MEG visual tutorial: Single subject (BIDS)

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.

License

This dataset was obtained from the OpenNeuro project (http://openneuro.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

For citing the analysis, the processing pipeline is published in this article:
Tadel F, Bock E, Niso G, Mosher JC, Cousineau M, Pantazis D, Leahy RM, Baillet S, MEG/EEG Group Analysis With Brainstorm, Frontiers in Neuroscience, Feb 2019

Presentation of the experiment

Experiment

MEG acquisition

Subject anatomy

Download and installation

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. Plus, we will need some additional steps that are not part of the standard import, because of the data anonymization (MRI were defaced and acquisition dates removed).

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.

Access the recordings

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

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.

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.

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.

Pre-processing

Spectral evaluation

Remove line noise

EEG reference and bad channels

Artifact detection

Heartbeats: Detection

Eye blinks: Detection

Heartbeats: Correction with SSP

Additional bad segments

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

Average by run

Review EEG ERP

Source estimation

MEG noise covariance: Empty room recordings

The minimum norm model we will use next to estimate the source activity can be improved by modeling the the noise contaminating the data. The 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.

There are 8 empty room files available in this dataset. For each subject, we will use only one file, the one that was acquired at the closest date. We will now import and process all the empty room recordings simultaneously, even if only one is needed by the current subject. Later, for each subject we will select the most appropriate noise covariance matrix.

EEG noise covariance: Pre-stimulus baseline

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).

Forward model: EEG and MEG

Inverse model: Minimum norm estimates

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.

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 16 subjects: brainstorm3/toolbox/script/tutorial_visual_single.m (execution time: 10-30 hours)
Report for the first subject: report_TutorialVisual_sub-01.html

