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runFullMT2.m
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runFullMT2.m
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function funPsd = runFullMT2(funTs,W,K,win,onsets,ondurs,mask,memFlag,skipSVD,skipPSD,verbose,taperPerm,phaseRand)
% Wrapper for the Chronux's mtspectrumc function for multitaper estimation of
% pds spectra, compatible with MRI data imported by MRIread.m.
%
% win [int int]: time window width and step size, in seconds
%
% Other features are implemented on top or adapted from of Chronux:
% -missing data slepian tapers catenate consecutive runs
% -...
%
% Parameterization is simplified to use either the halfbandwidth parameter W or the number of tapers.
% Alternatively, precomputed tapers can be input as K to save
% time (useful when missing data slepian are used).
% See funPsd.psd for other useful parameters.
% funPsd.tr reflects the frequency resolution in Hz*1000
if ~exist('verbose','var') || isempty(verbose); verbose = true; end
if ~exist('memFlag','var') || isempty(memFlag); memFlag = false; end
if ~exist('W','var'); W = []; end
if ~exist('K','var'); K = []; end
if isempty(K) && isempty(W); K = 1; end
if ~exist('skipSVD','var') || isempty(skipSVD); skipSVD = false; end
if ~exist('skipPSD','var') || isempty(skipPSD); skipPSD = false; end
if ~exist('mask','var'); mask = []; end
if ~exist('onsets','var'); onsets = []; end
if ~exist('ondurs','var'); ondurs = []; end
if isempty(onsets)
if isfield(funTs.dsgn,'onsets')
onsets = funTs.dsgn.onsets;
else
onsets = funTs.dsgn.onsetList;
end
end
if isempty(ondurs)
if isfield(funTs.dsgn,'ondurs')
ondurs = funTs.dsgn.ondurs;
else
ondurs = funTs.dsgn.ondurList;
end
end
% if K==1; skipSVD = true; end
if iscell(funTs)
for I = 1:numel(funTs)
funPsd{I} = runFullMT2(funTs{I},W,K,win,onsets,ondurs,mask,memFlag,skipSVD,skipPSD,verbose,taperPerm,phaseRand);
end
elseif isstruct(funTs)
for I = 1:numel(funTs)
cohFperm = [0 1.1];
funPsd(I) = doIt(funTs(I),W,K,win,onsets,ondurs,mask,memFlag,skipSVD,skipPSD,verbose,taperPerm,phaseRand,[],cohFperm);
% % taperPerm = 2^7;
% % phaseRand = 0;
% for K = [4 8]
% cohFperm = [0 1.1];
% funPsd(I) = doIt(funTs(I),W,K,win,mask,memFlag,skipSVD,skipPSD,verbose,taperPerm,phaseRand,[],cohFperm);
% funPsd(I).svd.nPerm = taperPerm;
% % funTs(2:end) = [];
% % save tmp2 -v7.3
% close all
% % f0List = [0 0.00301408 0.0572676 0.072338 0.180845 0.186873 0.277296 0.72338 0.747492 0.940394];
% % f0List = [1.33825 1.46183 1.46484];
% % f0List = [1.33222 1.36538 1.46183];
% % f0List = [0.093465 0.271267 0.60583];
% f0List = [0.0542535 0.60583];
% plotPerm4(funPsd(I).svd,f0List)
% % plotPerm4(funPsd(I).svd)
% end
% keyboard
% % funTs(2:end) = [];
% % save tmp -v7.3
end
else
dbstack; error('this should not happen')
end
function funPsd = doIt(funTs,W,K,win,onsetList,durList,mask,memFlag,skipSVD,skipPSD,verbose,taperPerm,phaseRand,cohF,cohFperm)
if length(K)>1; tp = K; K = size(tp,2); else tp = []; end
if ~exist('win','var'); win = []; end
if ~exist('onsetList','var'); onsetList = []; end
if ~exist('durList','var'); durList = []; end
if isempty(win); win = [inf 0]; end
windFlag = 1;
if funTs.nvoxels==1; if verbose; disp('only one timeseries, skipping SVD'); end; skipSVD = true; end
param.win = win;
param.onsetList = onsetList;
param.durList = durList;
if param.win(1)==inf; skipGram = true; skipTrialGram = true; else skipGram = false; skipTrialGram = false; end
if isempty(param.onsetList)
skipTrialGram = true;
else
if all((size(param.onsetList)==1)==[1 0])
dbstack; error('onsetList must be a column vector')
end
end
% if length(param.win)>2
% param.onsetList = param.win(3:end)';
% param.win(3:end) = [];
% else
% param.onsetList = [];
% end
%% Mask
if ~isempty(mask)
funTs = applyMask(funTs,mask);
end
%% Assert
if isfield(funTs,'vec') && ~isempty(funTs.vec); tmp = all(funTs.vec==0,1); else tmp = all(funTs.vol==0,4); end
if any(tmp(:)); warning('Some voxels are all 0s. Adjust your mask to avoid later problems'); end
%% Set parameters
tr = funTs.tr/1000;
Wflag = ~isempty(W);
Kflag = ~isempty(K);
tpFlag = ~isempty(tp); if tpFlag; Wflag = false; Kflag = false; end
if ~isfield(funTs,'nruns'); funTs.nruns = 1; end
%%% Window size
if param.win(1)==inf
%%%% single-window over the full time series
T = tr.*funTs.nframes;
param.win(1) = funTs.nframes;
param.win(2) = 0;
else
%%%% multiple time windows
% param.win = ceil(param.win./tr).*tr;
if length(param.win)==1
param.win(2) = 1;
end
T = param.win(1)*tr;
end
% if param.win(2)==inf
% if verbose; disp('Param for psd. Svd uses twice K of psd'); end
% else
% if verbose; disp('Param for time-reolved psd, full psd uses ceil(K/2). Svd uses twice K of psd'); end
