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process_beamformer_con_speedup.m
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process_beamformer_con_speedup.m
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function varargout = process_beamformer_con_speedup( varargin )
% PROCESS_BEAMFORMER_TEST:
% @=============================================================================
% This software is part of the Brainstorm software:
% http://neuroimage.usc.edu/brainstorm
%
% Copyright (c)2000-2013 Brainstorm by the University of Southern California
% This software is distributed under the terms of the GNU General Public License
% as published by the Free Software Foundation. Further details on the GPL
% 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.
% =============================================================================@
%
% Authors:
eval(macro_method)
%macro_methodcall;
end
%% ===== GET DESCRIPTION =====
function sProcess = GetDescription() %#ok<DEFNU>
% Description the process
sProcess.Comment = 'Beamformer-based connectivity imaging';
sProcess.FileTag = '';
sProcess.Category = 'Custom';
sProcess.SubGroup = 'Connectivity';
sProcess.Index = 3005;
% Definition of the input accepted by this process
sProcess.InputTypes = {'data','matrix'};
sProcess.OutputTypes = {'results','results'};
sProcess.nInputs = 2;
sProcess.nMinFiles = 1;
%sProcess.options.sep.Type = 'label';
% % Definition of the options
% sProcess.options.oriconstraint.Comment = {'Unconstrained', 'Cortical Constrained'};
% sProcess.options.oriconstraint.Type = 'radio';
% sProcess.options.oriconstraint.Value = 1;
sProcess.options.result_comm.Comment = 'Comment: ';
sProcess.options.result_comm.Type = 'text';
sProcess.options.result_comm.Value = '';
% === MEASURE ===
sProcess.options.measure.Comment = {'SILSC (Corr)', 'BIPAC (ESC)', 'Methods (Measures):'};
sProcess.options.measure.Type = 'radio_line';
sProcess.options.measure.Value = 1;
% Options: Time-freq
sProcess.options.labelC.Comment = '<HTML><B><U>Options for BIPAC (ESC)</U></B>:';
sProcess.options.labelC.Type = 'label';
% === NESTING FREQ
sProcess.options.nesting.Comment = 'Nesting frequency band (low, data, Files A):';
sProcess.options.nesting.Type = 'range';
sProcess.options.nesting.Value = {[2, 30], 'Hz', 2};
% === NESTED FREQ
sProcess.options.nested.Comment = 'Nested frequency band (high, ref, Files B):';
sProcess.options.nested.Type = 'range';
sProcess.options.nested.Value = {[40, 150], 'Hz', 2};
% Options: number of randomization test
sProcess.options.nRand.Comment = 'Number of randomization (very time consuming)';
sProcess.options.nRand.Type = 'value';
sProcess.options.nRand.Value = {0, '', 0};
sProcess.options.nRand.Hidden = 1;
% sProcess.options.ref_replicate_num.Comment = 'Number to replicate reference signals:';
% sProcess.options.ref_replicate_num.Type = 'value';
% sProcess.options.ref_replicate_num.Value = {1,'times',0};
%
% sProcess.options.ref_replicate_interval.Comment = 'Interval of replicated reference signals:';
% sProcess.options.ref_replicate_interval.Type = 'value';
% sProcess.options.ref_replicate_interval.Value = {0.01,'ms',1};
% Separator
sProcess.options.sep2.Type = 'separator';
sProcess.options.sep2.Comment = ' ';
sProcess.options.labelB.Comment = '<HTML><B><U>Estimation options (Files A)</U></B>:';
sProcess.options.labelB.Type = 'label';
% sProcess.options.beamformertype.Comment = {'Vector-type beamformer', 'Scalar-type beamformer'};
% sProcess.options.beamformertype.Type = 'radio';
% sProcess.options.beamformertype.Value = 1;
% === MINIMUM VARIANCE
sProcess.options.minvar_time.Comment = 'Minimum variance time window: ';
sProcess.options.minvar_time.Type = 'timewindow';
sProcess.options.minvar_time.Value = [];
% === ACTIVE TIME RANGE
sProcess.options.corr_range.Comment = 'Time range of interest: ';
sProcess.options.corr_range.Type = 'timewindow';
sProcess.options.corr_range.Value = [];
% === ACTIVE TIME WINDOW SIZE
sProcess.options.active_window_size.Comment = 'Sliding window size: ';
sProcess.options.active_window_size.Type = 'value';
sProcess.options.active_window_size.Value = {0.1, 'ms', 1};
% === ACTIVE TEMPORAL RESOLUTION
sProcess.options.corr_tresolution.Comment = 'Temporal resolution: ';
sProcess.options.corr_tresolution.Type = 'value';
sProcess.options.corr_tresolution.Value = {0.01, 'ms', 1};
% === REGULARIZATION
sProcess.options.reg.Comment = 'Regularization parameter: ';
sProcess.options.reg.Type = 'value';
sProcess.options.reg.Value = {0.1, '%', 4};
% === Sensor types