1 function tutorial_visual_single(bids_dir, reports_dir) 2 % TUTORIAL_VISUAL_SINGLE: Runs the Brainstorm/SPM group analysis pipeline (single subject, BIDS version). 3 % 4 % ONLINE TUTORIALS: https://neuroimage.usc.edu/brainstorm/Tutorials/VisualSingle 5 % 6 % INPUTS: 7 % - bids_dir: Path to folder ds000117 (https://openneuro.org/datasets/ds000117) 8 % |- derivatives/freesurfer/sub-XX : Segmentation folders generated with FreeSurfer 9 % |- derivatives/meg_derivatives/sub-XX/ses-meg/meg/*.fif : MEG+EEG recordings (processed with MaxFilter's SSS) 10 % |- derivatives/meg_derivatives/sub-emptyroom/ses-meg/meg/*.fif : Empty room measurements 11 % - reports_dir: If defined, exports all the reports as HTML to this folder 12 13 % @============================================================================= 14 % This function is part of the Brainstorm software: 15 % https://neuroimage.usc.edu/brainstorm 16 % 17 % Copyright (c) University of Southern California & McGill University 18 % This software is distributed under the terms of the GNU General Public License 19 % as published by the Free Software Foundation. Further details on the GPLv3 20 % license can be found at http://www.gnu.org/copyleft/gpl.html. 21 % 22 % FOR RESEARCH PURPOSES ONLY. THE SOFTWARE IS PROVIDED "AS IS," AND THE 23 % UNIVERSITY OF SOUTHERN CALIFORNIA AND ITS COLLABORATORS DO NOT MAKE ANY 24 % WARRANTY, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO WARRANTIES OF 25 % MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE, NOR DO THEY ASSUME ANY 26 % LIABILITY OR RESPONSIBILITY FOR THE USE OF THIS SOFTWARE. 27 % 28 % For more information type "brainstorm license" at command prompt. 29 % =============================================================================@ 30 % 31 % Author: Francois Tadel, Elizabeth Bock, 2016-2018 32 33 34 %% ===== SCRIPT VARIABLES ===== 35 % Full list of subjects to process 36 SubjectNames = {'sub-01', 'sub-02', 'sub-03', 'sub-04', 'sub-05', 'sub-06', 'sub-07', 'sub-08', ... 37 'sub-09', 'sub-10', 'sub-11', 'sub-12', 'sub-13', 'sub-14', 'sub-15', 'sub-16'}; 38 % Empty-room dates for each subject (so that we can match automatically recordings with empty-room) 39 EmptyRoomSubj = 'sub-emptyroom'; 40 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', ... 41 '15-May-2009', '15-May-2009', '01-Jun-2009', '01-Jun-2009', '01-Jun-2009', '26-Nov-2009', '08-Dec-2009', '08-Dec-2009'}; 42 % Bad channels {iSubj} = {Run01, Run02, Run03, Run04, Run05, Run06} 43 BadChannels{1} = {'EEG016', 'EEG070', 'EEG050',{'EEG008','EEG050'}, [], []}; 44 BadChannels{2} = {{'EEG027', 'EEG030', 'EEG038'}, 'EEG010', 'EEG010', 'EEG010', 'EEG010', 'EEG010'}; 45 BadChannels{3} = {{'EEG008','EEG017'}, {'EEG008','EEG017'}, {'EEG008','EEG017'}, {'EEG008','EEG017'}, {'EEG008','EEG017','EEG001'}, {'EEG008','EEG017','EEG020'}}; 46 BadChannels{4} = {{'EEG038'}, {'EEG038','EEG001','EEG016'}, {'EEG038','EEG001','EEG016'}, {'EEG038','EEG001'}, {'EEG038','EEG001','EEG016'}, {'EEG038','EEG001','EEG016'}}; 47 BadChannels{5} = {'EEG001', 'EEG001', [], [], [], []}; 48 BadChannels{6} = {'EEG068', [], 'EEG004', [], [], []}; 49 BadChannels{7} = {[], [], {'EEG004','EEG008'}, {'EEG004','EEG008'},{'EEG004','EEG008','EEG043','EEG045','EEG047'}, {'EEG004','EEG008'}}; 50 BadChannels{8} = {[], [], [], [], [], []}; 51 BadChannels{9} = {[], 'EEG004', 'EEG004', [], 'EEG004', 'EEG004'}; 52 BadChannels{10} = {[], [], [], [], [], []}; 53 BadChannels{11} = {{'EEG010','EEG050'}, 'EEG050', 'EEG050', 'EEG050', 'EEG050', 'EEG050'}; 54 BadChannels{12} = {{'EEG024','EEG057'}, {'EEG024','EEG057'}, {'EEG024','EEG057'}, {'EEG024','EEG057','EEG070'}, {'EEG024','EEG057'}, {'EEG024','EEG057','EEG070'}}; 55 BadChannels{13} = {'EEG009', 'EEG009', {'EEG009','EEG057','EEG69'}, 'EEG009', {'EEG009','EEG044'}, {'EEG009','EEG044'}}; 56 BadChannels{14} = {'EEG029', 'EEG029', 'EEG029', {'EEG004','EEG008','EEG016','EEG029'}, {'EEG004','EEG008','EEG016','EEG029'}, {'EEG004','EEG008','EEG016','EEG029'}}; 57 BadChannels{15} = {'EEG038', 'EEG038', 'EEG038', 'EEG038', {'EEG054','EEG038'}, 'EEG038'}; 58 BadChannels{16} = {'EEG008', 'EEG008', 'EEG008', 'EEG008', 'EEG008', 'EEG008'}; 59 % SSP components to remove {iSubj} = {sRun01, sRun02, sRun03, sRun03, sRun04, sRun05, sRun06}, sRun0X={ECG_GRAD,ECG_MAG} 60 SspSelect{1} = {{1,1}, {1,1}, {1,1}, {1,1}, {1,1}, {1,1}}; 61 SspSelect{2} = {{1,1}, {1,1}, {1,1}, {1,1}, {3,1}, {1,1}}; 62 SspSelect{3} = {{[],1}, {1,1}, {1,1}, {1,1}, {1,1}, {1,1}}; 63 SspSelect{4} = {{[],1}, {[],1}, {[],1}, {[],1}, {[],1}, {[],1}}; 64 SspSelect{5} = {{2,1}, {1,1}, {1,1}, {[],1}, {1,1}, {1,1}}; 65 SspSelect{6} = {{2,1}, {2,1}, {1,1}, {2,1}, {1,1}, {2,1}}; 66 SspSelect{7} = {{1,1}, {1,1}, {1,1}, {1,1}, {1,1}, {1,1}}; 67 SspSelect{8} = {{1,1}, {1,1}, {[],1}, {2,1}, {1,1}, {2,1}}; 68 SspSelect{9} = {{1,1}, {1,1}, {[],1}, {[],1}, {1,1}, {1,1}}; 69 SspSelect{10} = {{1,1}, {1,1}, {1,1}, {1,1}, {1,1}, {1,1}}; 70 SspSelect{11} = {{[],1}, {[],1}, {[],1}, {[],1}, {[],[]}, {[],[]}}; 71 SspSelect{12} = {{[1,2],[1,2]}, {1,1}, {1,1}, {1,1}, {1,1}, {1,1}}; 72 SspSelect{13} = {{[],[]}, {[],[]}, {[],[]}, {[],[]}, {[],[]}, {[],[]}}; 73 SspSelect{14} = {{1,1}, {1,1}, {1,1}, {1,1}, {1,1}, {1,1}}; 74 SspSelect{15} = {{1,1}, {1,1}, {1,1}, {1,1}, {1,1}, {1,1}}; 75 SspSelect{16} = {{1,1}, {1,1}, {1,1}, {2,1}, {1,1}, {1,1}}; 76 77 78 %% ===== CREATE PROTOCOL ===== 79 % Start brainstorm without the GUI 80 if ~brainstorm('status') 81 brainstorm nogui 82 end 83 % Output folder for reports 84 if (nargin < 2) || isempty(reports_dir) || ~isdir(reports_dir) 85 reports_dir = []; 86 end 87 % You have to specify the folder in which the tutorial dataset is unzipped 88 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')) 89 error('The first argument must be the full path to the tutorial folder.'); 90 end 91 % The protocol name has to be a valid folder name (no spaces, no weird characters...) 