% end
if Wflag && Kflag
error('Cannot specify both W and K');
elseif Wflag
[TW,W,K] = W2K(T,W);
elseif Kflag || tpFlag
if tpFlag && verbose
disp('using precomputed tapers');
end
[TW,W,K] = K2W(T,K,verbose);
end
param.tapers = [TW K];
%% xgram parameters
if ~skipGram
param.win(3) = ceil(funTs.nframes / (param.win(2)));
allWin = repmat(1:param.win(1),[param.win(3) 1]);
allWin = allWin + (((1:param.win(3))-1)*param.win(2))'; % win x t
allWin(any(allWin>funTs.nframes,2),:) = [];
% allWin(end+1,:) = (funTs.nframes-param.win(1)+1:funTs.nframes)';
param.win(3) = [];
param.win = param.win*tr;
if verbose && param.win(2)~=inf
disp(['win(1) (window width): ' num2str(param.win(1),'%0.3f') 'sec or ' num2str(param.win(1)/tr) 'vol'])
disp(['win(2) (step size) : ' num2str(param.win(2),'%0.3f') 'sec or ' num2str(param.win(2)/tr) 'vol'])
end
else
allWin = [];
end
allWin = unique(allWin,'rows');
%% trial-locked xgram parameters
if ~skipTrialGram
allWin; % [win X timeIndex]
onsetList = param.onsetList;
if windFlag; onsetList(1) = []; end
winSz = param.win(1)./tr;
n = funTs.nframes;
% n = max(allWin(:));
nWin = size(allWin,1);
nTrial = size(onsetList,1);
allWin2 = repmat({zeros(n,nWin)},[nTrial 1]);
for winInd = 1:nWin
for trialInd = 1:nTrial
if trialInd == 1
allWin2{trialInd}(allWin(winInd,:),winInd) = 1;
else
% offsetInd = floor((onsetList(trialInd) - onsetList(1)) ./ tr);
offsetInd = floor((onsetList(trialInd) - onsetList(1)) ./ tr) + 1;
tInd = allWin(winInd,:) + offsetInd;
tInd(tInd>n) = [];
allWin2{trialInd}(tInd,winInd) = 1;
end
end
end
%%% allign windows to the end of the timeseries
timeShiftVol = n-find(allWin2{1}(:,end),1,'last');
% imagesc(any(cat(3,allWin2{:}),3))
% find(allWin2{1}(:,end),1,'last')
% find(allWin2{2}(:,end),1,'last')
% find(allWin2{3}(:,end),1,'last')
% find(allWin2{4}(:,end),1,'last')
% find(allWin2{5}(:,end),1,'last')
for trialInd = 1:nTrial
allWin2{trialInd}(end-timeShiftVol+1:end,:) = false;
allWin2{trialInd} = circshift(allWin2{trialInd},timeShiftVol,1);
end
%%% remove windows exceeding timeseries
allWin3 = any(cat(3,allWin2{:}),3); % [win X time]
endInd = find(allWin3(end,:),1)+1;
% endInd = find(allWin3(end,:),1);
for trialInd = 1:nTrial
allWin2{trialInd}(:,endInd:end) = [];
end
% allWin3(:,endInd:end) = [];
% %%% remove completely overlapping windows
% endInd = find(sum((allWin2{1}+allWin2{2}(:,1))==2,1)==winSz);
% for trialInd = 1:nTrial
% allWin2{trialInd}(:,endInd:end) = [];
% end
% % allWin3(:,endInd:end) = [];
%%% remove completely overlapping windows
[a,endInd] = max(sum(allWin2{end}==allWin2{end-1}(:,end),1));
if a<n; endInd = endInd - 1; end
for trialInd = 1:nTrial
allWin2{trialInd}(:,1:endInd) = [];
end
% imagesc(any(cat(3,allWin2{:}),3))
% %%% visualize
% figure('WindowStyle','docked');
% ht = tiledlayout(1,nTrial+1); ax = {};
% for trialInd = 1:nTrial
% ax{trialInd} = nexttile([1 1]);
% imagesc(allWin2{trialInd})
% end
% ax{trialInd} = nexttile([1 1]);
% imagesc(allWin3)
%%% put back to standard index format
% winSz2 = unique(sum(allWin3,1));
% if length(winSz2)~=1; error('badly defined trial-locked windows'); end
% allWin4 = zeros(size(allWin3,2),winSz2);
% for winInd = 1:size(allWin3,2)
% allWin4(winInd,:) = find(allWin3(:,winInd));
% end
% allWinTrialLock = allWin4; % [win X timeIndex]
allWin3 = cat(3,allWin2{:}); % [win X time X trial]
figure('WindowStyle','docked');
% imagesc(any((allWin3(:,:,end)),3))
imagesc(any(allWin3,3))
winSz2 = unique(sum(allWin3,1));
if length(winSz2)~=1; dbstack; error('badly defined trial-locked windows'); end
allWin4 = zeros(winSz2,size(allWin3,2),size(allWin3,3));
for winInd = 1:prod(size(allWin3,[2 3]))
allWin4(:,winInd) = find(allWin3(:,winInd));
end
allWinTrialLock = permute(allWin4,[2 1 3]); % [win X timeIndex]
% allWinTrialLock = reshape(allWinTrialLock,size(allWinTrialLock,1),prod(size(allWinTrialLock,[2 3])));
clear allWin2 allWin3 allWin4 winSz2
else
allWinTrialLock = [];
end
%% initiate stuff
funPsd = funTs;
[funPsd.vol] = deal([]);
[funPsd.vec] = deal([]);
if ~isfield(funTs,'volInfo'); [funTs.volInfo] = deal(strjoin({'X' 'Y' 'Z' 'time/freq' 'taper/mode' 'run'},' x ')); end
if ~isfield(funTs,'vecInfo'); [funTs.vecInfo] = deal(strjoin({'time/freq' 'vox' 'taper/mode' 'run'},' x ')); end
tmp = strsplit(funTs(1).vecInfo,' x '); tmp{1} = 'freq/time'; tmp = strjoin(tmp,' x ');
[funPsd.vecInfo] = deal(tmp);
tmp = strsplit(funTs(1).volInfo,' x '); tmp{4} = 'freq/time'; tmp = strjoin(tmp,' x ');
[funPsd.volInfo] = deal(tmp);
param.Fs = 1/tr;
param.complex = 1;
funTs = vol2vec(funTs);
for sInd = 1:length(funTs)
%% Get tapers
if ~isfield(funTs(sInd),'t') || isempty(funTs(sInd).t)
funTs(sInd).t = (0:funTs(sInd).tr/1000:(funTs(sInd).nframes-1)*funTs(sInd).tr/1000)';
