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MEG visual tutorial: Single subject (BIDS)

[WORK IN PROGRESS: THIS TUTORIAL IS NOT READY FOR PUBLIC USE]

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". We use here a recent update of this dataset, reformatted to follow the Brain Imaging Data Structure (BIDS), a standard for neuroimaging data organization. It is part of a collective effort to document and standardize MEG/EEG group analysis, see Frontier's research topic: From raw MEG/EEG to publication: how to perform MEG/EEG group analysis with free academic software.

The data processed here consists of simultaneous MEG/EEG recordings from 16 participants performing a simple visual recognition task from presentations of famous, unfamiliar and scrambled faces. The analysis is split in two tutorial pages: the present tutorial describes the detailed interactive analysis of one single subject; the second tutorial describes batch processing and group analysis of all 16 participants.

Note that the operations used here are not detailed, the goal of this tutorial is not to introduce Brainstorm to new users. For in-depth explanations of the interface and theoretical foundations, please refer to the introduction tutorials.

Contents

License
Presentation of the experiment
Download and installation [TODO]
Import the anatomy
Access the recordings
Pre-processing
Artifact detection
Epoching and averaging
Source estimation [TODO]
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 regarding the data, please contact: rik.henson@mrc-cbu.cam.ac.uk

Presentation of the experiment

Experiment

16 subjects (the original version of the dataset included 19 subjects, but 3 were excluded from the group analysis for various reasons)
6 acquisition runs of approximately 10mins for each subject
Presentation of series of images: familiar faces, unfamiliar faces, phase-scrambled faces
Participants had 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.
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
MaxFilter/tSSS: The data repository includes both the raw MEG recordings and the data cleaned using Signal-Space Separation as implemented in Elekta MaxFilter 2.2. The data was collected with continuous head localization and cannot be processed easily without running the algorithms SSS or tSSS. Brainstorm currently does not offer any free alternative to MaxFilter, therefore in this tutorial we will import the recordings already processed with MaxFilter's tSSS, available in the "derivatives" folder of the BIDS architecture. To save disk space, will not download the raw MEG recordings. This means we will not get any of the additional files available in the BIDS structure (headshape, events, channels) but it doesn't matter because all the information is directly available in the .fif files.
The data 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 was removed from the structural images for anonymization purposes.
Processed with FreeSurfer 5.3.

Download and installation [TODO]

First, make sure you have enough space on your hard drive, at least 350Gb:
- Raw files: 100Gb
- Processed files: 250Gb
The data is hosted on the OpenfMRI website: https://openfmri.org/dataset/ds000117/
Download the following files for revision 1.0.0 (approximately 70Gb):
- Derivatives for subjects 01-04 (16.6 GB)
- Derivatives for subjects 05-08 (16.1 GB)
- Derivatives for subjects 09-12 (16.6 GB)
- Derivatives for subjects 13-16 (16.5 GB)
- Metadata (235.4 KB)
- FreeSurfer segmentation: NOT AVAILABLE YET [TODO]
- Empty room recordings: NOT AVAILABLE YET [TODO]
Unzip all the .zip files in the same folder.
Reminder: Do not save 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 dataset is formatted following the BIDS-MEG specifications, therefore we could import all the relevant information (MRI, FreeSurfer segmentation, MEG+EEG recordings) in just one click, with the menu File > Load protocol > Import BIDS dataset, as described in the online tutorial MEG resting state & OMEGA database. However, because your own data might not be organized following the BIDS standards, in this tutorial we preferred illustrating all the detailed steps for importing the data rather than the BIDS shortcut.

This page explains how to import and process the first run of subject #01 only. All the other files will have to be processed in the same way.

