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| # Brain Entropy in space and time (BEst) plug-in for Brainstorm | ||
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| [TUTORIAL](https://neuroimage.usc.edu/brainstorm/Tutorials/TutBEst) |
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| 2.6.3 | ||
| july 25th, 2017 | ||
| Minor change: | ||
| - launch from the 2016 SOURCE PANEL is now operational | ||
| 2.7.1 | ||
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| june 19th, 2019 | ||
| Minor changes: | ||
| - Fix MATLAB parallelism | ||
| - Suppress warning for the custom Brainstorm process |
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| function [Results, OPTIONS] = be_cmem_solver_NIRS(HeadModel, OPTIONS, Results) | ||
| % cMEMSOLVER: Maximum Entropy on the Mean solution. | ||
| % | ||
| % NOTES: | ||
| % - This function is not optimized for stand-alone command calls. | ||
| % - Please use the generic BST_SOURCEIMAGING function, or the GUI.a | ||
| % | ||
| % INPUTS: | ||
| % - HeadModel : Brainstorm head model structure | ||
| % - OPTIONS : Structure of parameters (described in be_main.m) | ||
| % -solver : Parameters for MEM solver | ||
| % |- spatial_smoothing : optional spatial constraint on the | ||
| % variance of the active state function, | ||
| % for each cluster. Set to 1 to activate. | ||
| % |- NoiseCov : Noise variance-covariance matrix. | ||
| % Should be available from Brainstorm or can be | ||
| % estimated and provided separately | ||
| % |- active_var_mult : multiplier for the variance-covariance of | ||
| % the active state function | ||
| % |- inactive_var_mult : multiplier for the variance-covariance of | ||
| % the inactive state function (default: 0 i.e. a dirac) | ||
| % |- Optim_method : Choice of optimization routine. By | ||
| % default uses minFunc (Mark Schmidt's implementation). Specify 'fminunc' to use | ||
| % Matlab optimization toolbox routine. | ||
| % -clustering : Structure containing parcelization parameters | ||
| % |- MSP_R2_threshold : threshold (between 0 and 1) to filter | ||
| % data and forward operator in the msp | ||
| % calculation. | ||
| % |- MSP_window : number of samples to use for calculating the MSP. | ||
| % The window should be small enough to satisfy assumption of | ||
| % stationarity within that window. | ||
| % |- clusters_type : Set to 1 to used C. Grova's clustering | ||
| % method | ||
| % |- MSP_scores_threshold : Threshold of the MSP scores (between 0 | ||
| % and 1). Set to 0 to keep all the scores. | ||
| % |- neighborhood_order: Nb. of neighbors used to cluterize | ||
| % cortical surface | ||
| % -model : Parameters for MEM model | ||
| % |-alpha_threshold : Threshold on the cluster probabilities of | ||
| % being active. Clusters whose probas are | ||
| % lower than this threshold will be turned off | ||
| % |- alpha : Option to specify the alpha probabilities | ||
| % manually | ||
| % |- alpha_method : Selection of the method to initialize the | ||
| % probability (alpha) of activation of the | ||
| % clusters (alpha). | ||
| % | 1 = average of the scores of sources in a cluster | ||
| % | 2 = maximum of the scores of sources in a cluster | ||
| % | 3 = median of the scores of sources in a cluster | ||
| % | 4 = equiprobable activity (0.5) | ||
| % |- active_mean_method: Selection of the method to initialize | ||
| % the mean of the "active state" law (mu). | ||
| % | 1 = null hypothesis | ||
| % | 2 = Null activity (0) | ||
| % -mandatory : structure containing data and channel information | ||
| % |- Data : Data to process (channels x time samples) | ||
| % -automatic : structure containing outputs | ||
| % |- Units : Structure for rescaling units | ||
| % | ||
| % OUTPUTS: | ||
| % - OPTIONS : Keep track of parameters | ||
| % - Results : Structure | ||
| % |- ImageGridAmp : Source activity (sources x times) | ||
| % |- ImagingKernel : Not computed ([]) | ||
| % | ||
| % ============================================== | ||
| % Copyright (C) 2011 - MultiFunkIm & LATIS Team | ||
| % | ||
| % Authors: LATIS team, 2011 | ||
| % | ||
| %% ============================================== | ||
| % License | ||
| % | ||
| % BEst is free software: you can redistribute it and/or modify | ||
| % it under the terms of the GNU General Public License as published by | ||
| % the Free Software Foundation, either version 3 of the License, or | ||
| % (at your option) any later version. | ||
| % | ||
| % BEst is distributed in the hope that it will be useful, | ||
| % but WITHOUT ANY WARRANTY; without even the implied warranty of | ||
| % MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the | ||
| % GNU General Public License for more details. | ||
| % | ||
| % You should have received a copy of the GNU General Public License | ||
| % along with BEst. If not, see <http://www.gnu.org/licenses/>. | ||
| % ------------------------------------------------------------------------- | ||
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| %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% | ||
| %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% TO DO LIST %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% | ||
| %%%% | ||
| %%%% INITIALIZE GLOBAL VARIABLE IF FIRST STUDY | ||
| %%%% CLEAR GLOBAL VARIABLE IF LAST STUDY | ||
| %%%% | ||
| %%%%%%%%%%%%%%%%%%%%%%%%%%%%% END OF TO DO LIST %%%%%%%%%%%%%%%%%%%%%%%%%%% | ||
| %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% | ||