end
if skipPSD
dbstack; error('code that')
else
%%% full timeseries
if verbose; disp('getting taper for full timeseries'); end
K = param.tapers(2);
tr = funTs(sInd).tr/1000;
N = funTs(sInd).nframes;
[TP.full.tp,TP.full.eigs] = getTapers(K,tr,N);
TP.full.t = funTs(sInd).t;
% [~,funPsd(sInd).psd.f,funPsd(sInd).psd.tp] = mtspectrumc4(funTs(sInd).vec(:,1,:,1), paramPsd,funTs(sInd).t(:,:,:,:,:,1),tp);
%%% time-resolved
if ~skipGram
if verbose; disp('getting taper for time-resolved analysis'); end
K = param.tapers(2);
tr = funTs(sInd).tr/1000;
N = size(allWin,2);
[TP.gram.tp,TP.gram.eigs] = getTapers(K,tr,N);
TP.gram.t = funTs.t(allWin(1,:));
else
TP.gram.tp = [];
TP.gram.eigs = [];
TP.gram.t = [];
end
%%% trial-locked
if ~skipTrialGram
%%%% Regular tapers repeated at each trial
E = length(param.onsetList);
TP.trialGram.tp = repmat(TP.gram.tp,[1 1 E]);
TP.trialGram.eigs = TP.gram.eigs;
TP.trialGram.t = TP.gram.t;
%%%% Missing data tapers (experimental)
K = param.tapers(2);
tr = funTs(sInd).tr/1000;
t = funTs(sInd).t;
t = t(allWinTrialLock(1,:));
N = length(t);
[TP.trialGramMD.tp,TP.trialGramMD.eigs] = getTapers(K,tr,N,t);
TP.trialGramMD.t = t;
% if verbose; disp('getting taper for trial-locked analysis'); end
% %%% Defining KE cross-trial tapers as K regular taper per E
% %%% events (trials)
% K = param.tapers(2);
% tr = funTs(sInd).tr/1000;
% N = size(allWin,2);
% TP.trialGram = getTapers(K,tr,N);
% E = length(param.onsetList);
% % eval(['TP.trialGram = blkdiag(' strjoin(repmat({'TP.trialGram'},1,E),',') ');']);
% eval(['TP.trialGram = reshape(blkdiag(' strjoin(repmat({'TP.trialGram'},1,E),',') '),[N*E K E]);']);
% % TPx = nan(size(funTs(sInd).t,1),size(TP.trialGram,2));
% % TPx(allWinTrialLock(1,:),:) = TP.trialGram;
% % plot(funTs(sInd).t,TPx)
%
%
% %%% Defining missing data tapers
% % if verbose; disp('getting taper for trial-locked analysis'); end
% % K = param.tapers(2);
% % tr = funTs(sInd).tr/1000;
% % N = false(1,funTs(sInd).nframes); N(allWinTrialLock(1,:)) = true; % here N is a logical vector specifying included data, producing tapers appropriate for timeseries with missing data
% % TP.trialGram = getTapers(K,tr,N);
else
TP.trialGram.tp = [];
TP.trialGram.eigs = [];
TP.trialGram.t = [];
TP.trialGramMD.tp = [];
TP.trialGramMD.eigs = [];
TP.trialGramMD.t = [];
end
TP.full.info = 'time x taper x trial';
TP.gram.info = 'time x taper x trial';
TP.trialGram.info = 'time x taper x trial';
TP.trialGramMD.info = 'time x taper x trial';
end
%% Compute (also with taper-level permutation
skip.psd = skipPSD;
skip.svd = skipSVD;
skip.gram = skipGram;
skip.trialGram = skipTrialGram;
paramInit = param;
param.psd = rmfield(paramInit,{'onsetList' 'durList'});
param.svd = rmfield(paramInit,{'onsetList' 'durList'});
param.psdGram = rmfield(paramInit,{'onsetList' 'durList'});
param.svdGram = rmfield(paramInit,{'onsetList' 'durList'});
param.psdTrialGram = paramInit;
param.svdTrialGram = paramInit;
clear paramInit
param.psdGram.winInd = allWin;
param.svdGram.winInd = allWin;
param.psdTrialGram.winInd = allWinTrialLock;
param.svdTrialGram.winInd = allWinTrialLock;
param.perm = taperPerm;
% tic
[funPsd.psd,funPsd.psdGram,funPsd.psdTrialGram,funPsd.psdTrialGramMD,funPsd.svd,funPsd.svdGram,funPsd.svdTrialGram,funPsd.svdTrialGramMD]...
= computeAll(funTs,TP,param,windFlag,skip,verbose);
% disp('+++++')
% toc
% disp('+++++')
% %% Compute with fourrier domain phase randomization
% tic
% if phaseRand
% dbstack; error('double check that')
% % cohF = [0 0.2];
% nf = nnz(funPsd.svd.f>=cohFperm(1) & funPsd.svd.f<=cohFperm(2));
% funTsShuf = funTs;
% % sz = size(funPsd.svd.u,1:5); sz(3) = nf;
% % funPsd.svd.uPhaseRand = zeros([size(funPsd.svd.u,1:5) phaseRand],'single');
% sz = size(funPsd.svd.s,1:5); sz(3) = nf;
% funPsd.svd.sPhaseRand = zeros([sz phaseRand],'single');
% % sz = size(funPsd.svd.v,1:5); sz(3) = nf;
% % funPsd.svd.vPhaseRand = zeros([sz phaseRand],'single');
% sz = size(funPsd.svd.coh,1:5); sz(3) = nf;
% funPsd.svd.cohPhaseRand = zeros([sz phaseRand],'single');
% % sz = size(funPsd.svdGram.u,1:5); sz(3) = nf;
% % funPsd.svdGram.uPhaseRand = zeros([sz phaseRand],'single');
% % sz = size(funPsd.svdGram.s,1:5); sz(3) = nf;
% % funPsd.svdGram.sPhaseRand = zeros([sz phaseRand],'single');
% % sz = size(funPsd.svdGram.v,1:5); sz(3) = nf;
% % funPsd.svdGram.vPhaseRand = zeros([sz phaseRand],'single');
% % sz = size(funPsd.svdGram.coh,1:5); sz(3) = nf;
% % funPsd.svdGram.cohPhaseRand = zeros([sz phaseRand],'single');
% for phaseRandInd = 1:phaseRand
% disp(['phase randomization ' num2str(phaseRandInd) '/' num2str(phaseRand)])
% [X,Y] = pol2cart(rand(size(funTs(sInd).vec)).*(2*pi),abs(fft(funTs(sInd).vec,[],1)));