sProcess.options.sensortypes.Comment = 'Sensor types or names (empty=all): ';
sProcess.options.sensortypes.Type = 'text';
sProcess.options.sensortypes.Value = 'MEG, EEG';
% Separator
sProcess.options.sep.Type = 'separator';
sProcess.options.sep.Comment = ' ';
sProcess.options.labelA.Comment = '<HTML><B><U>Reference signal options (Files B)</U></B>:';
sProcess.options.labelA.Type = 'label';
% === REF TIME TYPE ===
sProcess.options.ref_range_type.Comment = {'Fixed time range (Absolute)', 'Range of time delay (Relative)', 'Time range type:'};
sProcess.options.ref_range_type.Type = 'radio_line';
sProcess.options.ref_range_type.Value = 1;
% === TIME WINDOW ===
sProcess.options.ref_range.Comment = 'Time range to extract ref signal: ';
sProcess.options.ref_range.Type = 'timewindow';
sProcess.options.ref_range.Value = [];
sProcess.options.ref_replicate_tresolution.Comment = 'Temporal resolution to extract ref signal: ';
sProcess.options.ref_replicate_tresolution.Type = 'value';
sProcess.options.ref_replicate_tresolution.Value = {0.01, 'ms', 1};
% === BASELINE TIME RANGE FOR Z STATISTICS CALCULATION
sProcess.options.baseline_time.Comment = 'Baseline: ';
sProcess.options.baseline_time.Type = 'baseline';
sProcess.options.baseline_time.Value = [];
% Options: Hanning window
sProcess.options.isHann.Comment = 'Apply hanning window';
sProcess.options.isHann.Type = 'checkbox';
sProcess.options.isHann.Value = 0;
%sProcess.options.sep2.Type = 'label';
% % Options: Mirror
% sProcess.options.mirror.Comment = 'Estimating in frequency domain';
% sProcess.options.mirror.Type = 'checkbox';
% sProcess.options.mirror.Value = 0;
% sProcess.options.mirror.InputTypes = {'data'};
% % === Low bound
% sProcess.options.highpassA.Comment = 'Lower cutoff frequency (Data):';
% sProcess.options.highpassA.Type = 'value';
% sProcess.options.highpassA.Value = {15,'Hz ',2};
% sProcess.options.highpassA.InputTypes = {'data'};
% % === High bound
% sProcess.options.lowpassA.Comment = 'Upper cutoff frequency (Data):';
% sProcess.options.lowpassA.Type = 'value';
% sProcess.options.lowpassA.Value = {29,'Hz ',2};
% sProcess.options.lowpassA.InputTypes = {'data'};
% % === Low bound
% sProcess.options.highpassB.Comment = 'Lower cutoff frequency (Ref):';
% sProcess.options.highpassB.Type = 'value';
% sProcess.options.highpassB.Value = {0,'Hz ',2};
% sProcess.options.highpassB.InputTypes = {'data'};
% % === High bound
% sProcess.options.lowpassB.Comment = 'Upper cutoff frequency (Ref):';
% sProcess.options.lowpassB.Type = 'value';
% sProcess.options.lowpassB.Value = {0,'Hz ',2};
% sProcess.options.lowpassB.InputTypes = {'data'};
% % === TF METHOD ===
% sProcess.options.tfmethod.Comment = {'Hilbert', 'Wavelet', 'STFT', 'TF method:'};
% sProcess.options.tfmethod.Type = 'radio_line';
% sProcess.options.tfmethod.Value = 1;
% % === Width
% sProcess.options.width.Comment = 'Cycles of wavelet:';
% sProcess.options.width.Type = 'value';
% sProcess.options.width.Value = {7, 'Cycles', 0};
% % === WINDOW LENGTH
% sProcess.options.winlength.Comment = 'Estimator window length: ';
% sProcess.options.winlength.Type = 'value';
% sProcess.options.winlength.Value = {0.128, 'ms ', 1};
% sProcess.options.winlength.InputTypes = {'data'};
% % === Low bound
% sProcess.options.winoverlap.Comment = 'Overlap percentage: ';
% sProcess.options.winoverlap.Type = 'value';
% sProcess.options.winoverlap.Value = {0.75, '% ', 1};
% sProcess.options.winoverlap.InputTypes = {'data'};
% % === Freq band
% sProcess.options.freqband.Comment = 'Frequency band of interested:';
% sProcess.options.freqband.Type = 'text';
% sProcess.options.freqband.Value = 'beta';
% sProcess.options.freqband.InputTypes = {'timefreq'};
% % === SCOUTS ===
% sProcess.options.scouts.Comment = 'Use scouts';
% sProcess.options.scouts.Type = 'scout';
% sProcess.options.scouts.Value = [];
% % === Freq band
% sProcess.options.DICStest.Comment = 'DICS test';
% sProcess.options.DICStest.Type = 'text';
% sProcess.options.DICStest.Value = '1';
% === CONNECT INPUT
%sProcess = process_corr1n('DefineConnectOptions', sProcess, 1);
end
%% ===== DEFINE SCOUT OPTIONS =====
function sProcess = DefineConnectOptions(sProcess, isConnNN) %#ok<DEFNU>
% === TIME WINDOW ===
sProcess.options.label1.Comment = '<HTML><B><U>Input options</U></B>:';
sProcess.options.label1.Type = 'label';
sProcess.options.timewindow.Comment = 'Time window:';
sProcess.options.timewindow.Type = 'timewindow';
sProcess.options.timewindow.Value = [];
% === FROM: CONNECTIVITY [1xN] ===
if ~isConnNN