92 ProtocolName = 'TutorialVisual'; 93 % Delete existing protocol 94 gui_brainstorm('DeleteProtocol', ProtocolName); 95 % Create new protocol 96 gui_brainstorm('CreateProtocol', ProtocolName, 0, 0); 97 % Set visualization filters: 40Hz low-pass, no high-pass 98 panel_filter('SetFilters', 1, 40, 0, [], 0, [], 0, 0); 99 % Set colormap: local color scale 100 bst_colormaps('SetMaxMode', 'meg', 'local'); 101 bst_colormaps('SetMaxMode', 'eeg', 'local'); 102 103 104 %% ===== PRE-PROCESS AND IMPORT ===== 105 for iSubj = 1:16 106 % Start a new report (one report per subject) 107 bst_report('Start'); 108 disp(sprintf('\n===== IMPORT: SUBJECT #%d =====\n', iSubj)); 109 110 % If subject already exists: delete it 111 [sSubject, iSubject] = bst_get('Subject', SubjectNames{iSubj}); 112 if ~isempty(sSubject) 113 db_delete_subjects(iSubject); 114 end 115 116 % ===== FILES TO IMPORT ===== 117 % Build the path of the files to import 118 AnatDir = fullfile(bids_dir, 'derivatives', 'freesurfer', SubjectNames{iSubj}, 'ses-mri', 'anat'); 119 DataDir = fullfile(bids_dir, 'derivatives', 'meg_derivatives', SubjectNames{iSubj}, 'ses-meg', 'meg'); 120 % Check if the folder contains the required files 121 if ~file_exist(AnatDir) 122 error(['The folder "' AnatDir '" does not exist.']); 123 end 124 if ~file_exist(DataDir) 125 error(['The folder "' DataDir '" does not exist.']); 126 end 127 128 % ===== ANATOMY ===== 129 % Process: Import anatomy folder 130 bst_process('CallProcess', 'process_import_anatomy', [], [], ... 131 'subjectname', SubjectNames{iSubj}, ... 132 'mrifile', {AnatDir, 'FreeSurfer'}, ... 133 'nvertices', 15000); 134 135 % ===== PROCESS EACH RUN ===== 136 for iRun = 1:6 137 % Files to import 138 FifFile = bst_fullfile(DataDir, sprintf('%s_ses-meg_task-facerecognition_run-%02d_proc-sss_meg.fif', SubjectNames{iSubj}, iRun)); 139 140 % ===== LINK CONTINUOUS FILE ===== 141 % Process: Create link to raw file 142 sFileRaw = bst_process('CallProcess', 'process_import_data_raw', [], [], ... 143 'subjectname', SubjectNames{iSubj}, ... 144 'datafile', {FifFile, 'FIF'}, ... 145 'channelreplace', 1, ... 146 'channelalign', 0); 147 % Set acquisition date 148 panel_record('SetAcquisitionDate', sFileRaw.iStudy, AcquisitionDates{iSubj}); 149 150 % ===== PREPARE CHANNEL FILE ===== 151 % Process: Set channels type 152 bst_process('CallProcess', 'process_channel_settype', sFileRaw, [], ... 153 'sensortypes', 'EEG061, EEG064', ... 154 'newtype', 'NOSIG'); 155 bst_process('CallProcess', 'process_channel_settype', sFileRaw, [], ... 156 'sensortypes', 'EEG062', ... 157 'newtype', 'EOG'); 158 bst_process('CallProcess', 'process_channel_settype', sFileRaw, [], ... 159 'sensortypes', 'EEG063', ... 160 'newtype', 'ECG'); 161 162 % Process: Remove head points 163 sFileRaw = bst_process('CallProcess', 'process_headpoints_remove', sFileRaw, [], ... 164 'zlimit', 0); 165 % Process: Refine registration 166 sFileRaw = bst_process('CallProcess', 'process_headpoints_refine', sFileRaw, []); 167 % Process: Project electrodes on scalp 168 sFileRaw = bst_process('CallProcess', 'process_channel_project', sFileRaw, []); 169 170 % Process: Snapshot: Sensors/MRI registration 171 bst_process('CallProcess', 'process_snapshot', sFileRaw, [], ... 172 'target', 1, ... % Sensors/MRI registration 173 'modality', 1, ... % MEG (All) 174 'orient', 1, ... % left 175 'Comment', sprintf('MEG/MRI Registration: Subject #%d, Run #%d', iSubj, iRun)); 176 bst_process('CallProcess', 'process_snapshot', sFileRaw, [], ... 177 'target', 1, ... % Sensors/MRI registration 178 'modality', 4, ... % EEG 179 'orient', 1, ... % left 180 'Comment', sprintf('EEG/MRI Registration: Subject #%d, Run #%d', iSubj, iRun)); 181 182 % ===== IMPORT TRIGGERS ===== 183 % Process: Read from channel 184 bst_process('CallProcess', 'process_evt_read', sFileRaw, [], ... 185 'stimchan', 'STI101', ... 186 'trackmode', 2, ... % Bit: detect the changes for each bit independently 187 'zero', 0); 188 % Process: Group by name 189 bst_process('CallProcess', 'process_evt_groupname', sFileRaw, [], ... 190 'combine', 'Unfamiliar=3,4', ... 191 'dt', 0, ... 192 'delete', 1); 193 % Process: Rename event 194 bst_process('CallProcess', 'process_evt_rename', sFileRaw, [], ... 195 'src', '3', ... 196 'dest', 'Famous'); 197 % Process: Rename event 198 bst_process('CallProcess', 'process_evt_rename', sFileRaw, [], ... 199 'src', '5', ... 200 'dest', 'Scrambled'); 201 % Process: Add time offset 202 bst_process('CallProcess', 'process_evt_timeoffset', sFileRaw, [], ... 203 'info', [], ... 204 'eventname', 'Famous, Unfamiliar, Scrambled', ... 205 'offset', 0.0345); 206 % Process: Delete events 207 bst_process('CallProcess', 'process_evt_delete', sFileRaw, [], ... 208 'eventname', '1,2,6,7,8,9,10,11,12,13,14,15,16'); 209 % Process: Detect cHPI activity (Elekta):STI201 210 bst_process('CallProcess', 'process_evt_detect_chpi', sFileRaw, [], ... 211 'eventname', 'chpi_bad', ... 212 'channelname', 'STI201', ... 213 'method', 'off'); % Mark as bad when the HPI coils are OFF 214 215 % ===== FREQUENCY FILTERS ===== 216 % Process: Notch filter: 50Hz 100Hz 150Hz 200Hz 217 sFileClean = bst_process('CallProcess', 'process_notch', sFileRaw, [], ... 218 'freqlist', [50, 100, 150, 200], ... 219 'sensortypes', 'MEG, EEG', ... 220 'read_all', 0); 221 % Process: Power spectrum density (Welch) 222 sFilesPsd = bst_process('CallProcess', 'process_psd', [sFileRaw, sFileClean], [], ... 223 'timewindow', [], ... 224 'win_length', 4, ... 225 'win_overlap', 50, ... 226 'sensortypes', 'MEG, EEG', ... 227 'edit', struct(... 228 'Comment', 'Power', ... 229 'TimeBands', [], ... 230 'Freqs', [], ... 231 'ClusterFuncTime', 'none', ... 232 'Measure', 'power', ... 233 'Output', 'all', ... 234 'SaveKernel', 0)); 235 % Process: Snapshot: Frequency spectrum 236 bst_process('CallProcess', 'process_snapshot', sFilesPsd, [], ... 237 'target', 10, ... % Frequency spectrum 238 'Comment', sprintf('Power spctrum: Subject #%d, Run #%d', iSubj, iRun)); 239 240 % ===== BAD CHANNELS ===== 241 if ~isempty(BadChannels{iSubj}{iRun}) 242 % Process: Set bad channels 243 bst_process('CallProcess', 'process_channel_setbad', sFileClean, [], ... 244 'sensortypes', BadChannels{iSubj}{iRun}); 245 end 246 247 % ===== EEG REFERENCE ===== 248 % Process: Re-reference EEG 249 bst_process('CallProcess', 'process_eegref', sFileClean, [], ... 250 'eegref', 'AVERAGE', ... 251 'sensortypes', 'EEG'); 252 253 % ===== DETECT ARTIFACTS ====== 254 % Process: Detect heartbeats 255 bst_process('CallProcess', 'process_evt_detect_ecg', sFileClean, [], ... 256 'channelname', 'EEG063', ... 257 'timewindow', [], ... 258 'eventname', 'cardiac'); 259 % Different amplitude thresholds for different subjects 260 if strcmpi(SubjectNames{iSubj}, 'sub-05') 261 thresholdMAX = 50; 262 else 263 thresholdMAX = 100; 264 end 265 % Process: Detect: blink_BAD - Detects all events where the amplitude exceeds 100uV 266 bst_process('CallProcess', 'process_evt_detect_threshold', sFileClean, [], ... 267 'eventname', 'blink_BAD', ... 268 'channelname', 'EEG062', ... 269 'timewindow', [], ... 270 'thresholdMAX', thresholdMAX, ... 