Switch to the "anatomy" view.
Right-click on the TutorialVisual folder > New subject > sub-01
- 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: derivatives/freesurfer/sub-01
- Number of vertices of the cortex surface: 15000 (default value)
- When asked to select the anatomical fiducials, click on "Compute MNI transformation". This will register the MRI volume to an MNI template with an affine transformation, using SPM functions embedded in Brainstorm (spm_maff8.m). This will also place default fiducial points NAS/LPA/RPA in the MRI, based on MNI coordinates. We will use head points for the MEG-MRI coregistration, therefore we don't need accurate anatomical positions here.
- Note that if you don't have a good digitized head shape for the subject, or if the final registration MEG-MRI doesn't look good with this head shape, you should not skip this step and mark accurate NAS/LPA/RPA fiducial points in the MRI, using the same anatomical convention that was used during the MRI acquisition.
- Then click on [Save] to start the import.
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)"
- Go to the folder: derivatives/meg-derivatives/sub-01/ses-meg/meg
- Select file: sub-01_ses-meg_task-facerecognition_run-01_proc-tsss_meg.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 (or any other non-informative type). 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, which are not even accurately set (we used default MNI positions). All the MRI scans were anonymized (defaced) so all the head points below the nasion cannot be used. We will try to refine this registration using the additional head points that were digitized above the nasion.

Right-click on the channel file > Digitized head points > Remove points below nasion.
(45 points removed, 92 head points left)
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). Edit the colors if you don't like the default ones.

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.
The MEG sensors look awful, because of one small segment of data located around 248s. Open the MEG recordings and scroll to 248s (just before the first Unfamiliar event).
In these recordings, the continuous head tracking was activated, but it starts only at the time of the stimulation (248s) while the acquisition of the data starts 20s before (226s). The first 20s do not have head localization coils (HPI) coils activity and are not corrected by MaxFilter. After 248s, the HPI coils are on, and MaxFilter filters them out. The transition between the two states is not smooth and creates important distortions in the spectral domain. For a proper evaluation of the recordings, we should compute the PSD only after the HPI coils are turned on.
Run process Events > Detect cHPI activity (Elekta). This detects the changes in the cHPI activity from channel STI201 and marks all the data without head localization as bad.
Re-run the process Frequency > Power spectrum density (Welch). All the bad segments are excluded from the computation, therefore the PSD is now estimated only with the data after 248s.
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 (HPI coils): 293Hz, 307Hz, 314Hz, 321Hz, 328Hz.
- Peak from unknown source at 103.4Hz in the MEG only.
- 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

Eye blinks: Detection

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 Events > 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.
In many other tutorials, we use the process "Detect eye blinks", which creates simple events indicating the peak of the EOG signal during a blink. In this study, we preferred using this process "Detect events above threshold" because it creates extended events marking as bad all the segment for which the EOG value is above a given threshold. It's more manual but it makes sure we really exclude all the ocular activity.
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), low-pass filter at 32Hz:
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 [TODO]

MEG noise covariance: Empty room recordings [TODO]

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 introduction tutorials explain 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: sub-emptyroom/ses-meg/meg/090707_raw_st.fif (processed with MaxFilter/tSSS)
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 (same MaxFilter parameters, same frequency filters).
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 sub-01/run-01_tsss_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 sub-01/run_01_tsss_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. We will restrict the computation to the MEG magnetometers and the EEG channels to limit the computation time and disk usage.

In Process1, select the imported trials "Famous" for run#01.
Run process Frequency > Time-frequency (Morlet wavelets): Sensor types=MEG MAG,EEG
Not normalized, Frequency=Log(6:20:60), Measure=Power, Save average
Double-click on the file to display it. In the Display tab, select the option "Hide edge effects" to exclude from the display all the values that could not be estimated in a reliable way. Let's extract only the good values from this file (-200ms to +900ms).
In Process1, select the time-frequency file.
Run process Extract > Extract time: Time window=[-200, 900]ms, Overwrite input files
Display the file again, observe that all the possibly bad values are gone.
You can display all the sensors at once (MEG MAG or EEG): right-click > 2D Layout (maps).
Repeat these steps 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 it is with the process "Average files". This 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 (execution time: 10-30 hours)
Report for the first subject: report_TutorialVisual_sub-01.html

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], ...
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         [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, then exported as text ans copied at the end of this script.