% === FROM: REFERENCE CHANNELS ===
sProcess.options.src_channel.Comment = 'Source channel: ';
sProcess.options.src_channel.Type = 'channelname';
sProcess.options.src_channel.Value = 'name';
sProcess.options.src_channel.InputTypes = {'data'};
% === FROM: ROW NAME ===
sProcess.options.src_rowname.Comment = 'Source rows (names or indices): ';
sProcess.options.src_rowname.Type = 'text';
sProcess.options.src_rowname.Value = '';
sProcess.options.src_rowname.InputTypes = {'timefreq', 'matrix'};
end
% === TO: SENSOR SELECTION ===
sProcess.options.dest_sensors.Comment = 'Sensor types or names (empty=all): ';
sProcess.options.dest_sensors.Type = 'text';
sProcess.options.dest_sensors.Value = 'MEG, EEG';
sProcess.options.dest_sensors.InputTypes = {'data'};
% === SCOUTS ===
sProcess.options.scouts.Comment = 'Use scouts';
if isConnNN
sProcess.options.scouts.Type = 'scout_confirm';
else
sProcess.options.scouts.Type = 'scout';
end
sProcess.options.scouts.Value = [];
sProcess.options.scouts.InputTypes = {'results'};
% Atlas: surface/volume
sProcess.options.isvolume.Comment = '';
sProcess.options.isvolume.Type = 'checkbox';
sProcess.options.isvolume.Value = 0;
sProcess.options.isvolume.Hidden = 1;
% === SCOUT FUNCTION ===
sProcess.options.scoutfunc.Comment = {'Mean', 'Max', 'PCA', 'Std', 'All', 'Scout function:'};
sProcess.options.scoutfunc.Type = 'radio_line';
sProcess.options.scoutfunc.Value = 2;
sProcess.options.scoutfunc.InputTypes = {'results'};
% === SCOUT TIME ===
sProcess.options.scouttime.Comment = {'Before', 'After', 'When to apply the scout function:'};
sProcess.options.scouttime.Type = 'radio_line';
sProcess.options.scouttime.Value = 2;
sProcess.options.scouttime.InputTypes = {'results'};
end
%% ===== FORMAT COMMENT =====
function Comment = FormatComment(sProcess) %#ok<DEFNU>
Comment = sProcess.Comment;
end
%% ===== RUN =====
function OutputFiles = Run(sProcess, sInputsA, sInputsB) %#ok<DEFNU>
% Initialize returned list of files
OutputFiles = {};
if isempty(sInputsA) || isempty(sInputsB)
bst_report('Error', sProcess, sInputsA, 'No inputs');
return;
end
InputsData = sInputsA ;
InputsRef = sInputsB ;
% Get option values
%isFreq = sProcess.options.mirror.Value;
OPTIONS.isHann = sProcess.options.isHann.Value;
OPTIONS.BeamformerType = 2;%sProcess.options.beamformertype.Value;
%ActiveTime = sProcess.options.active_time.Value{1};
% Get option values
OPTIONS.BaselineTime = sProcess.options.baseline_time.Value{1};
OPTIONS.MinVarTime = sProcess.options.minvar_time.Value{1};
%OPTIONS.RefTime = sProcess.options.ref_range.Value{1};
OPTIONS.CORRrange = sProcess.options.corr_range.Value{1};
OPTIONS.Reg = sProcess.options.reg.Value{1};
OPTIONS.SensorTypes = sProcess.options.sensortypes.Value;
OPTIONS.CORRTResolu = sProcess.options.corr_tresolution.Value{1};
OPTIONS.WinSize = sProcess.options.active_window_size.Value{1};
% OPTIONS.BandBoundsData = sProcess.options.nesting.Value;
% OPTIONS.BandBoundsRef = sProcess.options.nested.Value;
% OPTIONS.WinOverlap = sProcess.options.winoverlap.Value{1};
% OPTIONS.SegmentLength = sProcess.options.winlength.Value{1};
% OPTIONS.Width = sProcess.options.width.Value{1};
% Get and check frequencies
OPTIONS.BandBoundsData = sProcess.options.nesting.Value{1};
OPTIONS.BandBoundsRef = sProcess.options.nested.Value{1};
OPTIONS.RefRangeType = sProcess.options.ref_range_type.Value;
OPTIONS.Width = 256;
%OPTIONS.Target = sProcess.options.scouts.Value;
OPTIONS.nRand = sProcess.options.nRand.Value{1};
OPTIONS.TFmethod = 'hilbert';
% switch (sProcess.options.tfmethod.Value)
% case 1, OPTIONS.TFmethod = 'hilbert';
% case 2, OPTIONS.TFmethod = 'wavelet';
% case 3, OPTIONS.TFmethod = 'stft';
% end
switch (sProcess.options.measure.Value)
case 1, OPTIONS.measure = 'cor'; OPTIONS.method = 'SILSC';
case 2, OPTIONS.measure = 'esc'; OPTIONS.method = 'BIPAC';
end
if strcmp(OPTIONS.measure,'esc') || strcmp(OPTIONS.measure,'cfc')
if (min(OPTIONS.BandBoundsData) < 0)
bst_report('Error', sProcess, [], 'This function cannot be used to estimate PAC for nesting frequencies below 0 Hz.');
return;
end
if (max(OPTIONS.BandBoundsData) > min(OPTIONS.BandBoundsRef)) && min(OPTIONS.BandBoundsRef)~=0 && max(OPTIONS.BandBoundsRef)~=0
bst_report('Error', sProcess, [], 'The low and high frequency band cannot overlap.');
return;
end
end
OPTIONS.RepRefNum = 0;%sProcess.options.ref_replicate_num.Value{1};
OPTIONS.RepRefInterval = 0;%sProcess.options.ref_replicate_interval.Value{1};
OPTIONS.RefDelayInterval = sProcess.options.ref_replicate_tresolution.Value{1};