271 'units', 3, ... % uV (10^-6) 272 'bandpass', [0.3, 20], ... 273 'isAbsolute', 1, ... 274 'isDCremove', 0); 275 276 % ===== SSP COMPUTATION ===== 277 % Process: SSP ECG: cardiac 278 bst_process('CallProcess', 'process_ssp_ecg', sFileClean, [], ... 279 'eventname', 'cardiac', ... 280 'sensortypes', 'MEG GRAD', ... 281 'usessp', 1, ... 282 'select', SspSelect{iSubj}{iRun}{1}); 283 bst_process('CallProcess', 'process_ssp_ecg', sFileClean, [], ... 284 'eventname', 'cardiac', ... 285 'sensortypes', 'MEG MAG', ... 286 'usessp', 1, ... 287 'select', SspSelect{iSubj}{iRun}{2}); 288 % Process: Snapshot: SSP projectors 289 bst_process('CallProcess', 'process_snapshot', sFileClean, [], ... 290 'target', 2, ... 291 'Comment', sprintf('Subject #%d, Run #%d', iSubj, iRun)); % SSP projectors 292 293 % ===== IMPORT BAD EVENTS ===== 294 % Get bad segments: this is typically done manually, not from a script 295 BadSegments = GetBadSegments(iSubj, iRun); 296 % Process: Import from file 297 bst_process('CallProcess', 'process_evt_import', sFileClean, [], ... 298 'evtfile', {BadSegments, 'ARRAY-TIMES'}, ... 299 'evtname', 'BAD'); 300 301 % ===== IMPORT TRIALS ===== 302 % Process: Import MEG/EEG: Events 303 sFilesEpochs = bst_process('CallProcess', 'process_import_data_event', sFileClean, [], ... 304 'subjectname', SubjectNames{iSubj}, ... 305 'condition', '', ... 306 'eventname', 'Famous, Scrambled, Unfamiliar', ... 307 'timewindow', [], ... 308 'epochtime', [-0.5, 1.2], ... 309 'createcond', 0, ... 310 'ignoreshort', 1, ... 311 'usectfcomp', 1, ... 312 'usessp', 1, ... 313 'freq', [], ... 314 'baseline', [-0.5, -0.0009]); 315 316 % ===== AVERAGE: RUN ===== 317 % Process: Average: By trial group (folder average) 318 sFilesAvg = bst_process('CallProcess', 'process_average', sFilesEpochs, [], ... 319 'avgtype', 5, ... % By trial group (folder average) 320 'avg_func', 1, ... % Arithmetic average: mean(x) 321 'weighted', 0, ... 322 'keepevents', 0); 323 % Process: Snapshot: Recordings time series 324 bst_process('CallProcess', 'process_snapshot', sFilesAvg, [], ... 325 'target', 5, ... % Recordings time series 326 'modality', 4, ... % EEG 327 'time', 0.11, ... 328 'Comment', sprintf('Subject #%d, Run #%d', iSubj, iRun)); 329 % Process: Snapshot: Recordings topography 330 bst_process('CallProcess', 'process_snapshot', sFilesAvg, [], ... 331 'target', 6, ... % Recordings topography (one time) 332 'modality', 4, ... % EEG 333 'time', 0.11, ... 334 'Comment', sprintf('Subject #%d, Run #%d', iSubj, iRun)); 335 336 % ===== COMPUTE NOISECOV: EEG ===== 337 % Process: Compute covariance (noise or data) 338 bst_process('CallProcess', 'process_noisecov', sFilesEpochs, [], ... 339 'baseline', [-0.5, -0.0009], ... 340 'sensortypes', 'EEG', ... 341 'target', 1, ... % Noise covariance (covariance over baseline time window) 342 'dcoffset', 1, ... % Block by block, to avoid effects of slow shifts in data 343 'identity', 0, ... 344 'copycond', 0, ... 345 'copysubj', 0, ... 346 'replacefile', 1); % Replace 347 end 348 349 % Save report 350 ReportFile = bst_report('Save', []); 351 if ~isempty(reports_dir) && ~isempty(ReportFile) 352 bst_report('Export', ReportFile, bst_fullfile(reports_dir, ['report_' ProtocolName '_' SubjectNames{iSubj} '.html'])); 353 end 354 end 355 356 357 %% ===== EMPTY ROOM RECORDINGS ===== 358 disp(sprintf('\n===== IMPORT: EMPTY-ROOM =====\n')); 359 % Loop on all the noise sessions 360 NoiseFiles = {}; 361 for ses = {'20090409', '20090506', '20090511', '20090515', '20090518', '20090601', '20091126', '20091208'} 362 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']); 363 end 364 % Process: Create link to raw file 365 sFilesNoise = bst_process('CallProcess', 'process_import_data_raw', [], [], ... 366 'subjectname', EmptyRoomSubj, ... 367 'datafile', {NoiseFiles, 'FIF'}, ... 368 'channelreplace', 1, ... 369 'channelalign', 0); 370 % Process: Notch filter: 50Hz 100Hz 150Hz 200Hz 371 sFileNoiseClean = bst_process('CallProcess', 'process_notch', sFilesNoise, [], ... 372 'freqlist', [50, 100, 150, 200], ... 373 'sensortypes', 'MEG, EEG', ... 374 'read_all', 0); 375 % Process: Compute noise covariance 376 bst_process('CallProcess', 'process_noisecov', sFileNoiseClean, [], ... 377 'baseline', [], ... 378 'sensortypes', 'MEG', ... 379 'target', 1, ... % Noise covariance (covariance over baseline time window) 380 'dcoffset', 1, ... % Block by block, to avoid effects of slow shifts in data 381 'identity', 0, ... 382 'copycond', 1, ... 383 'copysubj', 1, ... 384 'copymatch', 1, ... 385 'replacefile', 2); % Merge 386 387 388 %% ===== SOURCE ESTIMATION ===== 389 % Start a new report (one report for the source estimation of all the subjects) 390 bst_report('Start'); 391 % Loop on the subjects: This loop is separated from the previous one, because we should 392 % compute the BEM surfaces after importing all the runs, so that the registration is done 393 % using the high resolution head surface, instead of the smooth scalp BEM layer. 394 for iSubj = 1:length(SubjectNames) 395 disp(sprintf('\n===== SOURCES: SUBJECT #%d =====\n', iSubj)); 396 397 % ===== BEM SURFACES ===== 398 % Process: Generate BEM surfaces 399 bst_process('CallProcess', 'process_generate_bem', [], [], ... 400 'subjectname', SubjectNames{iSubj}, ... 401 'nscalp', 1082, ... 402 'nouter', 642, ... 403 'ninner', 642, ... 404 'thickness', 4); 405 406 % ===== SELECT ALL AVERAGES ===== 407 % Process: Select data files in: */* 408 sFilesAvg = bst_process('CallProcess', 'process_select_files_data', [], [], ... 409 'subjectname', SubjectNames{iSubj}); 410 % Process: Select file comments with tag: Avg 411 sFilesAvg = bst_process('CallProcess', 'process_select_tag', sFilesAvg, [], ... 412 'tag', 'Avg'); % Select only the files with the tag 413 414 % ===== COMPUTE HEAD MODELS ===== 415 % Process: Compute head model (only for the first run of the subject) 416 bst_process('CallProcess', 'process_headmodel', sFilesAvg(1), [], ... 417 'sourcespace', 1, ... % Cortex surface 418 'meg', 3, ... % Overlapping spheres 419 'eeg', 3, ... % OpenMEEG BEM 420 'ecog', 1, ... % 421 'seeg', 1, ... % 422 'openmeeg', struct(... 423 'BemSelect', [1, 1, 1], ... 424 'BemCond', [1, 0.0125, 1], ... 425 'BemNames', {{'Scalp', 'Skull', 'Brain'}}, ... 426 'BemFiles', {{}}, ... 427 'isAdjoint', 0, ... 428 'isAdaptative', 1, ... 429 'isSplit', 0, ... 