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 25 as of 2015-07-24 23:27:03 → 
  Size: 9414
  Editor: FrancoisTadel
  Comment:
+   ← Revision 233 as of 2017-12-06 14:13:33 → ⇥
  Size: 31839
  Editor: FrancoisTadel
  Comment:
-Deletions are marked like this.
+Additions are marked like this.
 Line 1:
-= MEG visual tutorial: Single subject =
+= MEG visual tutorial: Single subject (BIDS) =
'''[WORK IN PROGRESS: THIS TUTORIAL IS NOT READY FOR PUBLIC USE]'''
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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]]". We use here a recent update of this dataset, reformatted to follow the [[http://bids.neuroimaging.io|Brain Imaging Data Structure (BIDS)]], a standard for neuroimaging data organization. It is part of a collective effort to document and standardize MEG/EEG group analysis, see Frontier's research topic: [[https://www.frontiersin.org/research-topics/5158|From raw MEG/EEG to publication: how to perform MEG/EEG group analysis with free academic software]].

The data processed here consists of simultaneous MEG/EEG recordings from 16 participants performing a simple visual recognition task from presentations of famous, unfamiliar and scrambled faces. The analysis is split in two tutorial pages: the present tutorial describes the detailed interactive analysis of one single subject; the second tutorial describes batch processing and [[Tutorials/VisualGroup|group analysis of all 16 participants]].

Note that the operations used here are not detailed, the goal of this tutorial is not to introduce Brainstorm to new users. For in-depth explanations of the interface and theoretical foundations, 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)

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

Any questions regarding the data, please contact: rik.henson@mrc-cbu.cam.ac.uk
-Line 19:
+Line 21:
- * 16 subjects
 * 6 runs (sessions) of approximately 10mins for each subject
+ * 16 subjects (the original version of the dataset included 19 subjects, but 3 were excluded from the group analysis for [[http://neuroimage.usc.edu/brainstorm/Tutorials/VisualSingleOrig#Bad_subjects|various reasons]])
 * 6 acquisition runs of approximately 10mins for each subject
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+Line 24:
- * The subject has to judge the left-right symmetry of each stimulus
 * Total of nearly 300 trials in total for each of the 3 conditions
+ * Participants had to judge the left-right symmetry of each stimulus
 * Total of nearly 300 trials for each of the 3 conditions
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+Line 33:
- * 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.
 * The distribution 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).
+ * 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'''
 * MaxFilter/tSSS: The data repository includes both the raw MEG recordings and the data cleaned using Signal-Space Separation as implemented in Elekta MaxFilter 2.2. The data was collected with continuous head localization and cannot be processed easily without running the algorithms SSS  or tSSS. Brainstorm currently does not offer any free alternative to  MaxFilter, therefore in this tutorial we will import the  recordings already processed with MaxFilter's tSSS, available in the  "derivatives" folder of the BIDS architecture. To save disk space, will  not download the raw MEG recordings. This means we will not get any of  the additional files available in the BIDS structure (headshape, events,  channels) but it doesn't matter because all the information is directly  available in the .fif files.
 * The data 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

== Download and installation ==
 * 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''': MEG empty room measurements.
  * '''SubXX/MEEG''': MEG and EEG recordings in FIF format and triggers definition files.
  * '''Publications''': Reference publications related with this dataset.
  * '''README.TXT''': License and dataset description.
 * The FreeSurfer segmentations of the T1 images are not part of this package. 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_anat.zip'''<<BR>>Unzip this file in the same folder where you downloaded all the datasets.
 * Reminder: Do not put the downloaded files in the Brainstorm  folders (program or database folders).
+ * MRI data acquired on a 3T Siemens TIM Trio: 1x1x1mm T1-weighted structural MRI.
 * The face was removed from the structural images for anonymization purposes.
 * Processed with FreeSurfer 5.3.

== Download and installation [TODO] ==
 * First, make sure you have enough space on your hard drive, at least '''350Gb''':
  * Raw files: '''100Gb'''
  * Processed files: '''250Gb'''
 * The data is hosted on the OpenfMRI website: https://openfmri.org/dataset/ds000117/
 * Download the following files for revision 1.0.0 (approximately 70Gb):
  * [[https://s3.amazonaws.com/openneuro/ds000117/ds000117_R1.0.0/compressed/ds000117_R1.0.0_derivatives_sub01-04.zip|Derivatives for subjects 01-04 (16.6 GB)]]
  * [[https://s3.amazonaws.com/openneuro/ds000117/ds000117_R1.0.0/compressed/ds000117_R1.0.0_derivatives_sub05-08.zip|Derivatives for subjects 05-08 (16.1 GB)]]
  * [[https://s3.amazonaws.com/openneuro/ds000117/ds000117_R1.0.0/compressed/ds000117_R1.0.0_derivatives_sub09-12.zip|Derivatives for subjects 09-12 (16.6 GB)]]
  * [[https://s3.amazonaws.com/openneuro/ds000117/ds000117_R1.0.0/compressed/ds000117_R1.0.0_derivatives_sub13-16.zip|Derivatives for subjects 13-16 (16.5 GB)]]
  * [[https://s3.amazonaws.com/openneuro/ds000117/ds000117_R1.0.0/compressed/ds000117_R1.0.0_metadata.zip|Metadata (235.4 KB)]]
  * FreeSurfer segmentation: '''NOT AVAILABLE YET [TODO]'''
  * Empty room recordings: '''NOT AVAILABLE YET ''''''[TODO]'''