OPTIONS.RefDelayRange = sProcess.options.ref_range.Value{1};
result_comment = sProcess.options.result_comm.Value;
if ~isempty(result_comment)
result_comment = [result_comment ': '];
end
% MinVarTime = OPTIONS.CORRrange;
% ===== LOAD THE REFRENCE DATA =====
% Read the first file in the list, to initialize the loop
RefMat = in_bst(InputsRef(1).FileName, [], 0);
%nRefTrials = size(RefMat.Value,1);
RefFs = 1/(RefMat.Time(2)-RefMat.Time(1));
% if RefMat.Time(1) > OPTIONS.RefTime(1)
% bst_report('Warning', sProcess, InputsData, 'The start for time range of reference signal is reset to the first time point of data');
% OPTIONS.RefTime(1) = RefMat.Time(1);
% end
% if RefMat.Time(end) < OPTIONS.RefTime(2)
% bst_report('Warning', sProcess, InputsData, 'The end for time range of reference signal is reset to the last time point of data');
% OPTIONS.RefTime(2) = RefMat.Time(end);
% end
% RefTimePoints= panel_time('GetTimeIndices', RefMat.Time, [OPTIONS.RefTime(1) OPTIONS.RefTime(2)]);
% RefLength = length(RefTimePoints);
% ===== LOAD THE DATA =====
% Read the first file in the list, to initialize the loop
DataMat = in_bst(InputsData(1).FileName, [], 0);
if strcmpi(InputsData(1).FileType,'data')
nChannels = size(DataMat.F,1);
else
nChannels = size(DataMat.TF,1);
end
Time = DataMat.Time;
nTime = length(Time);
DataFs = 1/(DataMat.Time(2)-DataMat.Time(1));
CorrWindowSize = OPTIONS.WinSize;%.RefTime(end)-OPTIONS.RefTime(1);%RefLength;
if CorrWindowSize <= 0 || OPTIONS.CORRTResolu <= 0
CorrWindowSize = OPTIONS.CORRrange(2)-OPTIONS.CORRrange(1);
OPTIONS.CORRTResolu = 0;
end
if OPTIONS.RepRefNum > 0 && OPTIONS.RepRefInterval > 0
CorrWindowSize = CorrWindowSize + (OPTIONS.RepRefNum-1)*OPTIONS.RepRefInterval;
end
HalfCorrWindowSize = CorrWindowSize/2;
% ===== PROCESS THE TIME WINDOWS =====
if round(RefFs) ~= round(DataFs)
bst_report('Error', sProcess, InputsData, 'The sampling rates of FileA and FileB are different.');
else
Fs = RefFs;
end
if OPTIONS.CORRrange(1) > OPTIONS.CORRrange(2)
bst_report('Error', sProcess, InputsData, 'The setting of time range of interest is incorrect.');
end
if OPTIONS.CORRrange(1) < Time(1)
bst_report('Warning', sProcess, InputsData, 'The start for time range of interest is reset to the first time point of data');
OPTIONS.CORRrange(1) = Time(1);
end
if OPTIONS.CORRrange(2) > Time(end)
bst_report('Warning', sProcess, InputsData, 'The end for time range of interest is reset to the end point of data');
OPTIONS.CORRrange(2) = Time(end);
end
if (OPTIONS.CORRrange(1)+CorrWindowSize) > OPTIONS.CORRrange(2) || OPTIONS.CORRTResolu <= 0;
bst_report('Warning', sProcess, InputsData, 'The active window size is reset to the same as the time range of interest.');
CorrWindowSize = OPTIONS.CORRrange(2) - OPTIONS.CORRrange(1);
OPTIONS.CORRTResolu = 0;
end
CorrRangePoints = panel_time('GetTimeIndices', Time, [OPTIONS.CORRrange(1)+CorrWindowSize OPTIONS.CORRrange(2)]);
if length(CorrRangePoints)<=1
nCORR = 1;
else
nCORR = length((OPTIONS.CORRrange(1)+CorrWindowSize):OPTIONS.CORRTResolu:OPTIONS.CORRrange(2));
if nCORR == 0
bst_report('Error', sProcess, InputsData, 'No correlation windows.');
end
end
CorrTimeList= zeros(nCORR,2);
for i=1:nCORR
CorrTimeList(i,:) = OPTIONS.CORRrange(1) + OPTIONS.CORRTResolu*(i-1) + [0 CorrWindowSize] ;
end
MinVarRangePoint = panel_time('GetTimeIndices', Time, [OPTIONS.MinVarTime(1) OPTIONS.MinVarTime(2)]);
% ===== LOAD CHANNEL FILE =====
% Load channel file
ChannelMat = in_bst_channel(InputsData(1).ChannelFile);
% Find the MEG channels
iChannels = channel_find(ChannelMat.Channel, OPTIONS.SensorTypes);
% ===== LOAD HEAD MODEL =====
% Get channel study
[sChannelStudy, iChannelStudy] = bst_get('ChannelFile', InputsData(1).ChannelFile);
% Load the default head model
HeadModelFile = sChannelStudy.HeadModel(sChannelStudy.iHeadModel).FileName;
sHeadModel = load(file_fullpath(HeadModelFile));
nCorrWindowPoints = length(panel_time('GetTimeIndices', Time, CorrTimeList(1,:)));
for i=2:nCORR
nCorrWindowPoints = min(nCorrWindowPoints,length(panel_time('GetTimeIndices', Time, CorrTimeList(i,:))));
end
iCorrWindowTime = zeros(nCORR, nCorrWindowPoints);
for i = 1:nCORR
if CorrTimeList(i,1) < Time(1) || CorrTimeList(i,2) > Time(end)
% Add an error message to the report
bst_report('Error', sProcess, sInputsA, 'One correlation time window is not within the data time range.');
% Stop the process
return;
end
single_iCorrWindow = panel_time('GetTimeIndices', Time, CorrTimeList(i,:));
if length(single_iCorrWindow) < nCorrWindowPoints