430 'SplitLength', 4000)); 431 % Get all the runs for this subject (ie the list of the study indices) 432 iStudyOther = setdiff(unique([sFilesAvg.iStudy]), sFilesAvg(1).iStudy); 433 % Copy the forward model file to the other runs 434 sHeadmodel = bst_get('HeadModelForStudy', sFilesAvg(1).iStudy); 435 for iStudy = iStudyOther 436 db_add(iStudy, sHeadmodel.FileName); 437 end 438 439 % ===== COMPUTE SOURCES: MEG ===== 440 % Process: Compute sources [2018] 441 sAvgSrcMeg = bst_process('CallProcess', 'process_inverse_2018', sFilesAvg, [], ... 442 'output', 1, ... % Kernel only: shared 443 'inverse', struct(... 444 'Comment', 'MN: MEG ALL', ... 445 'InverseMethod', 'minnorm', ... 446 'InverseMeasure', 'amplitude', ... 447 'SourceOrient', {{'fixed'}}, ... 448 'Loose', 0.2, ... 449 'UseDepth', 1, ... 450 'WeightExp', 0.5, ... 451 'WeightLimit', 10, ... 452 'NoiseMethod', 'reg', ... 453 'NoiseReg', 0.1, ... 454 'SnrMethod', 'fixed', ... 455 'SnrRms', 1e-06, ... 456 'SnrFixed', 3, ... 457 'ComputeKernel', 1, ... 458 'DataTypes', {{'MEG GRAD', 'MEG MAG'}})); 459 % Process: Snapshot: Sources (one time) - Loop only to get a correct comment for the report 460 for i = 1:length(sAvgSrcMeg) 461 bst_process('CallProcess', 'process_snapshot', sAvgSrcMeg(i), [], ... 462 'target', 8, ... % Sources (one time) 463 'orient', 4, ... % bottom 464 'time', 0.11, ... 465 'threshold', 20, ... 466 'Comment', ['MEG sources: ' sFilesAvg(i).FileName]); 467 end 468 469 % ===== COMPUTE SOURCES: EEG ===== 470 % Process: Compute sources [2018] 471 sAvgSrcEeg = bst_process('CallProcess', 'process_inverse_2018', sFilesAvg, [], ... 472 'output', 1, ... % Kernel only: shared 473 'inverse', struct(... 474 'Comment', 'MN: EEG', ... 475 'InverseMethod', 'minnorm', ... 476 'InverseMeasure', 'amplitude', ... 477 'SourceOrient', {{'fixed'}}, ... 478 'Loose', 0.2, ... 479 'UseDepth', 1, ... 480 'WeightExp', 0.5, ... 481 'WeightLimit', 10, ... 482 'NoiseMethod', 'reg', ... 483 'NoiseReg', 0.1, ... 484 'SnrMethod', 'fixed', ... 485 'SnrRms', 1e-06, ... 486 'SnrFixed', 3, ... 487 'ComputeKernel', 1, ... 488 'DataTypes', {{'EEG'}})); 489 % Process: Snapshot: Sources (one time) - Loop only to get a correct comment for the report 490 for i = 1:length(sAvgSrcEeg) 491 bst_process('CallProcess', 'process_snapshot', sAvgSrcEeg(i), [], ... 492 'target', 8, ... % Sources (one time) 493 'orient', 4, ... % bottom 494 'time', 0.11, ... 495 'threshold', 10, ... 496 'Comment', ['EEG sources: ' sFilesAvg(i).FileName]); 497 end 498 end 499 % Save report 500 ReportFile = bst_report('Save', []); 501 if ~isempty(reports_dir) && ~isempty(ReportFile) 502 bst_report('Export', ReportFile, bst_fullfile(reports_dir, ['report_' ProtocolName '_sources.html'])); 503 end 504 505 506 %% ===== TIME-FREQUENCY ===== 507 % Start a new report (one report for the time-frequency of all the subjects) 508 bst_report('Start'); 509 % List of conditions to process separately 510 AllConditions = {'Famous', 'Scrambled', 'Unfamiliar'}; 511 % Channels to display in the screen capture, by order of preference (if the first channel is bad, use the following) 512 SelChannel = {'EEG070','EEG060','EEG065','EEG050','EEG003'}; 513 % Compute one separate time-frequency average for each subject/run/condition 514 for iSubj = 1:length(SubjectNames) 515 disp(sprintf('\n===== TIME-FREQUENCY: SUBJECT #%d =====\n', iSubj)); 516 for iRun = 1:6 517 % Process: Select data files in: Subject/Run 518 sTrialsAll = bst_process('CallProcess', 'process_select_files_data', [], [], ... 519 'subjectname', SubjectNames{iSubj}, ... 520 'condition', sprintf('sub-%02d_ses-meg_task-facerecognition_run-%02d_proc-sss_meg_notch', iSubj, iRun)); 521 % Loop on the conditions 522 for iCond = 1:length(AllConditions) 523 % Comment describing this average 524 strComment = [SubjectNames{iSubj}, ' / ', sprintf('run_%02d', iRun), ' / ', AllConditions{iCond}]; 525 disp(['BST> ' strComment]); 526 % Find the first good channel in the display list 527 if isempty(BadChannels{iSubj}{iRun}) 528 iSel = 1; 529 else 530 iSel = find(~ismember(SelChannel,BadChannels{iSubj}{iRun}), 1); 531 end 532 % Process: Select file comments with tag: Avg 533 sTrialsCond = bst_process('CallProcess', 'process_select_tag', sTrialsAll, [], ... 534 'tag', [AllConditions{iCond}, '_trial'], ... 535 'search', 1, ... % Search the file names 536 'select', 1); % Select only the files with the tag 537 % Process: Time-frequency (Morlet wavelets), averaged across trials 538 sTimefreq = bst_process('CallProcess', 'process_timefreq', sTrialsCond, [], ... 539 'sensortypes', 'MEG MAG, EEG', ... 540 'edit', struct(... 541 'Comment', ['Avg: ' AllConditions{iCond} ', Power, 6-60Hz'], ... 542 'TimeBands', [], ... 543 '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], ... 544 'MorletFc', 1, ... 545 'MorletFwhmTc', 3, ... 546 'ClusterFuncTime', 'none', ... 547 'Measure', 'power', ... 548 'Output', 'average', ... 549 'RemoveEvoked', 0, ... 550 'SaveKernel', 0), ... 551 'normalize', 'none'); % None: Save non-standardized time-frequency maps 552 % Process: Extract time: [-200ms,900ms] 553 sTimefreq = bst_process('CallProcess', 'process_extract_time', sTimefreq, [], ... 554 'timewindow', [-0.2, 0.9], ... 555 'overwrite', 1); 556 % Screen capture of one sensor 557 hFigTf = view_timefreq(sTimefreq.FileName, 'SingleSensor', SelChannel{iSel}); 558 bst_report('Snapshot', hFigTf, strComment, 'Time-frequency', [200, 200, 400, 250]); 559 close(hFigTf); 560 end 561 end 562 end 563 % Save report 564 ReportFile = bst_report('Save', []); 565 if ~isempty(reports_dir) && ~isempty(ReportFile) 566 bst_report('Export', ReportFile, bst_fullfile(reports_dir, ['report_' ProtocolName '_timefreq.html'])); 567 end 568 end 569 570 571 572 573 %% ===== SUPPORT FUNCTIONS ===== 574 function BadSeg = GetBadSegments(iSubj, iRun) 575 BadSegments{1} = {... 576 [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], ... 577 [84.791 85.166], ... 578 [79.183 80.167], ... 579 [64.309 65.185], ... 580 [90.958 91.167; 178.005 178.355; 293.282 295.919; 312.298 316.479; 353.835 357.716], ... 581 [60.292 66.802; 69.975 71.210; 105.233 107.586; 108.822 109.506; 376.225 376.325]}; 582 BadSegments{2} = {... 583 [223.806 224.199; 279.772 279.895; 453.241 455.108; 692.423 692.593], ... 584 [65.298 66.194; 304.727 306.178; 399.165 400.732], ... 585 [203.141 205.085; 281.579 287.883; 420.395 421.128], ... 586 [387.118 388.229; 440.318 441.900; 554.825 558.744], ... 587 [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], ... 588 [38.000 47.999; 47.825 50.046; 61.298 61.384; 249.653 253.379; 282.917 283.820; 286.135 287.616; 298.167 300.196; 328.254 329.511; 335.957 337.817; 478.277 480.707]}; 589 BadSegments{3} = {... 