% funTsShuf(sInd).vec = ifft(complex(X,Y));
% paramTmp = param; paramTmp.win = [inf inf];
% [~,~,svd,svdGram] = computeAll(funTsShuf,TP,szPsd,1,skipSVD,nBloc,avTapers,paramPsd,paramSvd,0,tr,paramTmp,allWin,paramPsdGram,paramSvdGram,[],cohFperm,[]);
% % funPsdShuf = computeAll(funTsShuf,funPsd,szPsd,1,skipSVD,nBloc,avTapers,paramPsd,paramSvd,0,sInd,tr,param,allWin,paramPsdGram,paramSvdGram);
% % funPsd.svd.uPhaseRand(:,:,:,:,:,phaseRandInd) = svd.u;
% funPsd.svd.sPhaseRand(:,:,:,:,:,phaseRandInd) = svd.s;
% % funPsd.svd.vPhaseRand(:,:,:,:,:,phaseRandInd) = svd.v;
% funPsd.svd.cohPhaseRand(:,:,:,:,:,phaseRandInd) = svd.coh;
% % funPsd.svdGram.uPhaseRand(:,:,:,:,:,phaseRandInd) = svdGram.u;
% % funPsd.svdGram.sPhaseRand(:,:,:,:,:,phaseRandInd) = svdGram.s;
% % funPsd.svdGram.vPhaseRand(:,:,:,:,:,phaseRandInd) = svdGram.v;
% % funPsd.svdGram.cohPhaseRand(:,:,:,:,:,phaseRandInd) = svdGram.coh;
% end
% end
% disp('+++++')
% toc
% disp('+++++')
%% Sort outputs
funPsd(sInd).param = param;
% funPsd(sInd).psd.f = permute(funPsd(sInd).psd.f,[2 1 3 4 5 6]);
% funPsd(sInd).psd.tp = TP.full;
% funPsd(sInd).psd.vec;
% funPsd(sInd).psd.K = param.psd.tapers(2);
% [~,funPsd(sInd).psd.W,~] = K2W(funPsd(sInd).psd.T,funPsd(sInd).psd.K,0);
% funPsd(sInd).psd.param = param.psd;
% funPsd(sInd).psd.info = strjoin({'freq/time' 'tapers' 'vox' 'run' 'timeWindow' 'mode'},' x ');
% funPsd(sInd).psd.mask = funTs(sInd).vol2vec;
%
% if ~skipGram
% funPsd(sInd).psdGram.f = permute(funPsd(sInd).psdGram.f,[2 1 3 4 5 6]);
% funPsd(sInd).psdGram.tp = TP.gram;
% funPsd(sInd).psdGram.vec;
% % funPsd(sInd).psdGram.t;
% % funPsd(sInd).psdGram.tWin = mean(funPsd(sInd).psdGram.t,1);
% funPsd(sInd).psdGram.lWin = param.psdGram.win(1);
% funPsd(sInd).psdGram.K = param.psdGram.tapers(2);
% [~,funPsd(sInd).psdGram.W,~] = K2W(funPsd(sInd).psdGram.T,funPsd(sInd).psdGram.K,0);
% funPsd(sInd).psdGram.param = param.psdGram;
% funPsd(sInd).psdGram.info = strjoin({'freq/time' 'tapers' 'vox' 'run' 'timeWindow' 'mode'},' x ');
% funPsd(sInd).psdGram.mask = funTs(sInd).vol2vec;
% end
%
% if ~skipSVD
% funPsd(sInd).svd.f;
% funPsd(sInd).svd.tp = permute(TP.full,[3 4 1 5 6 2]);
% funPsd(sInd).svd.u;
% funPsd(sInd).svd.s = permute(funPsd(sInd).svd.s,[2 1 3 4 5 6]);
% funPsd(sInd).svd.v = permute(funPsd(sInd).svd.v,[6 2 3 4 5 1]);
% funPsd(sInd).svd.coh = permute(funPsd(sInd).svd.coh,[2 1 3 4 5 6]);
% funPsd(sInd).svd.K = param.svd.tapers(2);
% [~,funPsd(sInd).svd.W,~] = K2W(funPsd(sInd).svd.T,funPsd(sInd).svd.K,0);
% funPsd(sInd).svd.param = param.svd;
% funPsd(sInd).svd.info = strjoin({'vox' 'mode' 'freq/time' 'run' 'timeWindow' 'tapers'},' x ');
% funPsd(sInd).svd.mask = funTs(sInd).vol2vec;
% else
% funPsd(sInd).svd = [];
% end
%
% if ~skipGram && ~skipSVD
% funPsd(sInd).svdGram.f;
% funPsd(sInd).svdGram.tp = permute(TP.gram,[3 4 1 5 6 2]);
% funPsd(sInd).svdGram.u;
% funPsd(sInd).svdGram.s = permute(funPsd(sInd).svdGram.s,[2 1 3 4 5 6]);
% funPsd(sInd).svdGram.v = permute(funPsd(sInd).svdGram.v,[6 2 3 4 5 1]);
% funPsd(sInd).svdGram.coh = permute(funPsd(sInd).svdGram.coh,[2 1 3 4 5 6]);
% % funPsd(sInd).svdGram.t;
% % funPsd(sInd).svdGram.tWin = mean(funPsd(sInd).svdGram.t,1);
% funPsd(sInd).svdGram.lWin = param.svdGram.win(1);
% funPsd(sInd).svdGram.K = param.svdGram.tapers(2);
% [~,funPsd(sInd).svdGram.W,~] = K2W(funPsd(sInd).svdGram.T,funPsd(sInd).svdGram.K,0);
% funPsd(sInd).svdGram.param = param.svdGram;
% funPsd(sInd).svdGram.info = strjoin({'vox' 'mode' 'freq/time' 'run' 'timeWindow' 'tapers'},' x ');
% funPsd(sInd).svdGram.mask = funTs(sInd).vol2vec;
% else
% funPsd(sInd).svdGram = [];
% end
%
%
% if ~skip.trialGram
% if ~skip.psd
% funPsd(sInd).psdTrialGram.f = permute(funPsd(sInd).psdTrialGram.f,[2 1 3 4 5 6]);
% funPsd(sInd).psdTrialGram.tp = TP.trialGram;
% funPsd(sInd).psdTrialGram.vec;
% funPsd(sInd).psdTrialGram.lWin = param.psdTrialGram.win(1);
% funPsd(sInd).psdTrialGram.K = param.psdTrialGram.tapers(2);
% [~,funPsd(sInd).psdTrialGram.W,~] = K2W(funPsd(sInd).psdTrialGram.T,funPsd(sInd).psdTrialGram.K,0);
% funPsd(sInd).psdTrialGram.param = param.psdTrialGram;
% funPsd(sInd).psdTrialGram.info = strjoin({'freq/time' 'tapers' 'vox' 'run' 'timeWindow' 'mode'},' x ');
% funPsd(sInd).psdTrialGram.mask = funTs(sInd).vol2vec;
% else
% funPsd(sInd).psdTrialGram = [];
% end
% if ~skip.svd
% funPsd(sInd).svdTrialGram.f;
% funPsd(sInd).svdTrialGram.tp = permute(TP.trialGram,[3 4 1 5 6 2]);
% % funPsd(sInd).svdTrialGram.u;
% % funPsd(sInd).svdTrialGram.s = permute(funPsd(sInd).svdTrialGram.s,[2 1 3 4 5 6]);
% % funPsd(sInd).svdTrialGram.v = permute(funPsd(sInd).svdTrialGram.v,[6 2 3 4 5 1]);
