faces''': 5 (00101), 6 (00110), 7 (00111) => Bit 3 only
  * '''Unfamiliar faces''': 13 (01101), 14 (01110), 15 (01111) => Bit 3 and 4
  * '''Scrambled images''': 17 (10001), 18 (10010), 19 (10011) => Bit 5 only
  * We will start by creating the category "Unfamiliar" (combination of events "3" and "4") and remove the remove the initial events. Then we just have to rename the remaining "3" in "Famous", and all the "5" in "Scrambled".
 * Add process '''Events > Group by name''': "'''Unfamiliar=3,4'''", Delay=0, '''Delete original events'''
 * Add process '''Events > Rename event''': 3 => Famous
 * Add process '''Events > Rename event''': 5 => Scrambled <<BR>><<BR>> {{attachment:events_merge.gif||height="402",width="532"}}
 * Add process '''Events > Add time offset''' to correct for the presentation delays:<<BR>>Event names: "'''Famous, Unfamiliar, Scrambled'''", Time offset = '''34.5ms'''<<BR>><<BR>> {{attachment:events_offset.gif||height="355",width="348"}}

 * Finally run the script. Double-click on the recordings to make sure the labels were detected correctly. You can delete the unwanted events that are left in the recordings (1,2,9,13): <<BR>><<BR>> {{attachment:events_display.gif||height="194",width="595"}}

== Pre-processing ==
=== Spectral evaluation ===
 * Keep the "Link to raw file" in Process1.
 * Run process '''Frequency > Power spectrum density (Welch)''' with the options illustrated below. <<BR>><<BR>> {{attachment:psd_process.gif||height="314",width="669"}}
 * Right-click on the PSD file > Power spectrum.  <<BR>><<BR>> {{attachment:psd_plot.gif||height="315",width="546"}}
 * Observations:
  * Three groups of sensors, from top to bottom: EEG, MEG gradiometers, MEG magnetometers.
  * Power lines: '''50 '''Hz and harmonics
  * Alpha peak around 10 Hz
  * Artifacts due to Elekta electronics: '''293'''Hz, '''307'''Hz, '''314'''Hz, '''321'''Hz, '''328'''Hz.
  * Suspected bad EEG channels: '''EEG016'''
 * Close all the windows.

=== Remove line noise ===
 * Keep the "Link to raw file" in Process1.
 * Select process '''Pre-process > Notch filter''' to remove the line noise (50-200Hz).<<BR>>Add the process '''Frequency > Power spectrum density (Welch)'''. <<BR>><<BR>> {{attachment:notch_process.gif||height="262",width="562"}}
 * Double-click on the PSD for the new continuous file to evaluate the quality of the correction. <<BR>><<BR>> {{attachment:notch_result.gif||height="215",width="566"}}
 * Close all the windows (use the [X] button at the top-right corner of the Brainstorm window).

=== EEG reference and bad channels ===
 * Right-click on link to the processed file ("Raw | notch(50Hz ...") > '''EEG > Display time series'''.
 * Select channel '''EEG016''' and mark it as '''bad''' (using the popup menu or pressing the Delete key). <<BR>><<BR>> {{attachment:channel_bad.gif||height="214",width="587"}}
 * In the Record tab, menu '''Artifacts > Re-reference EEG''' > "AVERAGE". <<BR>><<BR>> {{attachment:channel_ref.gif||height="270",width="535"}}
 * At the end, the window "select active projectors" is open to show the new re-referencing projector. Just close this window. To get it back, use the menu Artifacts > Select active projectors.

== Artifact detection ==
=== Heartbeats: Detection ===
 * Empty the Process1 list (right-click > Clear list).
 * Drag and drop the continuous processed file ("Raw | notch(50Hz...)") to the Process1 list.
 * Run process''' Events > Detect heartbeats''': Channel name='''EEG063''', All file, Event name=cardiac <<BR>><<BR>> {{attachment:detect_cardiac.gif||height="230",width="310"}}

 * In many of the other tutorials, we detect the blinks and remove them with SSP. In this experiment, we are particularly interested in the subject's response to seeing the stimulus. Therefore we will exclude from the analysis all the recordings contaminated with blinks or other eye movements.

 * Run process '''Artifacts > Detect events above threshold''': <<BR>>Event name='''blink_BAD''', Channel='''EEG062''', All file,  Maximum threshold='''100''', Threshold units='''uV''', Filter='''[0.30,20.00]Hz''',  '''Use absolute value of signal'''.<<BR>><<BR>> {{attachment:detect_blinks.gif||height="455",width="285"}}

 * Inspect visually the two new cateogries of events: cardiac and blink_BAD.<<BR>><<BR>> {{attachment:detect_display.gif||height="237",width="655"}}

 * Close all the windows (using the [X] button).