590 [406.312 407.207; 727.055 728.714], ... 591 [84.894 85.156; 152.028 152.946; 297.835 298.915; 418.272 421.845; 554.084 554.794], ... 592 [73.758 74.159; 378.212 378.536; 406.065 407.099; 470.541 471.698; 488.900 491.168; 529.596 530.453], ... 593 [94.874 95.152; 317.385 321.374; 325.696 327.055; 439.220 439.829; 454.473 455.175; 486.196 486.829; 518.660 522.015; 524.400 525.249; 562.417 570.325], ... 594 [96.208 97.181; 98.942 99.096; 135.005 135.754; 143.990 144.599; 250.139 250.247; 300.459 300.559; 338.265 339.322; 545.913 546.067], ... 595 [91.415 92.156; 284.843 286.525; 297.886 298.404; 317.046 317.163; 332.698 332.791; 358.946 359.402; 428.405 428.775; 478.374 478.690; 549.866 550.128]}; 596 BadSegments{4} = {... 597 [22.967 22.967; 50.036 50.098; 52.058 52.058; 156.653 156.653; 171.565 173.386; 239.544 242.105; 268.162 270.175; 268.992 268.992; 316.032 316.032; 338.283 339.000; 357.959 361.909; 370.871 370.871; 381.579 383.677; 437.731 437.731; 463.482 468.505; 476.135 479.838; 486.652 488.272; 504.860 508.999], ... 598 [309.493 311.707; 342.681 344.525; 354.019 357.321; 390.023 391.225; 393.926 395.855; 404.221 405.069; 432.522 435.932; 459.048 460.715; 471.763 478.529; 549.387 551.999; 591.087 594.143; 608.541 611.079; 624.847 626.615; 649.648 651.570], ... 599 [57.411 58.198; 88.346 88.955; 200.761 202.335; 227.016 227.688; 257.726 258.054; 356.798 359.005; 404.260 411.003], ... 600 [46.000 54.823; 61.000 70.332; 203.005 207.125; 275.875 278.121; 313.500 314.824; 337.973 338.636; 422.505 426.239], ... 601 [58.000 62.479; 78.250 85.166; 89.955 91.360; 116.322 117.888; 130.013 131.987; 149.509 150.489; 174.650 175.823; 182.030 183.334; 196.758 197.384; 204.458 204.697; 205.236 208.663; 311.028 316.383; 320.700 327.181; 332.437 335.354; 344.205 346.133; 374.208 374.865; 385.519 386.214; 441.942 444.241; 453.957 456.997; 486.039 487.004; 501.238 504.185; 512.962 514.675; 553.398 556.215], ... 602 [41.406 45.743; 58.681 59.144; 108.086 108.896; 140.633 143.750; 196.110 199.474; 210.778 210.971; 234.649 235.143; 258.081 259.632; 339.101 340.805; 390.277 390.609; 438.935 442.122; 528.221 534.031]}; 603 BadSegments{5} = {... 604 [265.539 265.778; 266.334 266.495; 268.479 268.965; 367.428 367.636; 439.655 442.779; 453.497 453.853; 504.997 505.329; 519.513 519.683; 595.674 595.982; 602.000 602.463], ... 605 [121.113 121.499; 124.971 126.213; 253.735 254.075; 272.232 272.464; 272.895 273.104; 346.368 346.645; 368.812 369.052; 406.382 406.605; 452.920 453.113; 454.903 455.112; 507.655 507.840; 508.766 509.013; 584.853 585.030; 594.831 595.656; 597.261 602.249], ... 606 [37.251 37.497; 38.825 39.056; 40.615 41.849; 43.624 44.758; 53.333 53.641; 54.698 55.076; 57.668 59.196; 79.129 79.360; 81.475 81.714; 122.658 123.375; 284.787 285.296; 288.754 288.993; 345.790 346.022; 421.212 421.459; 481.428 482.207; 503.408 503.685; 504.272 504.449; 524.451 524.714; 526.913 531.499], ... 607 [87.322 88.178; 91.085 91.325; 95.121 95.491; 114.174 114.397; 129.874 130.113; 151.220 151.544; 281.689 281.959; 532.966 533.345], ... 608 [59.176 60.218; 74.854 75.317; 308.180 309.877; 380.705 381.059], ... 609 [182.382 183.245; 196.220 196.736; 276.018 276.327; 292.490 294.086; 370.755 370.847; 435.644 436.624; 467.535 468.460; 522.838 525.847]}; 610 BadSegments{6} = {... 611 [141.690 142.424; 157.070 157.417; 355.138 356.025; 423.999 423.999; 424.999 424.999; 426.999 426.999; 427.999 427.999; 486.430 488.151; 493.999 493.999; 501.511 501.619; 501.549 501.549; 501.585 501.585; 502.999 502.999; 503.999 503.999; 540.999 540.999; 541.999 541.999; 555.999 555.999; 556.999 556.999; 561.999 561.999; 563.999 563.999; 564.999 564.999; 565.999 565.999; 567.999 567.999], ... 612 [64.898 65.161; 71.700 72.718; 226.185 226.740; 324.124 324.425; 329.062 329.301; 486.143 486.975], ... 613 [62.300 63.148; 266.254 266.639; 409.920 410.221], ... 614 [54.048 55.214; 330.893 331.255], ... 615 [185.616 186.495; 331.411 331.796; 386.843 387.028; 387.999 387.999; 389.999 389.999; 434.575 434.875; 519.802 519.995], ... 616 [44.720 45.167; 211.446 211.964; 368.955 369.172]}; 617 BadSegments{7} = {... 618 [154.966 155.144; 551.639 551.855], ... 619 [88.774 89.167; 107.999 107.999; 109.999 109.999; 110.999 110.999; 112.999 112.999; 113.999 113.999; 114.999 114.999; 119.999 119.999; 121.999 121.999; 124.223 125.465; 137.011 138.871], ... 620 [81.627 82.136; 377.953 381.046], ... 621 [241.136 242.171; 543.849 544.196; 596.639 598.553; 600.227 601.075; 603.999 603.999; 605.999 605.999; 609.999 609.999; 611.305 612.809; 614.999 614.999; 615.803 616.937; 623.694 625.877; 653.999 653.999; 655.055 655.756; 663.999 663.999], ... 622 [68.852 69.481; 74.034 75.200; 78.426 78.600; 104.497 105.963; 253.951 254.034; 256.964 257.038; 257.915 258.048; 323.254 324.156; 365.880 368.131; 369.952 370.060; 371.728 372.999; 430.931 431.965; 535.521 542.999], ... 623 [70.271 71.205; 94.441 96.445; 98.613 99.126; 112.318 112.749; 131.686 132.265; 148.935 150.615; 161.120 161.600; 205.325 208.214; 215.035 215.863; 217.403 218.818; 286.178 287.171; 399.075 404.853]}; 624 BadSegments{8} = {... 625 [238.505 238.546; 256.354 257.224; 316.116 316.167; 341.519 341.558; 356.493 356.566; 380.095 380.170; 391.906 392.048; 448.457 448.562; 469.931 470.028; 530.488 530.575; 555.180 558.136; 562.152 562.245; 588.403 588.502; 625.205 625.662; 638.019 638.438; 649.982 650.008; 650.691 651.357; 651.925 652.061; 665.445 665.472; 695.923 696.015; 706.528 706.720; 729.706 732.534], ... 626 [99.546 106.114; 113.245 114.199; 150.885 154.989; 277.486 278.075; 333.645 335.574; 339.358 340.646; 371.860 375.394; 497.459 499.156], ... 627 [49.008 50.205; 231.446 233.406; 329.590 329.659; 355.101 356.019; 360.733 360.973; 372.955 374.891; 389.283 392.068; 453.610 455.431; 464.632 465.265; 489.209 489.996; 514.777 515.295], ... 628 [178.368 179.232; 293.865 294.521; 418.252 418.314; 450.124 450.209; 480.236 480.445; 492.725 493.975; 495.170 497.624; 500.382 500.459; 504.247 504.402; 628.017 628.079; 628.827 628.905; 630.209 630.332], ... 629 [100.616 101.172; 107.691 108.539; 188.814 188.875; 193.119 193.281; 207.964 208.033; 432.254 432.385; 489.834 489.911; 517.960 518.037; 518.955 519.040; 520.938 521.062; 521.945 522.037; 523.959 524.052; 525.942 526.057; 526.945 527.015; 528.958 529.059; 531.138 531.192; 531.979 532.048; 532.951 533.028; 533.962 534.024], ... 