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| global MEMglobal | ||
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| if OPTIONS.optional.verbose | ||
| fprintf('\n\n===== pipeline cMEM\n'); | ||
| end | ||
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| %% Retrieve vertex connectivity - needed for clustering | ||
| %[OPTIONS, obj.VertConn] = be_vertex_connectivity(HeadModel, OPTIONS); % not necessary for NIRS | ||
| % WATCH OUT NIRS tomography FOV is not full mesh, it's part of mesh after | ||
| % threshold sensitivity | ||
| obj.VertConn = HeadModel.vertex_connectivity; % replaced by this line to retrieve vertex connectivity. | ||
| if isempty(OPTIONS.optional.clustering) && isempty(obj.VertConn) || diff(size(obj.VertConn)) | ||
| fprintf('MEM error : no vertex connectivity matrix available.\n'); | ||
| return | ||
| end | ||
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| %% ===== Comment ===== %% | ||
| OPTIONS.automatic.Comment = OPTIONS.optional.Comment; | ||
| if strcmp( OPTIONS.automatic.Comment(1:3), 'MEM' ) | ||
| OPTIONS.automatic.Comment = ['c' OPTIONS.optional.Comment]; | ||
| end | ||
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| %% ===== DC offset ===== %% | ||
| % we remove the DC offset the data | ||
| %[OPTIONS] = be_remove_dc(OPTIONS); | ||
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| %% ===== Channels ===== %% | ||
| % we retrieve the channels name and the data | ||
| [OPTIONS, obj] = be_main_channel(HeadModel, obj, OPTIONS); | ||
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| %% ===== AVG reference ===== %% | ||
| % we average reference the data | ||
| %[OPTIONS] = be_avg_reference(OPTIONS); | ||
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| %% ===== Sources ===== %% | ||
| % we verify that all sources in the model have good leadfields | ||
| [OPTIONS, obj] = be_main_sources(obj, OPTIONS); | ||
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| %% ===== Pre-whitening of the data ==== %% | ||
| % it uses empty-room data if available | ||
| % if PlOS one : nothing is done here | ||
| % [OPTIONS] = be_prewhite(OPTIONS); | ||
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| %% ===== Pre-process the leadfield(s) ==== %% | ||
| % we keep leadfields of interest; we compute svd of normalized leadfields | ||
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| [OPTIONS, obj] = be_main_leadfields(obj, OPTIONS); | ||
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| %% ===== Normalization ==== %% | ||
| % we absorb units (pT, nA) in the data, leadfields; we normalize the data | ||
| % and the leadfields | ||
| [OPTIONS] = be_normalize_and_units_NIRS(OPTIONS); | ||
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| %% ===== Apply temporal data window ===== %% | ||
| % check for a time segment to be localized | ||
| [OPTIONS] = be_apply_window( OPTIONS, [] ); | ||
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| %% ===== Double precision to single ===== %% | ||
| % relax the double precision for the msp (leadfield and data) | ||
| [OPTIONS] = be_switch_precision( OPTIONS, 'single' ); | ||
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| %% ===== Clusterize cortical surface ===== %% | ||
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| [OPTIONS, obj] = be_main_clustering(obj, OPTIONS); | ||
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| %% ===== Single precision to double ===== %% | ||
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| [OPTIONS] = be_switch_precision( OPTIONS, 'double' ); | ||
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| %% ===== pre-processing for spatial smoothing (Green mat. square) ===== %% | ||
| % matrix W'W from the Henson paper | ||
| [OPTIONS, obj.GreenM2] = be_spatial_priorw( OPTIONS, obj.VertConn); | ||
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| %% ===== Noise estimation ===== %% | ||
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| [OPTIONS, obj] = be_main_data_preprocessing(obj, OPTIONS); | ||
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| %% ===== Fuse modalities ===== %% | ||
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| obj = be_fusion_of_modalities( [], obj, OPTIONS); | ||
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| %% ===== Launch MEM ===== %% | ||
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| fprintf('\n\n===== pipeline Signel Wavelength\n'); | ||
| [obj.ImageGridAmp, OPTIONS] = be_launch_mem_NIRS(obj, OPTIONS); | ||
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| %% ===== Inverse temporal data window ===== %% | ||
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| [OPTIONS, obj] = be_apply_window( OPTIONS, obj ); | ||
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| % Clean up options | ||
| OPTIONS.automatic = struct( 'entropy_drops', OPTIONS.automatic.entropy_drops, ... | ||
| 'final_alpha', {OPTIONS.automatic.final_alpha}, ... | ||
| 'Comment', OPTIONS.automatic.Comment, ... | ||
| 'initial_alpha', obj.ALPHA, ... | ||
| 'clusters', obj.CLS, ... | ||
| 'minimum_norm',OPTIONS.automatic.Modality(1).Jmne, ... | ||
| 'MSP',obj.SCR); | ||
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| % Results (full temporal sequence) | ||
| Results = struct(... | ||
| 'ImageGridAmp', obj.ImageGridAmp, ... | ||
| 'ImagingKernel', [], ... | ||
| 'MEMoptions', OPTIONS); % ... | ||
| %'MEMdata', obj); | ||
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| return | ||
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