bst_report('Error', sProcess, sInputsA, 'Wrong time range');
% Stop the process
return;
end
iCorrWindowTime(i,:) = single_iCorrWindow(1:nCorrWindowPoints);
end
bst_progress('start', ['Applying process: ' OPTIONS.method], 'Loading reference...', 0, length(InputsRef));
nRef = 0; nOriRef = 0;
nTrials = length(InputsData);
AllRefData = cell(nCORR,1);
RowName = {};
if OPTIONS.RefRangeType == 1
DelayList = ((OPTIONS.RefDelayRange(1)+CorrWindowSize):OPTIONS.RefDelayInterval:OPTIONS.RefDelayRange(2))-HalfCorrWindowSize;
nDelay = length(DelayList);
if nDelay==0;
nDelay=1;
DelayList=0;
end
elseif OPTIONS.RefRangeType == 2
DelayList = OPTIONS.RefDelayRange(1):OPTIONS.RefDelayInterval:OPTIONS.RefDelayRange(2);
nDelay = length(DelayList);
if nDelay==0;
nDelay=1;
DelayList=0;
end
end
RefLength = nCorrWindowPoints;
TargetList = {};
%AllRefData = zeros(nTrials, RefLength,length(InputsRef));
% Reading all the input files in a big matrix
for i = 1:length(InputsRef)
% Read the file #i
RefMat = in_bst(InputsRef(i).FileName, [], 0);
% Check the dimensions of the recordings matrix in this file
if mod(size(RefMat.Value,1), nTrials)~=0
% Add an error message to the report
bst_report('Error', sProcess, InputsData, 'One reference file has a different number of trials.');
% Stop the process
return;
else
nRefSingleFile = (size(RefMat.Value,1) / nTrials);
refData = RefMat.Value;
end
hw = hann(double(nCorrWindowPoints));
for j = 1:nRefSingleFile
fileRefData = refData(j:nRefSingleFile:end,:);
sRate = 1 / (RefMat.Time(2) - RefMat.Time(1));
if strcmp(OPTIONS.measure,'esc') || strcmp(OPTIONS.measure,'cfc')
fileRefData = amp_vec(fileRefData,OPTIONS.BandBoundsRef,sRate,OPTIONS.Width,OPTIONS.TFmethod);
if strcmp(OPTIONS.measure, 'cfc')
fileRefData = (fileRefData / sRate).^2;
end
end
for m=1:nDelay
if strcmpi(InputsRef(i).FileType,'timefreq')
RowName{nRef+m} = [RefMat.RowNames{j} ' (' DelayList(m) 'ms)'];
else
RowName{nRef+m} = [RefMat.Description{j} ' (' DelayList(m) 'ms)'];
end
end
if isfield(RefMat,'Atlas')
if ~isempty(RefMat.Atlas)
TargetList{nOriRef+j}=RefMat.Atlas.Scouts(j);
end
end
if OPTIONS.RefRangeType == 1
for m=1:nDelay
refTime = OPTIONS.RefDelayRange(1) + OPTIONS.RefDelayInterval*(m-1) + [0 CorrWindowSize];
if (RefMat.Time(1) > refTime(1)) || (RefMat.Time(end) < refTime(2))
% Add an error message to the report
bst_report('Error', sProcess, InputsData, 'The selected range for extracting reference signal is out of range.');
% Stop the process
return;
end
iRefWindow = panel_time('GetTimeIndices', Time, refTime);
iRefWindow = iRefWindow(1:nCorrWindowPoints);
cRefData = fileRefData(:,iRefWindow);
if OPTIONS.isHann
for n = 1:nTrials
cRefData(n,:) = cRefData(n,:).*hw';
end
end
%else
% RefMat.Value = RefMat.Value(j:nRefSingleFile:end,RefTimePoints);
% RefMatAvg = mean(RefMat.Value, 2);
% fileRefData = bst_bsxfun(@minus, RefMat.Value, RefMatAvg);
%end
AllRefData{1} = cat(3,AllRefData{1}, cRefData);
end
elseif OPTIONS.RefRangeType == 2
for k=1:nCORR
for m=1:nDelay
refTime = CorrTimeList(k,:) + DelayList(m);
if (RefMat.Time(1) > refTime(1)) || (RefMat.Time(end) < refTime(2))
% Add an error message to the report
bst_report('Error', sProcess, InputsData, 'The selected range for extracting reference signal is out of range.');
% Stop the process
return;
end
iRefWindow = panel_time('GetTimeIndices', Time, refTime);
iRefWindow = iRefWindow(1:nCorrWindowPoints);
cRefData = fileRefData(:,iRefWindow);
if OPTIONS.isHann
for n = 1:nTrials
cRefData(n,:) = cRefData(n,:).*hw';
end
end
%else
% RefMat.Value = RefMat.Value(j:nRefSingleFile:end,RefTimePoints);
% RefMatAvg = mean(RefMat.Value, 2);
% fileRefData = bst_bsxfun(@minus, RefMat.Value, RefMatAvg);
%end
AllRefData{k} = cat(3,AllRefData{k}, cRefData);
end
end
end
end
nRef = nRef + nRefSingleFile*nDelay;
nOriRef = nOriRef + nRefSingleFile;
bst_progress('inc',1);
end
RepRefIntervalPoints = round(OPTIONS.RepRefInterval / (Time(2) - Time(1)));
if OPTIONS.RepRefNum > 0 && RepRefIntervalPoints > 0
for k=1:nCORR
% nRefTrials, RefLength, nRef
RepRefData = zeros(nTrials,RefLength + (OPTIONS.RepRefNum-1)*RepRefIntervalPoints,nRef*OPTIONS.RepRefNum);
% Relicate Reference Signal
for i = 1:OPTIONS.RepRefNum
RepRefData(:,(i-1)*RepRefIntervalPoints + (1:RefLength),(i-1)*nRef + (1:nRef)) = AllRefData{k};
end
nRef = nRef*OPTIONS.RepRefNum;
AllRefData{k} = RepRefData;
end
else
OPTIONS.RepRefNum = 1;
end
for i=1:nCORR
AllRefData{i} = permute(AllRefData{i},[2 3 1]); %RefLength, nRef, nRefTrials
end
AllCovRef = cell(nCORR,1);
AllRefDataCell = AllRefData;
AllRefDataCellorig = AllRefDataCell;