630 [135.997 137.232; 383.565 383.657; 418.763 418.955]}; 631 BadSegments{9} = {... 632 [215.107 216.187; 262.388 262.388; 287.519 287.635; 289.895 290.135; 311.999 311.999; 350.161 351.179; 526.154 527.033; 564.999 564.999; 584.935 585.059; 587.999 587.999; 601.999 601.999; 603.999 603.999; 608.999 608.999; 612.999 612.999], ... 633 [47.667 47.775; 49.172 54.681; 67.195 70.805; 78.988 80.477; 138.701 138.948; 138.727 138.727; 138.728 138.728; 138.728 138.728; 138.734 138.734; 138.734 138.734; 138.881 138.881; 138.881 138.881; 138.907 138.907; 140.993 141.093; 141.037 141.037; 141.045 141.045; 155.795 155.864; 155.822 155.822; 155.849 155.849; 168.999 168.999; 206.999 206.999; 219.999 219.999; 225.999 225.999; 226.999 226.999; 228.999 228.999; 236.999 236.999; 242.463 242.463; 242.463 242.463; 242.487 242.487; 247.999 247.999; 251.999 251.999; 252.483 252.483; 253.315 254.163; 265.999 265.999; 267.999 267.999; 272.358 272.358; 272.410 272.410; 298.999 298.999; 300.999 300.999; 314.037 314.037; 314.047 314.047; 321.813 321.813; 321.813 321.813; 321.833 321.833; 329.117 329.117; 329.117 329.117; 329.156 329.156; 346.999 346.999; 347.999 347.999; 349.320 349.320; 349.329 349.329; 352.528 355.869; 364.040 364.040; 396.278 397.420; 404.865 404.865; 407.905 407.905; 407.905 407.905; 407.954 407.954; 418.454 418.454; 418.454 418.454; 418.486 418.486; 441.999 441.999; 444.999 444.999; 447.999 447.999; 453.550 453.650; 454.931 455.055; 457.999 457.999; 479.964 480.079; 481.999 481.999; 482.949 483.073; 488.948 489.064; 511.999 511.999; 520.999 520.999; 523.999 523.999; 526.954 527.069; 533.999 533.999; 537.999 537.999], ... 634 [82.889 83.198; 206.143 207.424; 403.873 404.096; 406.790 407.485; 413.936 414.075; 415.912 416.112; 536.621 537.532], ... 635 [182.999 182.999; 182.999 182.999; 183.999 183.999; 195.999 195.999; 208.999 208.999; 209.999 209.999; 278.601 278.955; 413.913 414.067; 415.250 417.792; 419.940 420.087; 420.999 420.999; 512.999 512.999; 514.363 516.254; 521.917 522.134; 522.999 522.999; 523.915 524.101; 533.999 533.999; 538.999 538.999; 539.892 540.062; 543.999 543.999; 548.874 549.089; 549.900 550.062; 552.755 554.075; 585.999 585.999; 587.957 588.073; 588.999 588.999; 594.999 594.999; 603.971 604.056; 604.928 605.097; 615.986 618.495; 623.999 623.999], ... 636 [53.215 54.203; 164.227 168.965; 201.433 202.775; 292.973 295.889; 303.961 304.817; 309.354 311.236; 313.123 313.639; 322.547 323.017; 331.141 334.852; 356.637 356.985; 367.855 368.079; 369.995 373.459; 377.849 378.142; 406.072 407.306; 436.164 436.665; 458.033 458.905; 516.889 518.656; 517.812 518.684], ... 637 [113.945 115.225; 198.570 198.863; 264.162 264.795; 383.705 385.641; 396.477 397.064; 399.706 406.457; 452.261 453.179; 486.338 487.102; 498.869 499.579; 507.968 508.925; 546.266 547.285; 558.825 560.128]}; 638 BadSegments{10} = {... 639 [235.104 236.184; 324.272 325.028; 330.799 331.401; 541.062 541.826; 564.747 565.072], ... 640 [100.581 101.229; 285.644 285.644; 285.644 285.644; 297.979 298.033; 298.999 298.999; 300.949 301.026; 301.999 301.999; 303.999 303.999; 304.999 304.999; 306.999 306.999; 307.999 307.999; 310.999 310.999; 311.999 311.999; 313.971 314.025; 314.999 314.999; 316.995 317.035; 317.999 317.999; 319.999 319.999; 320.923 321.046; 326.971 327.048; 327.999 327.999; 329.999 329.999; 330.999 330.999; 332.974 333.028; 333.999 333.999; 335.971 336.025; 336.999 336.999; 338.973 339.035; 339.999 339.999; 341.951 342.044; 343.999 343.999; 344.964 345.033; 346.999 346.999; 347.999 347.999; 452.015 453.411; 458.735 459.776; 467.974 468.089], ... 641 [41.266 41.675; 53.905 55.240; 159.159 159.345; 220.255 220.394], ... 642 [66.751 67.190; 201.674 201.812; 294.119 295.168; 303.188 303.528; 316.992 317.037; 317.999 317.999; 317.999 317.999; 319.999 319.999; 320.980 321.042; 322.999 322.999; 325.999 325.999; 326.955 327.048; 328.999 328.999; 333.999 333.999; 334.999 334.999; 342.999 342.999; 343.999 343.999; 351.915 352.038; 352.999 352.999; 407.979 408.056; 409.999 409.999; 411.999 411.999; 415.405 416.154; 435.999 435.999; 436.962 437.046; 468.885 469.100; 469.999 469.999; 470.999 470.999; 516.210 516.357], ... 643 [105.369 106.172; 141.468 142.178; 199.008 199.818; 201.269 201.716; 352.851 353.083; 449.865 452.041; 459.508 459.802], ... 644 [229.948 230.033; 230.999 230.999; 230.999 230.999; 259.588 259.990; 343.970 345.798; 361.999 361.999; 362.970 363.046; 364.999 364.999; 366.999 366.999; 367.962 368.031; 372.521 374.179; 405.999 405.999; 409.999 409.999; 411.999 411.999; 412.999 412.999; 434.999 434.999; 435.970 436.031; 516.999 516.999; 519.999 519.999]}; 645 BadSegments{11} = {... 646 [179.045 180.225; 323.999 323.999; 323.999 323.999; 324.999 324.999; 327.978 328.025; 328.966 329.028; 330.999 330.999; 365.999 365.999; 367.999 367.999; 370.999 370.999; 371.999 371.999; 373.999 373.999; 375.965 376.042; 377.999 377.999], ... 647 [52.107 53.156; 521.077 521.155], ... 648 [65.331 66.156], ... 649 [81.579 82.143; 108.565 108.565; 108.566 108.566; 112.176 112.176; 122.377 122.377; 122.565 122.565; 213.882 213.882; 215.305 215.305; 224.851 224.851; 224.919 224.919; 255.815 255.815; 257.893 257.893; 359.952 359.952; 361.630 361.754; 370.285 370.285; 376.853 376.853; 511.600 511.600; 513.631 513.631; 513.988 513.988; 518.215 518.215], ... 650 [63.205 64.154; 170.951 171.036; 176.841 176.841; 176.842 176.842; 177.282 177.282; 223.886 223.971; 259.963 259.963; 261.547 261.547; 341.185 341.302; 368.999 368.999; 370.999 370.999; 374.999 374.999; 382.971 383.033; 383.999 383.999; 386.999 386.999; 388.958 389.035; 390.999 390.999; 391.955 392.031; 394.955 395.040; 396.999 396.999; 398.999 398.999; 400.999 400.999; 402.999 402.999; 404.963 405.033; 406.999 406.999; 429.829 429.829; 430.346 430.346; 465.184 465.184; 470.290 470.290], ... 651 [50.195 51.145; 158.850 159.012]}; 652 BadSegments{12} = {... 653 [193.435 194.175; 500.000 501.000; 08.988 509.868; 528.999 528.999; 528.999 528.999; 529.999 529.999; 531.999 531.999; 547.999 547.999], ... 654 [133.484 134.185; 134.911 135.065; 553.999 553.999; 553.999 553.999; 554.999 554.999; 557.999 557.999; 558.999 558.999; 564.999 564.999; 565.977 566.046; 568.999 568.999; 569.951 570.028; 571.999 571.999; 579.959 580.028; 580.977 581.055; 583.971 584.033; 583.999 583.999; 585.999 585.999; 586.999 586.999; 588.980 589.026; 590.999 590.999; 591.958 592.044; 595.999 595.999], ... 