% funPsd(sInd).svdTrialGram.coh = permute(funPsd(sInd).svdTrialGram.coh,[2 1 3 4 5 6]);
% funPsd(sInd).svdTrialGram.lWin = param.svdTrialGram.win(1);
% funPsd(sInd).svdTrialGram.K = param.svdTrialGram.tapers(2);
% [~,funPsd(sInd).svdTrialGram.W,~] = K2W(funPsd(sInd).svdTrialGram.T,funPsd(sInd).svdTrialGram.K,0);
% funPsd(sInd).svdTrialGram.param = param.svdTrialGram;
% funPsd(sInd).svdTrialGram.info = strjoin({'vox' 'mode' 'freq/time' 'run' 'timeWindow' 'tapers'},' x ');
% funPsd(sInd).svdTrialGram.mask = funTs(sInd).vol2vec;
% else
% funPsd(sInd).svdTrialGram = [];
% end
% end
%
% %% For backward compatibility
% funPsd(sInd).vec = permute(funPsd(sInd).psd.vec,[1 3 2 4]); funPsd(sInd).psd.vec = [];
% funPsd(sInd).f = permute(funPsd(sInd).psd.f,[2 3 4 5 6 1]);
% funPsd(sInd).K = funPsd(sInd).psd.K;
% funPsd(sInd).T = funPsd(sInd).psd.T;
% funPsd(sInd).W = funPsd(sInd).psd.W;
% funPsd(sInd).tr = mode(diff(funPsd(sInd).f))*1000;
% funPsd(sInd).nfreq = size(funPsd(sInd).vec,1);
% funPsd(sInd).nframes=size(funTs(sInd).vec,1);
% funPsd(sInd).nruns=size(funPsd(sInd).vec,4);
% % if funPsd(sInd).nframes~=size(funTs(sInd).vec,1); dbstack; warning('something wrong with nframes'); end
% % if funPsd(sInd).nruns~=size(funPsd(sInd).vec,4); dbstack; warning('something wrong with nframes'); end
% funPsd(sInd).ntapers = size(funPsd(sInd).vec,3);
end
function [psd,psdGram,psdTrialGram,psdTrialGramMD,svd,svdGram,svdTrialGram,svdTrialGramMD] = computeAll(funTs,TP,param,windFlag,skip,verbose)
if ~exist('nShuf','var'); nShuf = []; end
if ~exist('nRun','var'); nRun = []; end
if ~exist('cohFrange','var'); cohFrange = []; end
if isempty(nShuf); nShuf = 0; end
if isempty(nRun); nRun = 1; end
if isempty(cohFrange); cohFrange = [0 inf]; end
orderWin = 0;
% skip.gram = any(ismember(param.win(2),[0 inf nan]));
for runInd = 1:nRun
if verbose && nRun>1; disp(['---Run ' num2str(runInd) '/' num2str(nRun) '---']); end
Fs = param.Fs;
%% %%%%%%%%%%%%%%%%%%%%%
% Over full timeseires %
%%%%%%%%%%%%%%%%%%%%% %%
%[time x trial x run x taper x freq x vox x window x mode]
tic
disp('full timeseries analysis')
%%% tapers
tp = permute(TP.full.tp,[1 3 4 2 5 6 7 8]); % tapers[time x trial x run x taper x freq x vox x window x mode]
[Nk,Ek,Rk,Kk,Fk,Vk,Wk,M] = size(tp);
K = Kk;
N = Nk;
%%% windows and trials
W = 1;
E = 1;
%%% time
if isfield(funTs,'t') && ~isempty(funTs.t)
t = funTs.t; % tapers[time x trial x run x taper x freq x vox x window x mode]
else
dbstack; error('X');
t = permute(linspace(0,(N-1)/Fs,N),[2 1 3 4 5 6 7 8]); % tapers[time x trial x run x taper x freq x vox x window x mode]
end
[Nt,Et,Rt,Kt,Ft,Vt,Wt,Mt] = size(t);
%%% freq
pad = 0;
NFFT=max(2^(nextpow2(N)+pad),N);
[f,fInd]=getfgrid(Fs,NFFT,[0 Fs/2]);
f = permute(f,[1 3 4 5 2 6 7 8]); % frequencies[time x trial x run x taper x freq x vox x window x mode]
[Nf,Ef,Rf,Kf,Ff,Vf,Wf] = size(f);
F = Ff;
%%% channels and runs
[~,V,~,R] = size(funTs.vec);
%%% modes
M = min([V K]);
%%% allocate
res.full.PSD = zeros(1,E,1,1,F,V,W,1 ); % psd [time x trial x run x taper x freq x vox x window x mode] at each trial
res.full.COH = zeros(1,E,1,1,F,1,W,M ); % coherence [time x trial x run x taper x freq x vox x window x mode] at each trial
%%% Compute J
d = funTs.vec; % [time x vox x taper x run ]
d = permute(d,[3 2 4 1]); % [taper x vox x run x time*trial ]
d = reshape(d,[1 V R N E]); % [taper x vox x run x time x trial]
d = permute(d,[4 5 3 1 6 2 7 8]);
tp = tp;
t = t;
J = getJ2(d,tp,t,f)/Fs; % [time x trial x run x taper x freq x vox x window]
% [N E R K F V W]
%%% Compute psd
res.full.PSD = mean( conj(J).*J ,4);
%%% Compute coherence
if K > 1
dim = {'N' 'E' 'R' 'K' 'F' 'V' 'W' 'Mk'};
prm = [ 6 4 2 1 3 5 7 8 ];
dim = strjoin(dim(prm),' ');
% j = permute(J,prm); %[V K E N R F W Mk]
% j = reshape(j,[V K E*1*R*F*1*1]); %[V K E*N*R*F*W*Mk]
% [u,s,v] = pagesvd(j,'econ','vector'); % s[Mk V E*N*R*F*W]
[u,s,~] = pagesvd(reshape(permute(J,prm),[V K E*1*R*F*1*1]),'econ','vector'); % s[Mk V E*N*R*F*W]
coh = s.^2./sum(s.^2,1); % coherence[Mk V E*N*R*F*W]
spSVmag = abs(u);
% coh = reshape(coh,[M 1 E 1 R F 1]); % [Mk V E N R F W]
% 1 2 3 4 5 6 7 8
dim = {'Mk' 'V' 'E' 'N' 'R' 'F' 'W' 'K'};
prm = [4 3 5 8 6 2 7 1];
% prm = [ 2 4 3 6 5 7 8 1 ];
dim = strjoin(dim(prm),' ');
res.full.COH = permute(reshape(coh,[M 1 E 1 R F 1]),prm); %[time x trial x run x taper x freq x vox x window x mode]
res.full.spSV = permute(u,[4 5 6 7 3 1 8 2]); %[time x trial x run x taper x freq x vox x window x mode]
% %%% Estimate null distribution2
% if param.perm
% allPerm = perms(1:K); allPermN = size(allPerm,1);
% j2 = reshape(permute(j,[3 2 1]),[F K*V]);
%
% coh_pVal = zeros(M,1,F);
% spSVmag_pVal = zeros(V,M,F);
% parfor perm = 1:param.perm
%
% [u,s,vPerm] = pagesvd(...