clear AllRefData;
for m=1:nCORR
if OPTIONS.RefRangeType == 1 && m > 1
if nRef > 1
AllCovRef{m}.origcov = AllCovRef{1}.origcov;
AllCovRef{m}.originv_cov=AllCovRef{1}.originv_cov;
end
AllCovRef{m}.cov = AllCovRef{1}.cov;
AllCovRef{m}.inv_cov = AllCovRef{1}.inv_cov;
AllRefDataCellorig{m} = AllRefDataCellorig{1};
AllRefDataCell{m} = AllRefDataCell{1};
continue;
end
AllRefData = AllRefDataCell{m};
covRef = zeros(nRef,nRef);
for i = 1:nTrials
singleRefTrial = squeeze(AllRefData(:,:,i))';
singleRefTrialAvg = mean(singleRefTrial, 2);
AllRefData(:,:,i) = bst_bsxfun(@minus, singleRefTrial, singleRefTrialAvg)';
covRef = covRef + AllRefData(:,:,i)'*AllRefData(:,:,i);
end
AllRefDataCellorig{m}=AllRefData;
if nRef > 1
origAllRefData = AllRefDataCell{m};
covRef = covRef/(nTrials*RefLength-1);
[v,d]=eig(covRef);
C=v*diag(diag(d).^(-0.5));
origcovRef = covRef;
for i = 1:nTrials
singleRefTrial = C'*squeeze(origAllRefData(:,:,i))';
singleRefTrialAvg = mean(singleRefTrial, 2);
AllRefData(:,:,i) = bst_bsxfun(@minus, singleRefTrial, singleRefTrialAvg)';
covRef = covRef + AllRefData(:,:,i)'*AllRefData(:,:,i);
end
AllRefDataCell{m} = AllRefData;
% figure;
% subplot(2,1,1);
% imagesc(origcovRef);colorbar;
% subplot(2,1,2);
% imagesc(covRef/(nTrials*RefLength-1));colorbar;
AllCovRef{m}.origcov=origcovRef;
% Calculate the inverse of covRef
AllCovRef{m}.originv_cov=inv(AllCovRef{m}.origcov);
end
covRef = covRef / (nTrials*RefLength-1);
AllCovRef{m}.cov=covRef;
% Calculate the inverse of covRef
AllCovRef{m}.inv_cov=inv(covRef);
end
% if nRef == 1
% covRef_inv = inv(covRef);
% else
% [U,S,V] = svd(covRef);
% S = diag(S); % Covariance = Cm = U*S*U'
% Si = diag(1 ./ (S + S(1) * Reg / 100)); % 1/(S^2 + lambda I)
% covRef_inv = V*Si*U'; % U * 1/(S^2 + lambda I) * U' = Cm^(-1)
% end
% Find the indices for covariance calculation
%iMinVarTime = panel_time('GetTimeIndices', Time, MinVarTime);
% Initialize the covariance matrices
MinVarCov = zeros(nChannels, nChannels);
CorrCovOfNumerator = zeros(nChannels, nRef ,nCORR);
CorrCovOfDenominator = zeros(nChannels, nChannels, nCORR);
CorrCovOfNumeratorOrig = zeros(nChannels, nRef ,nCORR);
nTotalMinVar = zeros(nChannels, nChannels, nCORR);
nTotalCorrCovOfNumerator = zeros(nChannels, nRef, nCORR);
nTotalCorrCovOfDenominator = zeros(nChannels, nChannels, nCORR);
nTotalCorrCovOfNumeratorOrig = zeros(nChannels, nRef, nCORR);
% Extract the covariance matrix of the used channels
NoiseCovMat = load(file_fullpath(sChannelStudy.NoiseCov.FileName));
NoiseCov = NoiseCovMat.NoiseCov(iChannels,iChannels);
bst_progress('start', ['Applying process: ' OPTIONS.method], 'Calculating covariance matrices...', 0, length(InputsData)*nCORR);
if OPTIONS.BaselineTime(1)==OPTIONS.BaselineTime(2)
iBaselineTime = [];
else
iBaselineTime = panel_time('GetTimeIndices', Time, OPTIONS.BaselineTime);
end
% Reading all the input files in a big matrix
for i = 1:nTrials
% Read the file #i
DataMat = in_bst(InputsData(i).FileName, [], 0);
% Check the dimensions of the recordings matrix in this file
if (size(DataMat.F,1) ~= nChannels) || (size(DataMat.F,2) ~= nTime)
% Add an error message to the report
bst_report('Error', sProcess, InputsData, 'One file has a different number of channels or a different number of time samples.');
% Stop the process
return;
end
if strcmp(OPTIONS.measure,'esc') && OPTIONS.BandBoundsData(1)~=0 && OPTIONS.BandBoundsData(2)~=0
DataMat.F = bp_vec(DataMat.F,OPTIONS.BandBoundsData,sRate,OPTIONS.Width,OPTIONS.TFmethod);
end
% Get good channels
iGoodChan = find(DataMat.ChannelFlag == 1);
%for j = 1:nCORR
% j=1;
if ~isempty(iBaselineTime)
% Remove average
FavgActive = mean(DataMat.F(iGoodChan,iBaselineTime), 2);
DataMinVar = bst_bsxfun(@minus, DataMat.F(iGoodChan,MinVarRangePoint), FavgActive);
else
DataMinVar = DataMat.F(iGoodChan,MinVarRangePoint);
end
% % Average baseline values
% FavgMinVar = mean(DataMat.F(iGoodChan,MinVarRangePoint), 2);
% % Remove average
% DataMinVar = bst_bsxfun(@minus, DataMat.F(iGoodChan,MinVarRangePoint), FavgMinVar);
% Compute covariance for this file
fileMinVarCov = DataMat.nAvg .* (DataMinVar * DataMinVar');
MinVarCov(iGoodChan,iGoodChan,j) = bst_bsxfun(@plus,MinVarCov(iGoodChan,iGoodChan), fileMinVarCov);
nTotalMinVar(iGoodChan,iGoodChan,j) = nTotalMinVar(iGoodChan,iGoodChan) + RefLength*DataMat.nAvg;
%end
% end
% if ~isFreq
% % Average baseline values
% FavgMinVar = mean(DataMat.F(iGoodChan,iMinVarTime), 2);
% % Remove average
% DataMinVar = bst_bsxfun(@minus, DataMat.F(iGoodChan,iMinVarTime), FavgMinVar);