 * Unzip all the .zip files in the same folder.<<BR>>'''Reminder''': Do not save the downloaded files in the Brainstorm  folders (program or database folders)
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+This dataset is formatted following the [[https://www.biorxiv.org/content/early/2017/08/08/172684|BIDS-MEG specifications]], therefore we could import all the relevant information (MRI, FreeSurfer segmentation, MEG+EEG recordings) in just one click, with the menu '''File > Load protocol''' > '''Import BIDS dataset''', as described in the online tutorial [[http://neuroimage.usc.edu/brainstorm/Tutorials/RestingOmega#BIDS_specifications|MEG resting state & OMEGA database]]. However, because your own data might not be organized following the BIDS standards, in this tutorial we preferred illustrating all the detailed steps for importing the data rather than the BIDS shortcut.

This page explains how to import and process the first run of '''subject''' '''#01''' only. All the other files will have to be processed in the same way.
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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 subject node > Import anatomy folder:
+ * Right-click on the TutorialVisual folder > New subject > '''sub-01'''
  * Leave the default options you defined for the protocol.
 * Right-click on the subject node > '''Import anatomy folder''':
-Line 60:
+Line 78:
-  * Select the folder: '''Anatomy/Sub01'''  (from sample_group_anat.zip)
+  * Select the folder: '''derivatives/freesurfer/sub-01'''
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+Line 80:
- * The two sets of fiducials we usually have to define interactively are here set automatically.
  * '''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 [[CoordinateSystems|coordinates systems page]].
  * '''AC/PC/IH''': Identified automatically 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.<<BR>><<BR>> {{attachment:anatomy_import.gif||height="384",width="613"}}

 * All the anatomical atlases [[Tutorials/LabelFreeSurfer|generated by FreeSurfer]] were imported automatically: the surface-based cortical atlases and the atlas of sub-cortical regions (ASEG). <<BR>><<BR>> {{attachment:anatomy_atlas.gif||height="211",width="550"}}
+  * When asked to select the anatomical fiducials, click on "'''Compute MNI transformation'''". This will register the MRI volume to an MNI template with an affine transformation, using SPM functions embedded in Brainstorm (spm_maff8.m). This will also place default fiducial points NAS/LPA/RPA in the MRI, based on MNI coordinates. We will use head points for the MEG-MRI coregistration, therefore we don't need accurate anatomical positions here.
  * Note that if you don't have a good digitized head shape for the subject, or if the final registration MEG-MRI doesn't look good with this head shape, you should not skip this step and mark accurate NAS/LPA/RPA fiducial points in the MRI, using the same anatomical convention that was used during the MRI acquisition.
  * Then click on [Save] to start the import.

 * 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.<<BR>><<BR>> {{attachment:anatomy_import.gif||width="613",height="384"}}

 * 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||width="550",height="211"}}
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+We need to attach the continuous .fif files containing the recordings to the database.
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+Line 95:
-  * Select the '''first''' FIF files in: '''Sub01/MEEG''' <<BR>><<BR>> {{attachment:review_raw.gif}}
 * Events:  '''SCREEN CAPTURES '''
 * 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.
 * Right-click on the channel file > '''Digitized head points > Remove points below nasion'''.<<BR>><<BR>>{{attachment:channel_remove.gif}}
 * Right-click on the channel file > '''MRI registration > Remove points below nasion'''.<<BR>><<BR>>{{attachment:channel_refine.gif}}
 * MEG/MRI registration, before (left) and after (right) this automatic registration procedure: {{attachment:registration.gif||height="222",width="251"}}