% permute( reshape( j2(:,curPerm) ,[F K V]) ,[3 2 1])...
% ,'econ','vector'); % s[M V E*N*R*F*W]
% cohPerm = s.^2./sum(s.^2,1); % coherence[M V E*N*R*F*W]
% spSVmagPerm = abs(u);
% coh_pVal = coh_pVal + cohPerm>coh;
% spSVmag_pVal = spSVmag_pVal + spSVmagPerm>spSVmag;
% end
%
%
% end
%%% Estimate null distribution
if param.perm
disp(['doing ' num2str(param.perm) ' random taper permutations'])
% V = 4; F = 3; K = 10;
% j = repmat(1:K,[V 1 F]);
%%%get all possible permutions beforehand for speed
allPerm = perms(1:K); allPermN = size(allPerm,1);
%%%initialize inputs and outputs
j2 = reshape(permute(j,[3 2 1]),[F K*V]); % F K*V
COH = permute(res.full.COH,[8 6 5 1 2 3 4 7]); % M V F
spSVmag = permute(abs(res.full.spSV),[6 8 5 1 2 3 4 7]); % V M F
COH_permMean = zeros(size(COH));
spSVmag_permMean = zeros(size(spSVmag));
COH_pVal = zeros(size(COH));
spSVmag_pVal = zeros(size(spSVmag));
%%%loop over permutations
parfor perm = 1:param.perm
% for each voxel, randomly permute tapers, using the same
% permutation across frequencies
[COH_pVal_cur,spSVmag_pVal_cur,COH_permMean_cur,spSVmag_permMean_cur] = tmpSvd(j2,allPerm,allPermN,V,K,F,COH,spSVmag);
COH_pVal = COH_pVal + COH_pVal_cur;
spSVmag_pVal = spSVmag_pVal + spSVmag_pVal_cur;
COH_permMean = COH_permMean + COH_permMean_cur;
spSVmag_permMean = spSVmag_permMean + spSVmag_permMean_cur;
end
%%%summarize across permutations
COH_permMean = COH_permMean ./ param.perm; % M V F
spSVmag_permMean = spSVmag_permMean ./ param.perm; % V M F
COH_pVal = COH_pVal ./ param.perm; % M V F
spSVmag_pVal = spSVmag_pVal ./ param.perm; % V M F
%%%FDR
COH_pVal = permute(COH_pVal,[3 2 1]); % F V M
COH_fdr = zeros(size(COH_pVal));
for i = 1:length(COH_pVal(1,:))
COH_fdr(:,i) = mafdr(COH_pVal(:,i),'BHFDR',true);
end
COH_pVal = permute(COH_pVal,[3 2 1]); % M V F
COH_fdr = permute(COH_fdr ,[3 2 1]); % M V F
spSVmag_fdr = zeros(size(spSVmag_pVal)); % V M F
for i = 1:length(spSVmag_pVal(1,:))
spSVmag_fdr(:,i) = mafdr(spSVmag_pVal(:,i),'BHFDR',true);
end
%%%compile results
res.full.COH_permMean = permute(COH_permMean,[4 5 6 7 3 2 8 1]); %[time x trial x run x taper x freq x vox x window x mode]
res.full.COH_pVal = permute(COH_pVal ,[4 5 6 7 3 2 8 1]); %[time x trial x run x taper x freq x vox x window x mode]
res.full.COH_fdr = permute(COH_fdr ,[4 5 6 7 3 2 8 1]); %[time x trial x run x taper x freq x vox x window x mode]
res.full.spSV_permMean = permute(spSVmag_permMean,[4 5 6 7 3 1 8 2]); %[time x trial x run x taper x freq x vox x window x mode]
res.full.spSV_pVal = permute(spSVmag_pVal ,[4 5 6 7 3 1 8 2]); %[time x trial x run x taper x freq x vox x window x mode]
res.full.spSV_fdr = permute(spSVmag_fdr ,[4 5 6 7 3 1 8 2]); %[time x trial x run x taper x freq x vox x window x mode]
end
else
res.full.COH = [];
res.full.spSV = [];
end
%%%
res.full.f = f;
res.full.K = K;
res.full.T = N/Fs;
res.full.E = [];
res.full.win = [];
res.full.param = rmfield(param,{'complex' 'psd' 'svd' 'psdGram' 'svdGram' 'psdTrialGram' 'svdTrialGram'});
res.full.dim = [N E R K F V W M];
res.full.dimInfo = '[N E R K F V W M]';
toc
%% %%%%%%%%%%%%%%%%%%%%%
% Over each timewindow %
%%%%%%%%%%%%%%%%%%%%% %%
tic
disp('time-resolved analysis')
if ~skip.gram
%[time x trial x run x taper x freq x vox x window x mode]
%[ 7 2 1 3 5 8 20]
tp = permute(TP.gram.tp,[1 3 4 2 5 6 7 8]); % tapers[time x trial x run x taper x freq x vox x window x mode]
[Nk,Ek,Rk,Kk,Fk,Vk,Wk,M] = size(tp);
K = Kk;
%%% windows
w = permute(param.psdGram.winInd,[2 3 4 5 6 7 1 8]); % windows[time x trial x run x taper x freq x vox x window x mode]
[Nw,Ew,Rw,Kw,Fw,Vw,Ww,Mw] = size(w);
W = Ww;
E = Ew;
if Nw~=Nk; dbstack; error('X'); end
N = Nk;
%%% time
t = permute(linspace(0,(N-1)/Fs,N),[2 1 3 4 5 6 7 8]); % tapers[time x trial x run x taper x freq x vox x window x mode]
[Nt,Et,Rt,Kt,Ft,Vt,Wt,Mt] = size(t);
%%% freq
pad = 0;
NFFT=max(2^(nextpow2(N)+pad),N);
[f,fInd]=getfgrid(Fs,NFFT,[0 Fs/2]);
f = permute(f,[1 3 4 5 2 6 7 8]); % frequencies[time x trial x run x taper x freq x vox x window x mode]
[Nf,Ef,Rf,Kf,Ff,Vf,Wf] = size(f);
F = Ff;
%%% channels and runs
[~,V,~,R] = size(funTs.vec);
%%% modes
M = min([V K]);
%%% allocate