=== Heartbeats: Correction with SSP ===
 * Keep the "Raw | notch" file selected in Process1.
 * Select process '''Artifacts > SSP: Heartbeats''' >  Sensor type: '''MEG MAG'''
 * Add process '''Artifacts > SSP: Heartbeats''' >  Sensor type: '''MEG GRAD''' - Run the execution <<BR>><<BR>> {{attachment:ssp_ecg_process.gif||height="225",width="293"}}
 * Double-click on the continuous file to show all the MEG sensors. <<BR>>In the Record tab, select sensors "Left-temporal".
 * Menu Artifacts > Select active projectors.
  * In category '''cardiac/MEG MAG''': Select '''component #1''' and view topography.
  * In category '''cardiac/MEG GRAD''': Select '''component #1 '''and view topography. <<BR>><<BR>> {{attachment:ssp_ecg_topo.gif||height="175",width="604"}}

 * Make sure that selecting the two components removes the cardiac artifact. Then click '''[Save]'''.

=== Additional bad segments ===
 * Process1>Run: Select the process "'''Events > Detect other artifacts'''". This should be done separately for MEG and EEG to avoid confusion about which sensors are involved in the artifact. <<BR>><<BR>> {{attachment:detect_other.gif||height="253",width="486"}}
 * Display the MEG sensors. Review the segments that were tagged as artifact, determine if each event represents an artifact and then mark the time of the artifact as BAD. This can be done by selecting the time window around the artifact, then right-click > '''Reject time segment'''. Note that this detection process marks 1-second segments but the artifact can be shorter.<<BR>><<BR>> {{attachment:artifacts_mark_bad.png||width="640"}}

 * Once all the events in the two categories are reviewed and bad segments are marked, the two categories (1-7Hz and 40-240Hz) can be deleted.
 * Do this detection and review again for the EEG.

=== SQUID jumps ===
MEG signals recorded with Elekta-Neuromag systems frequently contain SQUID jumps ([[http://neuroimage.usc.edu/brainstorm/Tutorials/BadSegments?highlight=(squid)#Elekta-Neuromag_SQUID_jumps|more information]]). These sharp steps followed by a change of baseline value are easy to identify visually but more complicated to detect automatically.

The process "Detect other artifacts" usually detects most of them in the category "1-7Hz". If you observe that some are skipped, you can try re-running it with a higher sensitivity. It is important to review all the sensors and all the time in each run to be sure these events are marked as bad segments.

 . {{attachment:detect_jumps.gif||height="214",width="547"}}

== Epoching and averaging ==
=== Import epochs ===
 * Keep the "Raw | notch" file selected in Process1.

 * Select process: '''Import recordings > Import MEG/EEG: Events''' (do not run immediately)<<BR>>Event names "Famous, Unfamiliar, Scrambled", All file, Epoch time=[-500,1200]ms
 * Add process: '''Pre-process > Remove DC offset''': Baseline=[-500,-0.9]ms - Run execution.<<BR>><<BR>> {{attachment:import_epochs.gif||height="399",width="647"}}

=== Average by run ===
 * In Process1, select all the imported trials.
 * Run process: '''Average > Average files''': '''By trial groups (folder average)''' <<BR>><<BR>>  {{attachment:average_process.gif||height="504",width="478"}}

=== Review EEG ERP ===
 * EEG evoked response (famous, scrambled, unfamiliar): <<BR>><<BR>> {{attachment:average_topo.gif||height="392",width="684"}}
 * Open the [[Tutorials/ChannelClusters|Cluster tab]] and create a cluster with the channel '''EEG065''' (button [NEW IND]).<<BR>><<BR>> {{attachment:cluster_create.gif||height="166",width="569"}}
 * Select the cluster, select the three average files, right-click > '''Clusters time series''' (Overlay:Files). <<BR>><<BR>> {{attachment:cluster_display.gif||height="220",width="658"}}
 * Basic observations for EEG065 (right parieto-occipital electrode):
  * Around 170ms (N170): greater negative deflection for Famous than Scrambled faces.
  * After 250ms: difference between Famous and Unfamiliar faces.

== Source estimation ==
=== MEG noise covariance: Empty room recordings ===
The minimum norm model we will use next to estimate the source activity  can be improved by modeling the the noise contaminating the data. The  section shows how to [[Tutorials/NoiseCovariance|estimate the noise covariance]]  in different ways for EEG and MEG. For the MEG recordings we will use  the empty room measurements we have, and for the EEG we will compute it  from the pre-stimulus baselines we have in all the imported epochs.