% % Compute covariance for this file
% fileMinVarCov = DataMat.nAvg .* (DataMinVar * DataMinVar');
% else
% [Cxy,freq] = crossSpectrum(DataMat.F(iGoodChan,iMinVarTime), DataMat.F(iGoodChan,iMinVarTime), Fs, SegmentLength, []);
% fileMinVarCov = zeros(size(Cxy,1),size(Cxy,2));
% nCxy = 0;
% for f = 1:length(freq)
% if (freq(f) <= BandBounds(2)) && (freq(f) >= BandBounds(1))
% fileMinVarCov = fileMinVarCov + Cxy(:,:,f);
% nCxy = nCxy + 1;
% end
% end
% if nCxy > 0
% fileMinVarCov = DataMat.nAvg * fileMinVarCov / nCxy;
% end
% end
%
% %fileMinVarCov = DataMinVar * DataMinVar';
% % Add file covariance to accumulator
% MinVarCov(iGoodChan,iGoodChan) = MinVarCov(iGoodChan,iGoodChan) + fileMinVarCov;
% nTotalMinVar(iGoodChan,iGoodChan) = nTotalMinVar(iGoodChan,iGoodChan) + length(iMinVarTime)*DataMat.nAvg;
for j = 1:nCORR
AllRefData = AllRefDataCell{j};
if strcmp(OPTIONS.measure,'cfc') || strcmp(OPTIONS.measure,'coh')
%if ~isCrossFreq
[Cxy,freq,Cxx] = crossSpectrum(DataMat.F(iGoodChan,iCorrWindowTime(j,:)), AllRefData(:,:,i)', Fs, OPTIONS.SegmentLength, [], OPTIONS.WinOverlap);
%else
% [Cxy,freq,Cxx] = crossSpectrumCrossFreq(DataMat.F(iGoodChan,iCorrWindowTime(j,:)), AllRefData(:,:,i)', Fs, BandBoundsRef, SegmentLength, [], WinOverlapLength);
%end
fileDataInCorrWindowCovD = zeros(size(Cxx,1),size(Cxx,2));
fileDataInCorrWindowCovN = zeros(size(Cxy,1),size(Cxy,2));
nCxy = 0;
for f = 1:length(freq)
if (freq(f) <= OPTIONS.BandBoundsData(2)) && (freq(f) >= OPTIONS.BandBoundsData(1))
fileDataInCorrWindowCovD = fileDataInCorrWindowCovD + Cxx(:,:,f);
fileDataInCorrWindowCovN = fileDataInCorrWindowCovN + Cxy(:,:,f);
nCxy = nCxy + 1;
end
end
if nCxy > 0
fileDataInCorrWindowCovN = fileDataInCorrWindowCovN / nCxy;
fileDataInCorrWindowCovD = fileDataInCorrWindowCovD / nCxy;
else
bst_report('Error', sProcess, InputsData, 'Cannot find signal within this frequency range.');
end
else
% Average baseline values
DataAvg = mean(DataMat.F(iGoodChan,iCorrWindowTime(j,:)), 2);
% Remove average
DataInCorrWindow = bst_bsxfun(@minus, DataMat.F(iGoodChan,iCorrWindowTime(j,:)), DataAvg);
% Compute covariance for this file
fileDataInCorrWindowCovN = (DataInCorrWindow * AllRefData(:,:,i));
fileDataInCorrWindowCovD = (DataInCorrWindow * DataInCorrWindow');
end
if nRef > 1
fileDataInCorrWindowCovNorig = (DataInCorrWindow * AllRefDataCellorig{j}(:,:,i));
% Add file covariance to accumulator
CorrCovOfNumeratorOrig(iGoodChan,:,j) = bst_bsxfun(@plus,CorrCovOfNumeratorOrig(iGoodChan,:,j), fileDataInCorrWindowCovNorig);
nTotalCorrCovOfNumeratorOrig(iGoodChan,:,j) = nTotalCorrCovOfNumeratorOrig(iGoodChan,:,j) + RefLength;
end
% Add file covariance to accumulator
CorrCovOfNumerator(iGoodChan,:,j) = bst_bsxfun(@plus, CorrCovOfNumerator(iGoodChan,:,j), fileDataInCorrWindowCovN);
nTotalCorrCovOfNumerator(iGoodChan,:,j) = nTotalCorrCovOfNumerator(iGoodChan,:,j) + RefLength;
% Add file covariance to accumulator
CorrCovOfDenominator(iGoodChan,iGoodChan,j) = bst_bsxfun(@plus, CorrCovOfDenominator(iGoodChan,iGoodChan,j), fileDataInCorrWindowCovD);
nTotalCorrCovOfDenominator(iGoodChan,iGoodChan,j) = nTotalCorrCovOfDenominator(iGoodChan,iGoodChan,j) + RefLength;
bst_progress('inc',1);
end
end
% Remove zeros from N matrix
nTotalMinVar(nTotalMinVar <= 1) = 2;
nTotalCorrCovOfNumerator(nTotalCorrCovOfNumerator <= 1) = 2;
nTotalCorrCovOfDenominator(nTotalCorrCovOfDenominator <=1 ) = 2;
nTotalCorrCovOfNumeratorOrig(nTotalCorrCovOfNumeratorOrig <= 1) = 2;
% Divide final matrix by number of samples
MinVarCov = MinVarCov ./ (nTotalMinVar-1);
CorrCovOfNumerator = CorrCovOfNumerator ./ (nTotalCorrCovOfNumerator-1);
CorrCovOfDenominator = CorrCovOfDenominator ./ (nTotalCorrCovOfDenominator-1);
CorrCovOfNumeratorOrig = CorrCovOfNumeratorOrig ./ (nTotalCorrCovOfNumeratorOrig-1);
CovCovOfNumerator = zeros(nChannels,nChannels,nCORR);
for j = 1:nCORR
CovCovOfNumerator(:,:,j) = CorrCovOfNumerator(:,:,j)*AllCovRef{j}.inv_cov*CorrCovOfNumerator(:,:,j)';
end
% ===== PROCESS =====
% Processing iChannels
%%%% TO EDIT %%%%%
% Get Study
% Extract the covariance matrix of the used channels
%ActiveCov = ActiveCov(iChannels,iChannels);
%NoiseCovMat = load(file_fullpath(sChannelStudy.NoiseCov.FileName));
%NoiseCov = NoiseCovMat.NoiseCov(iChannels,iChannels);
MinVarCov = MinVarCov(iChannels,iChannels,:);
CovCovOfNumerator = CovCovOfNumerator(iChannels,iChannels,:);
CorrCovOfNumerator = CorrCovOfNumerator(iChannels,:,:);
CorrCovOfDenominator = CorrCovOfDenominator(iChannels,iChannels,:);
CorrCovOfNumeratorOrig = CorrCovOfNumeratorOrig(iChannels,:,:);