res.gram.PSD = zeros(1,E,1,1,F,V,W,1 ); % psd [time x trial x run x taper x freq x vox x window x mode] at each trial
res.gram.COH = zeros(1,E,1,1,F,1,W,M ); % coherence [time x trial x run x taper x freq x vox x window x mode] at each trial
%%% loop over windows
fprintf([repmat('|',1,W) '\n\n']);
for wInd = 1:W
%%% Compute J
ind = w(:,:,:,:,:,:,wInd);
tWin = reshape(funTs.t(ind,:,:,:),size(ind)); tWin = tWin([1 end],:);
d = funTs.vec(ind,:,:,:); % [time x vox x taper x run ]
d = permute(d,[3 2 4 1]); % [taper x vox x run x time*trial ]
d = reshape(d,[1 V R N E]); % [taper x vox x run x time x trial]
d = permute(d,[4 5 3 1 6 2 7 8]); %[time x trial x run x taper x freq x vox x window x mode]
if orderWin == 0
d = d - mean(d,1);
end
tp = tp;
t = t; % adjust t here for sub-tr stimulus onsets
J = getJ2(d,tp,t,f)/Fs; % [time x trial x run x taper x freq x vox x window]
% [N E R K F V W]
%%% Compute psd at each trial
res.gram.PSD(:,:,:,:,:,:,wInd) = mean( conj(J).*J ,4);
%%% Compute coherence at each trial
if K > 1
dim = {'N' 'E' 'R' 'K' 'F' 'V' 'W' 'Mk'};
prm = [ 6 4 2 1 3 5 7 8 ];
dim = strjoin(dim(prm),' ');
% j = permute(J,prm); %[V K E N R F W Mk]
% j = reshape(j,[V K E*1*R*F*1*1]); %[V K E*N*R*F*W*Mk]
% [u,s,v] = pagesvd(j,'econ','vector'); % s[Mk V E*N*R*F*W]
[~,s,~] = pagesvd(reshape(permute(J,prm),[V K E*1*R*F*1*1]),'econ','vector'); % s[Mk V E*N*R*F*W]
coh = s.^2./sum(s.^2,1); % coherence[Mk V E*N*R*F*W]
coh = reshape(coh,[M 1 E 1 R F 1]); % [Mk V E N R F W]
dim = {'Mk' 'V' 'E' 'N' 'R' 'F' 'W' 'K'};
prm = [ 2 4 3 6 5 7 8 1 ];
dim = strjoin(dim(prm),' ');
coh = permute(coh,prm); %[V N E F R W K Mk]
res.gram.COH(:,:,:,:,:,:,wInd,:) = coh;
end
%%% Output window time
res.gram.t(:,:,:,:,:,:,wInd,:) = tWin;
fprintf('\b''\n');
end
if K == 1
res.gram.COH = [];
end
res.gram.f = f;
res.gram.K = K;
res.gram.T = N/Fs;
res.gram.E = [];
res.gram.win = [mean(reshape(diff(res.gram.t,[],1),[],1)) mean(reshape(diff(res.gram.t,[],7),[],1))];
res.gram.param = rmfield(param,{'complex' 'psd' 'svd' 'psdGram' 'svdGram' 'psdTrialGram' 'svdTrialGram'});
else
res.gram = [];
end
toc
%% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Over each event-related timewindow %
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% %%
% Much time could be saved by reusing the output of the time-resolved
% analysis, as long as the step sizes allow to construct every
% event-related window from available time-resolved windows. However,
% the missing-data event-related analysis seems superior to the
% event-related analysis so the latter might not be needed.
tic
disp('trial-locked time-resolved analysis')
%[time x trial x run x taper x freq x vox x window x mode]
%[ 7 2 1 3 5 8 20]
if ~skip.trialGram
%%% tapers
tp = permute(TP.gram.tp,[1 3 4 2 5 6 7 8]); % tapers[time x trial x run x taper x freq x vox x window x mode]
[Nk,Ek,Rk,Kk,Fk,Vk,Wk,M] = size(tp);
K = Kk;
%%% windows and trials
w = permute(param.psdTrialGram.winInd,[2 3 4 5 6 7 1 8]); % windows[time x trial x run x taper x freq x vox x window x mode]
[Nw,Ew,Rw,Kw,Fw,Vw,Ww,Mw] = size(w);
W = Ww;
E = Ew;
if Nw~=Nk; dbstack; error('X'); end
N = Nk;
%%% time
t = permute(linspace(0,(N-1)/Fs,N),[2 1 3 4 5 6 7 8]); % tapers[time x trial x run x taper x freq x vox x window x mode]
[Nt,Et,Rt,Kt,Ft,Vt,Wt,Mt] = size(t);
%%% freq
pad = 0;
% pad = E-1;
NFFT=max(2^(nextpow2(N*E)+pad),N*E);
[f,fInd]=getfgrid(Fs,NFFT,[0 Fs/2]);
f = permute(f,[1 3 4 5 2 6 7 8]); % frequencies[time x trial x run x taper x freq x vox x window x mode]
[Nf,Ef,Rf,Kf,Ff,Vf,Wf,Mf] = size(f);
F = Ff;
%%% channels and runs
[~,V,~,R] = size(funTs.vec);
%%% modes
M = min([V K]);
Mek = min([V K*E]);
%%% allocate
res.trialGram.PSD = zeros(1,E,1,1,F,V,W,1 ); % psd [time x trial x run x taper x freq x vox x window x mode] at each trial
res.trialGram.PSDeav = zeros(1,1,1,1,F,V,W,1 ); % psd [time x trial x run x taper x freq x vox x window x mode] averaged across trials (eVENT avRAGED )
res.trialGram.PSDepc = zeros(1,1,1,1,F,V,W,1 ); % psd [time x trial x run x taper x freq x vox x window x mode] phase-coherently averaged across trials (eVENT pHASE cOHERENT)