655 [46.028 47.216; 129.557 130.236; 200.999 200.999; 200.999 200.999; 201.975 202.075; 203.999 203.999; 204.967 205.053; 213.627 214.515; 218.958 219.044; 222.959 228.252; 309.999 309.999; 311.897 317.537; 311.999 311.999; 351.968 353.234; 399.999 399.999; 446.508 451.454; 487.999 487.999; 512.278 512.926], ... 656 [82.535 84.062; 102.247 102.355; 134.999 134.999; 134.999 134.999; 136.999 136.999; 138.973 139.042; 147.999 147.999; 148.999 148.999; 149.999 149.999; 193.999 193.999; 194.999 194.999; 224.984 226.095; 241.954 242.077; 243.948 244.087; 280.999 280.999; 281.999 281.999; 305.000 305.451; 310.945 311.068; 328.999 328.999; 352.999 352.999; 353.999 353.999; 397.025 397.411; 415.944 418.096; 415.999 415.999; 470.999 470.999; 477.971 478.041; 483.997 484.737; 492.958 493.089; 493.999 493.999; 494.999 494.999; 522.999 522.999; 523.999 523.999; 524.955 525.039], ... 657 [175.183 176.155; 246.112 246.991; 412.005 413.340; 483.915 484.061; 485.959 486.028; 497.360 498.880; 594.457 594.944; 596.641 598.006; 615.277 618.710], ... 658 [66.900 72.232; 75.510 78.688; 543.495 545.999]}; 659 BadSegments{13} = {... 660 [307.246 308.179; 627.881 628.089; 629.941 630.049], ... 661 [171.506 172.301; 172.999 172.999; 430.999 430.999; 432.999 432.999; 434.999 434.999; 448.999 448.999; 479.999 479.999; 489.999 489.999; 490.999 490.999; 491.999 491.999], ... 662 [92.059 93.209], ... 663 [52.791 53.231; 54.999 54.999; 65.985 68.138; 91.240 91.386; 92.137 92.207; 105.346 105.493; 121.120 121.398; 146.546 146.955; 194.025 197.652; 242.985 243.571; 247.101 247.556; 269.889 270.083; 270.946 271.070; 274.408 275.866; 294.565 295.267; 319.370 319.879; 365.999 365.999; 375.913 376.044; 376.869 377.055; 377.999 377.999; 390.130 393.225; 403.965 404.035; 404.953 405.075; 419.265 419.565; 427.015 427.154; 427.879 428.118; 427.999 427.999; 428.913 429.067; 480.945 482.095; 484.285 484.571; 516.929 517.099; 517.963 518.079], ... 664 [115.921 116.060; 117.999 117.999; 120.999 120.999; 126.896 127.058; 130.955 131.039; 131.957 132.043; 132.961 133.015; 134.999 134.999; 135.950 136.043; 139.999 139.999; 143.999 143.999; 144.850 145.066; 145.999 145.999; 146.999 146.999; 160.929 161.045; 161.999 161.999; 189.086 189.310; 193.939 194.063; 195.975 196.045; 199.961 200.045; 201.952 202.059; 202.939 205.084; 210.999 210.999; 211.738 213.629; 217.954 218.061; 218.987 219.049; 219.975 220.028; 258.953 259.037; 259.952 260.045; 260.963 261.032; 261.958 262.043; 262.954 263.054; 262.999 262.999; 288.999 288.999; 295.477 295.655; 298.976 299.054; 299.979 300.018; 300.975 301.005; 336.952 337.044; 337.977 338.032; 338.988 339.027; 338.999 338.999; 339.991 340.045; 340.975 341.029; 341.978 342.016; 343.976 344.054; 361.961 362.045; 365.942 366.050; 370.999 370.999; 384.961 385.045; 409.977 410.047; 415.984 416.030; 426.944 427.059; 430.999 430.999; 436.999 436.999; 448.999 448.999; 449.500 452.131; 481.962 482.055; 482.973 483.027; 484.975 485.060; 503.999 503.999; 504.999 504.999; 507.967 508.028; 508.955 509.032; 510.999 510.999; 512.999 512.999; 518.865 520.292; 522.958 523.043; 523.999 523.999; 524.948 525.034; 525.959 526.044; 526.946 527.055; 527.942 528.027; 528.961 529.053; 529.945 530.022; 535.999 535.999; 536.957 537.058; 538.967 539.036; 539.963 540.040; 541.976 542.045; 543.999 543.999; 551.956 552.034; 552.990 553.028; 553.977 554.024; 555.968 556.022; 556.204 557.454; 557.963 558.032; 559.969 560.023; 563.973 564.035; 563.999 563.999], ... 665 [87.308 87.639; 107.039 108.189; 427.943 428.035; 437.965 438.027; 439.973 440.026; 491.275 492.571]}; 666 BadSegments{14} = {... 667 [365.982 367.194; 368.999 368.999; 368.999 368.999; 369.999 369.999; 371.999 371.999; 373.999 373.999; 375.942 376.050; 376.999 376.999; 378.999 378.999; 380.999 380.999; 382.999 382.999; 383.999 383.999; 386.999 386.999; 387.999 387.999; 418.999 418.999; 444.999 444.999; 690.178 693.218], ... 668 [101.796 102.183; 218.999 218.999; 219.999 219.999; 221.999 221.999; 222.999 222.999; 227.999 227.999; 229.999 229.999; 230.999 230.999; 232.999 232.999; 233.999 233.999; 235.999 235.999; 237.999 237.999; 238.985 239.031; 240.999 240.999; 242.999 242.999; 243.999 243.999; 246.999 246.999; 247.999 247.999; 253.999 253.999; 258.999 258.999; 259.999 259.999; 260.227 261.123; 264.999 264.999; 265.999 265.999; 268.999 268.999; 269.999 269.999; 274.999 274.999; 276.999 276.999; 277.999 277.999; 280.999 280.999; 282.966 283.021; 284.999 284.999; 285.999 285.999; 360.999 360.999; 362.973 363.026; 364.999 364.999; 365.999 365.999; 367.999 367.999; 368.985 369.024; 370.999 370.999; 371.999 371.999; 378.999 378.999; 379.999 379.999; 381.999 381.999; 383.999 383.999; 385.999 385.999; 387.999 387.999; 388.999 388.999; 398.999 398.999; 415.999 415.999; 417.999 417.999; 418.999 418.999; 421.999 421.999; 422.999 422.999], ... 669 [89.285 90.187], ... 670 [85.991 87.179; 88.999 88.999; 89.999 89.999; 92.966 93.027; 93.999 93.999; 107.999 107.999; 110.999 110.999; 111.971 112.025; 118.999 118.999; 121.999 121.999; 122.999 122.999; 166.999 166.999; 168.957 169.035; 173.999 173.999; 174.999 174.999; 175.957 176.035; 198.999 198.999; 199.999 199.999; 202.999 202.999; 203.999 203.999; 205.999 205.999; 207.999 207.999; 208.999 208.999; 210.974 211.044; 211.999 211.999; 212.999 212.999; 232.984 233.023; 234.999 234.999; 235.999 235.999; 236.999 236.999; 239.958 240.044; 240.999 240.999], ... 671 [71.132 72.227], ... 672 [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]}; 673 BadSegments{15} = {... 674 [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], ... 675 [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], ... 676 [53.557 54.175; 148.851 149.136], ... 677 [43.681 44.214; 361.255 361.587; 409.462 411.021], ... 678 [90.868 91.130; 410.935 411.044], ... 679 [64.786 65.141; 132.385 132.786; 177.735 178.252; 278.747 278.902; 313.959 314.028; 314.970 315.031]}; 680 BadSegments{16} = {... 681 [38.958 44.335; 305.280 312.826; 324.778 326.067; 393.183 398.367; 403.410 422.854], ... 682 [49.726 50.251; 58.675 65.465; 263.918 264.088; 321.795 322.195; 522.881 523.081], ... 683 [61.245 62.325; 65.628 65.905; 315.686 317.275; 335.184 336.449; 387.870 388.117; 389.892 390.062], ... 684 [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], ... 685 [71.665 72.513; 76.347 77.181; 127.134 128.862], ... 686 [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]}; 687 BadSeg = BadSegments{iSubj}{iRun}'; 688 end 689


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).








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Tutorials/VisualSingle (last edited 2022-06-24 10:18:04 by FrancoisTadel)