% Normalize the regularization parameter
% eigValues = eig(MinVarCov);
% Reg_alpha = Reg * max(eigValues);
% Calculate the inverse of (Cm+alpha*I)
% if isFreq
% if strcmpi(InputsData(1).FileType,'data')
% MinVarCov = CorrCovOfDenominator;
% MinVarCov_inv = CorrCovOfDenominator;
% end
% for j=1:nCORR
MinVarCov_inv(:,:,1) = pInv(MinVarCov(:,:,1), OPTIONS.Reg);
% end
% else
% tmp = pInv(MinVarCov, Reg);
% MinVarCov_inv = zeros(length(iChannels),length(iChannels),nCORR);
% for j=1:nCORR
% MinVarCov_inv(:,:,j) = tmp;
% end
% end
% Get forward field
Kernel = sHeadModel.Gain(iChannels,:);
%Kernel(abs(Kernel(:)) < eps) = eps; % Set zero elements to strictly non-zero
[nChannels ,nSources] = size(Kernel); % size of Gain Matrix
% ===== CALCULATE NEURAL ACTIVITY INDEX AND FILTERS =====
CorrMaps = zeros(nSources/3,nCORR);
AmpMaps = zeros(nSources/3,nCORR);
Ori = zeros(3,nSources/3,nCORR);
spatialFilter = zeros(nSources/3, nChannels,nCORR); % calculate filter for outputs
bst_progress('start', ['Applying process: ' OPTIONS.method], 'Calculating spatial filters...', 0, nCORR*nSources/3);
%testNum = str2double(sProcess.options.DICStest.Value);
if OPTIONS.BeamformerType == 1
for r = 1:3:nSources % calculate noise power for index
r2 = (r+2)/3;
Gr = Kernel(:,r:r+2);
for j=1:nCORR
W = pinv(Gr' * MinVarCov_inv * Gr)* Gr' * MinVarCov_inv;
% [u,s,v] = svd(real(W * MinVarCov(:,:,j) * W'));
[u,s,v] = svd( real(W * CovCovOfNumerator(:,:,j)* W'));
if s(1) > 100*s(2)
csd = s(1,1);
%w = u(:,1);
else
csd = trace(s);
%w = [1 1 1]/sqrt(3);
end
[u,s,v] = svd( real(W * CorrCovOfDenominator(:,:,j)* W'));
if s(1) > 100*s(2)
pow = s(1,1);
else
pow = trace(s);
end
%CorrMaps(r2,j) = sqrt((w*CovCovOfNumerator(:,:,j)*w')/(w*CorrCovOfDenominator(:,:,j)*w'));
CorrMaps(r2,j) = sqrt(csd/pow);
spatialFilter(r2,:,j) = w;
bst_progress('inc',1);
end
end
else
usedKernel = mat2cell(Kernel,nChannels,3*ones(1,nSources/3));
Cmat = cellfun(@(x)MinVarCov_inv*x, usedKernel, 'UniformOutput', false);
Dmat = cellfun(@(x,y)x'*y, usedKernel, Cmat, 'UniformOutput', false);
for j=1:nCORR
Pmat = cellfun(@(x) x'*CovCovOfNumerator(:,:,j)*x, Cmat, 'UniformOutput', false);
Qmat = cellfun(@(x) x'*CorrCovOfDenominator(:,:,j)*x, Cmat, 'UniformOutput', false);
invQmat = cellfun(@(x)pInv(x,0.0000000001),Qmat, 'UniformOutput', false);
invQPmat = cellfun(@mtimes, invQmat, Pmat, 'UniformOutput', false);
[eigVecmat, eigValmat] = cellfun(@eig, invQPmat, 'UniformOutput', false);
if min(size(eigValmat{1,1}))>1
eigValmat = cellfun(@diag, eigValmat, 'UniformOutput', false);
end
[~, imaxmat] = cellfun(@max, eigValmat, 'UniformOutput', false);
Orimat = cellfun(@(x,y)x(:,y), eigVecmat, imaxmat, 'UniformOutput', false);
Ori(:,:,j) = cell2mat(Orimat);
bst_progress('inc',nSources/6);
tmpMat = cellfun(@(x,y)x'*y*x, Orimat, Dmat, 'UniformOutput', false);
tmpMat2 = cellfun(@mtimes, Cmat, Orimat, 'UniformOutput', false);
sfmat = cellfun(@mrdivide, tmpMat2, tmpMat, 'UniformOutput', false);
vcMat = cellfun(@(x)x'*NoiseCov*x, sfmat,'UniformOutput', false);
tmpMat = cellfun(@(x)x'*CovCovOfNumerator(:,:,j)*x, sfmat);
tmpMat2 = cellfun(@(x)x'*CorrCovOfDenominator(:,:,j)*x, sfmat);
CorrMat = bsxfun(@rdivide, tmpMat, tmpMat2);
CorrMaps(:,j) = arrayfun(@sqrt, CorrMat);
tmpMat = cell2mat(vcMat);
AmpMat =bsxfun(@rdivide, tmpMat2, tmpMat);
AmpMaps(:,j) = AmpMat;%arrayfun(@sqrt, AmpMat);
vcMat = cellfun(@sqrt, vcMat, 'UniformOutput', false);
ImagingKernel = cellfun(@mrdivide, sfmat, vcMat, 'UniformOutput', false);
spatialFilter(:,:,j) = cell2mat(ImagingKernel)';
bst_progress('inc',nSources/6);
end
% Cr = MinVarCov_inv * Gr;
% Dr = Gr' * Cr;
%
% Pr = Cr' * CovCovOfNumerator(:,:,j)* Cr;
% Qr = Cr' * CorrCovOfDenominator(:,:,j)* Cr;
% %Qr = Cr' * NoiseCov* Cr;
%
% % Regularize the matrix Q to avoid singular problem
% invQr = pInv(Qr,0.0000000001);
%
% % Compute the dipole orientation
% % (the eigenvector corresponding to maximum eigenvalue of inv(Q)*P)
% [eigVectors,eigValues] = eig(invQr*Pr);
% % check whether eigValues are saved as matrix or vector
% if min(size(eigValues))==1
% [tmp, imax] = max(eigValues);
% else
% [tmp, imax] = max(diag(eigValues));
% end
% DipoleOri = eigVectors(:,imax);
%
% Ori(:,r2,j) = DipoleOri;
% w = Cr*DipoleOri/(DipoleOri'*Dr*DipoleOri);
% spatialFilter(r2,:,j) = w';
% CorrMaps(r2,j) = sqrt((w'*CovCovOfNumerator(:,:,j)*w)/(w'*CorrCovOfDenominator(:,:,j)*w));
% AmpMaps(r2,j)=sqrt((w'*CorrCovOfDenominator(:,:,j)*w)/(w'*NoiseCov*w));
% % if imag(CorrMaps(r2,j))~=0
% % disp('ERROR');
% % end
%