res.trialGram.COH = zeros(1,E,1,1,F,1,W,M ); % coherence [time x trial x run x taper x freq x vox x window x mode] at each trial
res.trialGram.COHeav = zeros(1,1,1,1,F,1,W,M ); % coherence [time x trial x run x taper x freq x vox x window x mode] averaged across trials (eVENT avRAGED )
res.trialGram.COHepc = zeros(1,1,1,1,F,1,W,M ); % coherence [time x trial x run x taper x freq x vox x window x mode] phase-coherently averaged across trials (eVENT pHASE cOHERENT)
res.trialGram.COHek = zeros(1,1,1,1,F,1,W,Mek); % coherence [time x trial x run x taper x freq x vox x window x mode] trials concatenated as extra sets of tapers (eVENT AS TAPERS k )
%%% loop over windows
fprintf([repmat('|',1,W) '\n\n']);
for wInd = 1:W
%%% Compute J
ind = w(:,:,:,:,:,:,wInd,:);
tWin = reshape(funTs.t(ind,:,:,:),size(ind)); tWin = tWin([1 end],:);
d = funTs.vec(ind,:,:,:); % [time x vox x taper x run ]
d = permute(d,[3 2 4 1]); % [taper x vox x run x time*trial ]
d = reshape(d,[1 V R N E]); % [taper x vox x run x time x trial]
d = permute(d,[4 5 3 1 6 2 7 8]); %[time x trial x run x taper x freq x vox x window x mode]
if orderWin == 0
d = d - mean(d,1);
end
tp = tp;
t = t; %!!!!!!!!!!!!!!! adjust t here for sub-tr stimulus onsets
% dbstack; error('the error is somewhere here: in tp,t or f, or within getJ2')
J = getJ2(d,tp,t,f)/Fs; % [time x trial x run x taper x freq x vox x window]
% [N E R K F V W]
%%% Compute psd at each trial
res.trialGram.PSD(:,:,:,:,:,:,wInd,:) = mean( conj(J).*J ,4); % Compute power then average across tapers...
%%% Compute psd averaged across trials
res.trialGram.PSDeav(:,:,:,:,:,:,wInd,:) = mean( res.trialGram.PSD(:,:,:,:,:,:,wInd,:) ,2); % then average across trials.
%%% Compute psd phase-coherently averaged across trials
res.trialGram.PSDepc(:,:,:,:,:,:,wInd,:) = mean( conj(mean(J,2)).*mean(J,2) ,4); % Average across trials then compute power then average across tapers.
if K > 1
%%% Compute coherence at each trial
dim = {'N' 'E' 'R' 'K' 'F' 'V' 'W' 'M'};
prm = [ 6 4 2 1 3 5 7 8 ];
dim = strjoin(dim(prm),' ');
% j = permute(J,prm); %[V K E N R F W M]
% j = reshape(j,[V K E*1*R*F*1*1]); %[V K E*N*R*F*W*M]
% [u,s,v] = pagesvd(j,'econ','vector'); % s[M V E*N*R*F*W]
[~,s,~] = pagesvd(reshape(permute(J,prm),[V K E*1*R*F*1*1]),'econ','vector'); % s[M V E*N*R*F*W]
coh = s.^2./sum(s.^2,1); % coherence[M V E*N*R*F*W]
coh = reshape(coh,[M 1 E 1 R F 1]); % [M V E N R F W]
% 1 2 3 4 5 6 7 8
dim = {'M' 'V' 'E' 'N' 'R' 'F' 'W' 'K'};
prm = [4 3 5 8 6 2 7 1];
%[N E R K F V W M]
dim = strjoin(dim(prm),' ');
coh = permute(coh,prm); %[N E R K F V W M]
res.trialGram.COH(:,:,:,:,:,:,wInd,:) = coh;
%%% Average coherence across trials
res.trialGram.COHeav(:,:,:,:,:,:,wInd,:) = mean(res.trialGram.COH(:,:,:,:,:,:,wInd,:),2);
%%% Compute coherence after phase-coherently averaging across trials
dim = {'N' 'E' 'R' 'K' 'F' 'V' 'W' 'Mk'};
prm = [ 6 4 2 1 3 5 7 8 ];
dim = strjoin(dim(prm),' ');
% j = permute(mean(J,2),prm); %[V K E N R F W Mk]
% j = reshape(j,[V K 1*1*R*F*1*1]); %[V K E*N*R*F*W*M]
% [u,s,v] = pagesvd(j,'econ','vector'); % s[M V E*N*R*F*W]
[~,s,~] = pagesvd(reshape(permute(mean(J,2),prm),[V K 1*1*R*F*1*1]),'econ','vector'); % s[M V E*N*R*F*W]
coh = s.^2./sum(s.^2,1); % coherence[M V E*N*R*F*W]
coh = reshape(coh,[M 1 1 1 R F 1]); % [M V E N R F W]
dim = {'Mk' 'V' 'E' 'N' 'R' 'F' 'W' 'K'};
prm = [ 2 4 3 6 5 7 8 1 ];
dim = strjoin(dim(prm),' ');
coh = permute(coh,prm); %[V N E F R W K M]
res.trialGram.COHepc(:,:,:,:,:,:,wInd,:) = coh;
end
%%% Compute coherence with trials concatenated as extra sets of tapers (equivalent to averaging across trials)
% dim1 = {'N' 'E' 'R' 'K' 'F' 'V' 'W' 'Mk'};
% prm1 = [ 6 4 2 1 3 5 7 8 ];
% dim1 = strjoin(dim1(prm1),' ');
% j = permute(J,prm1); %[V K E N R F W Mk]
% j = reshape(j,[V K E 1*R*F*1*1]); %[V K E N*R*F*W*Mk]
% dim2 = {'V' 'K' 'E' 'N*R*F*W*Mk'};
% prm2 = [ 1 4 2 3 ];
% dim2 = strjoin(dim2(prm2),' ');
% j = permute(j,prm2); %[V N*R*F*W*Mk K E]
% j = reshape(j,[V 1*R*F*1*1 K*E]); %[V N*R*F*W*Mk K*E]
% j = permute(j,[1 3 2]); %[V K*E N*R*F*W*Mk];
dim1 = {'N' 'E' 'R' 'K' 'F' 'V' 'W' 'Mk'};
prm1 = [ 6 4 2 1 3 5 7 8 ];
dim1 = strjoin(dim1(prm1),' ');
dim2 = {'V' 'K' 'E' 'N*R*F*W*Mk'};
prm2 = [ 1 4 2 3 ];