 *

=== 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.
+  * Go to the folder: '''derivatives/meg-derivatives/sub-01/ses-meg/meg'''
  * Select file: '''sub-01_ses-meg_task-facerecognition_run-01_proc-tsss_meg.fif''' <<BR>><<BR>> {{attachment:review_raw.gif||width="583",height="205"}}
 * Events:''' Ignore'''. We will read the stimulus triggers later.<<BR>><<BR>> {{attachment:review_ignore.gif||width="330",height="186"}}
 * 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''' (or any other non-informative type). Close the window and save the modifications. <<BR>><<BR>> {{attachment:channel_edit.gif||width="561",height="252"}}

=== MRI registration ===
At this point, the registration MEG/MRI is based only on the three anatomical landmarks NAS/LPA/RPA, which are not even accurately set (we used default MNI positions). All the MRI scans were anonymized (defaced) so all the head points below the nasion cannot be used. We will try to refine this registration using the additional head points that were digitized above the nasion.

 * Right-click on the channel file > '''Digitized head points > Remove points below nasion'''. <<BR>>(45 points removed, 92 head points left)<<BR>><<BR>> {{attachment:channel_remove.gif||width="329",height="194"}}
 * Right-click on the channel file > '''MRI registration > Refine using head points'''.<<BR>><<BR>> {{attachment:channel_refine.gif||width="294",height="173"}}
 * MEG/MRI registration, before (left) and after (right) this automatic registration procedure:  <<BR>><<BR>> {{attachment:registration.gif||width="236",height="209"}} {{attachment:registration_final.gif||width="237",height="208"}}
 * 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||width="477",height="207"}}

=== 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||width="499",height="290"}}

 * 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||width="532",height="402"}}
 * 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||width="348",height="355"}}

 * 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). Edit the colors if you don't like the default ones.<<BR>><<BR>> {{attachment:events_display.gif||width="595",height="194"}}

== 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||width="669",height="314"}}
 * Right-click on the PSD file > Power spectrum.  <<BR>><<BR>> {{attachment:psd_plot.gif||width="408",height="235"}}
 * The MEG sensors look awful, because of one small segment of data located around 248s. Open the MEG recordings and scroll to 248s (just before the first Unfamiliar event). <<BR>><<BR>> {{attachment:psd_error.gif||width="527",height="183"}}
 * In these recordings, the continuous head tracking was activated, but it starts only at the time of the stimulation (248s) while the acquisition of the data starts 20s before (226s). The first 20s do not have head localization coils (HPI) coils activity and are not corrected by MaxFilter. After 248s, the HPI coils are on, and MaxFilter filters them out. The transition between the two states is not smooth and creates important distortions in the spectral domain. For a proper evaluation of the recordings, we should '''compute the PSD only after the HPI coils are turned on'''.
 * Run process '''Events > Detect cHPI activity (Elekta)'''. This detects the changes in the cHPI activity from channel STI201 and marks all the data without head localization as bad. <<BR>><<BR>> {{attachment:psd_badsegment.gif||width="503",height="234"}}
 * Re-run the process  '''Frequency > Power spectrum density (Welch)'''. All the bad segments are excluded from the computation, therefore the PSD is now estimated only with the data '''after 248s'''.  <<BR>><<BR>> {{attachment:psd_fix.gif||width="668",height="283"}}
 * 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 (HPI coils): '''293'''Hz, '''307'''Hz, '''314'''Hz, '''321'''Hz, '''328'''Hz.
  * Peak from unknown source at '''103.4Hz''' in the MEG only.
  * 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||width="562",height="262"}}
 * Double-click on the PSD for the new continuous file to evaluate the quality of the correction. <<BR>><<BR>> {{attachment:notch_result.gif||width="566",height="215"}}
 * 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||width="587",height="214"}}
 * In the Record tab, menu '''Artifacts > Re-reference EEG''' > "AVERAGE". <<BR>><<BR>> {{attachment:channel_ref.gif||width="535",height="270"}}
 * 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||width="310",height="230"}}

=== Eye blinks: Detection ===
 * 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 '''Events > 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||width="285",height="455"}}

 * In many other tutorials, we use the process "Detect eye blinks", which creates simple events indicating the peak of the EOG signal during a blink. In this study, we preferred using this process "Detect events above threshold" because it creates extended events marking as bad all the segment for which the EOG value is above a given threshold. It's more manual but it makes sure we really exclude all the ocular activity.