 * Create a new subject: '''emptyroom'''
 * Right-click on the new subject > '''Review raw file'''.<<BR>>Select file: '''sample_group/emptyroom/090707_raw_st.fif '''<<BR>>Do not apply default transformation, Ignore event channel.<<BR>><<BR>> {{attachment:noise_review.gif||height="164",width="506"}}
 * Select this new file in Process1 and run process '''Pre-process > Notch filter''': '''50 100 150 200Hz'''. When using empty room measurements to compute the noise covariance, they must be processed exactly in the same way as the other recordings. <<BR>><<BR>> {{attachment:noise_notch.gif||height="225",width="436"}}
 * Right-click on the filtered noise recordings > '''Noise covariance > Compute from recordings''': <<BR>><<BR>> {{attachment:noise_compute.gif||height="240",width="649"}}
 * Right-click on the '''Noise covariance > Copy to other subjects '''<<BR>><<BR>> {{attachment:noise_copy.gif||height="126",width="273"}}

=== EEG noise covariance: Pre-stimulus baseline ===
 * In folder sub002/run_01_sss_notch, select all the imported the imported trials, right-click > '''Noise covariance > Compute from recordings''',  Time=[-500,-0.9]ms, '''EEG only''', '''Merge'''. {{attachment:noise_eeg.gif||height="280",width="650"}}
 * This computes the noise covariance only for EEG, and combines it with the existing MEG information. {{attachment:noise_display.gif||height="223",width="375"}}

=== BEM layers ===
We will compute a [[Tutorials/TutBem|BEM forward model]]  to estimate the brain sources from the EEG recordings. For this, we  need some layers defining the separation between the different  tissues of the head (scalp, inner skull, outer skull).

 * Go to the anatomy view (first button above the database explorer).
 * Right-click on the subject folder > '''Generate BEM surfaces''':  The number of vertices to use for each layer depends on your computing  power and the accuracy you expect. You can try for instance with '''1082 vertices '''(scalp) and '''642 vertices '''(outer skull and inner skull). <<BR>><<BR>> {{attachment:anatomy_bem.gif||height="232",width="603"}}

=== Forward model: EEG and MEG ===
 * Go back to the functional view (second button above the database explorer).
 * Model used: Overlapping spheres for MEG, OpenMEEG BEM for EEG ([[Tutorials/HeadModel|more information]]).
 * In folder sub002/run_01_sss_notch, right-click on the channel file > '''Compute head model'''.<<BR>>Keep all the default options . Expect this to take a while...<<BR>><<BR>> {{attachment:headmodel_compute.gif||height="313",width="643"}}

=== Inverse model: Minimum norm estimates ===
 * Right-click on the new head model > '''Compute sources [2016]''': '''MEG MAG + GRAD''' (default options) <<BR>><<BR>> {{attachment:sources_compute.gif||height="406",width="626"}}

 * Right-click on the new head model > '''Compute sources [2016]''': '''EEG''' (default bad channels).

 * At the end we have two inverse operators, that are shared for all the files of the run (single trials and averages). If we wanted to look at the run-level source averages, we could normalize the source maps with a Z-score wrt baseline. In this tutorial, we will first average across runs and normalize the subject-level averages. This will be done in the next tutorial (group analysis).<<BR>><<BR>> {{attachment:sources_files.gif||height="147",width="243"}}

== Time-frequency analysis ==
We will compute the time-frequency decomposition of each trial using Morlet wavelets, and average the power of the Morlet coefficients for each condition and each run separately.

 * In Process1, select the imported trials "Famous" for run#01.
 * Run process '''Frequency > Time-frequency (Morlet wavelets)'''<<BR>>Sensor types=MEG,EEG, Not normalized, Frequency='''Log(6:40:80)''', Measure=Power, '''Save average''' <<BR>><<BR>> {{attachment:tf_process.gif||height="479",width="628"}}
 * Double-click on the file to display it. <<BR>><<BR>>{{attachment:tf_display.gif}}
 * Repeat this operation for other conditions (Scrambled and Unfamiliar) and the other runs (2-6). There is no way with this process to compute all the averages at once, as we did with the process "Average files". This operation will be easier to run from a script.
 * If we wanted to look at the run-level source averages, we could normalize these time-frequency maps.  In this tutorial, we will first average across runs and normalize the  subject-level averages. This will be done in the next tutorial (group  analysis).

== Scripting ==
We have now all the files we need for the group analysis ([[Tutorials/VisualGroup|next tutorial]]). We need to repeat the same operations for all the runs and all the subjects. Some of these steps are '''fully automatic''' and take a lot of time (filtering, computing the forward model), they should be executed from a script.

However, we recommend you always '''review manually''' some of the pre-processing steps (selection of the bad segments and bad channels, SSP/ICA components). Do not trust blindly any fully automated cleaning procedure.

For the strict reproducibility of this analysis, we provide a script that processes all the 19 subjects:  '''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 >)>>

<<BR>>You should note that this is not the result of a fully automated procedure. The bad channels were identified manually and are defined for each run in the script. The bad segments were detected automatically, confirmed manually for each run and saved in external files distributed with the Brainstorm package sample_group_freesurfer.zip (sample_group/brainstorm/bad_segments/*.mat).

All the process calls (bst_process) were generated automatically using with the script generator (menu '''Generate .m script''' in the pipeline editor). Everything else was added manually (loops, bad channels, file copies).

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

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