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

 * 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||width="293",height="225"}}
 * 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||width="604",height="175"}}

 * 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||width="486",height="253"}}
 * 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||width="547",height="214"}}

== 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||width="647",height="399"}}

=== 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||width="478",height="504"}}

=== Review EEG ERP ===
 * EEG evoked response (famous, scrambled, unfamiliar), low-pass filter at 32Hz: <<BR>><<BR>> {{attachment:average_topo.gif||width="684",height="392"}}
 * Open the [[Tutorials/ChannelClusters|Cluster tab]] and create a cluster with the channel '''EEG065''' (button [NEW IND]).<<BR>><<BR>> {{attachment:cluster_create.gif||width="569",height="166"}}
 * Select the cluster, select the three average files, right-click > '''Clusters time series''' (Overlay:Files). <<BR>><<BR>> {{attachment:cluster_display.gif||width="658",height="220"}}
 * 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 [TODO] ==
=== MEG noise covariance: Empty room recordings [TODO] ===
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 introduction tutorials explain 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: '''sub-emptyroom/ses-meg/meg/090707_raw_st.fif '''(processed with MaxFilter/tSSS)<<BR>>Do not apply default transformation, Ignore event channel.<<BR>><<BR>> {{attachment:noise_review.gif||width="506",height="164"}}
 * 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 (same MaxFilter parameters, same frequency filters). <<BR>><<BR>> {{attachment:noise_notch.gif||width="436",height="225"}}
 * Right-click on the filtered noise recordings > '''Noise covariance > Compute from recordings''': <<BR>><<BR>> {{attachment:noise_compute.gif||width="649",height="240"}}
 * Right-click on the '''Noise covariance > Copy to other subjects '''<<BR>><<BR>> {{attachment:noise_copy.gif||width="273",height="126"}}

=== EEG noise covariance: Pre-stimulus baseline ===
 * In folder sub-01/run-01_tsss_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||width="650",height="280"}}
 * This computes the noise covariance only for EEG, and combines it with the existing MEG information. {{attachment:noise_display.gif||width="375",height="223"}}

=== 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||width="603",height="232"}}

=== 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 sub-01/run_01_tsss_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||width="643",height="313"}}

=== 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||width="626",height="406"}}

 * 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||width="243",height="147"}}

== 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. We will restrict the computation to the MEG magnetometers and the EEG channels to limit the computation time and disk usage.

 * In Process1, select the imported trials "Famous" for run#01.
 * Run process '''Frequency > Time-frequency (Morlet wavelets)''': Sensor types=MEG MAG,EEG <<BR>>Not normalized, Frequency='''Log(6:20:60)''', Measure=Power, '''Save average''' <<BR>><<BR>> {{attachment:tf_process.gif||width="628",height="479"}}
 * Double-click on the file to display it. In the Display tab, select the option "Hide edge effects" to exclude from the display all the values that could not be estimated in a reliable way. Let's extract only the good values from this file (-200ms to +900ms). <<BR>><<BR>> {{attachment:tf_display.gif||width="270",height="155"}}
 * In Process1, select the time-frequency file.
 * Run process '''Extract > Extract time''': Time window='''[-200, 900]ms''', '''Overwrite input files''' <<BR>><<BR>> {{attachment:tf_cut.gif||width="411",height="197"}}
 * Display the file again, observe that all the possibly bad values are gone. <<BR>><<BR>> {{attachment:tf_cutdisplay.gif||width="410",height="178"}}
 * You can display all the sensors at once (MEG MAG or EEG): right-click > 2D Layout (maps).<<BR>><<BR>> {{attachment:tf_2dlayout.gif||width="653",height="270"}}
 * Repeat these steps 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 it is with the process "Average files". This 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''' (execution time: 10-30 hours)<<BR>>Report for the first subject: [[http://neuroimage.usc.edu/bst/examples/report_TutorialVisual_sub-01.html|report_TutorialVisual_sub-01.html]]

<<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, then exported as text ans copied at the end of this script.

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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