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Fusion_MultiSourceHRFEEG_TICA_FMRI.m
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%% Adding the correct toolboxes
% pharm_path(0)
% CustomPath_SourceReconst()
%% EEG EEG EEG EEG EEG EEG EEG EEG EEG EEG EEG EEG EEG EEG EEG EEG EEG EEG EEG EEG EEG EEG EEG EEG EEG EEG EEG EEG EEG EEG EEG EEG EEG EEG
%% Loading the Source space and sensor space data
[FileName,PathName] = uigetfile('*.mat', 'Select the source space EEG data (e.g., KET1)');
load(strcat([PathName FileName]))
[FileName,PathName] = uigetfile('*.mat', 'Select the source space EEG data (e.g., KET1)');
load(strcat([PathName FileName]))
%% Resample the previously cleaned EEG
Data = data_reconst ;
cfg2 = [] ;
cfg2.lpfilter = 'yes' ;
cfg2.lpfreq = [74] ;
[Data] = ft_preprocessing(cfg2, Data) ;
cfg2 = [];
cfg2.resamplefs = 150;
data_short_resamp = ft_resampledata(cfg2, Data)
if isfield(Data, 'sampleinfo')
sampleinfo = ceil(Data.sampleinfo/(Data.fsample/cfg2.resamplefs));
data_short_resamp.sampleinfo = sampleinfo;
end
%% Rereferencing
ft_progress('init', 'text', 'Rereferencing the EEG data ...');
cfg2 = [];
cfg2.implicitref = 'FCz' ;
cfg2.reref = 'yes';
cfg2.refchannel = 'all';
[data_short_resamp] = ft_preprocessing(cfg2, data_short_resamp);
%% Loading the forward model
[FileName,PathName] = uigetfile('*.mat', 'Select a forward model >><< Or press cancel to use the BEM template model');
if FileName == 0
cfg = [] ;
%ForwardModel = CreatT1BEM(SensorData);
load BrainCap1010
cfg.elec = elec;
ForwardModel = Create_TemplateBEM(cfg, Data);
ForwardModel_Orig = ForwardModel;
else
load(strcat([PathName FileName]))
ForwardModel_Orig = ForwardModel ;
end
%% Creating the source space data
Beamed = data_short_resamp ;
Beamed.trial{1} = SourceSpaceStuff.filters*data_short_resamp.trial{1} ;
for II = 1:size(Beamed.trial{1},1)
Beamed.label{II} = strcat('Sig',num2str(II));
end
%% Band passs the Source space data
cfg = [] ;
cfg.bpfilter = 'yes'
EEGBandPass = [ 3.5 5; 5.1 8; 8 13 ]
for Band_Index = 1:size(EEGBandPass,1)
cfg.bpfreq = EEGBandPass(Band_Index,:)
BeamedBPed{Band_Index}= ft_preprocessing(cfg, Beamed) ;
for I = 3:3:size(BeamedBPed{Band_Index}.trial{1},1)
BeamedBPed{Band_Index}.trial2{1}(I/3,:) = abs(BeamedBPed{Band_Index}.trial{1}(I-2,:))+abs(BeamedBPed{Band_Index}.trial{1}(I-1,:))+abs(BeamedBPed{Band_Index}.trial{1}(I,:)) ;
end
BeamedBPed{Band_Index}.trial{1} = BeamedBPed{Band_Index}.trial2{1} ;
BeamedBPed{Band_Index} = rmfield(BeamedBPed{Band_Index}, 'label') ;
for II = 1:size(BeamedBPed{Band_Index}.trial{1},1)
BeamedBPed{Band_Index}.label{II} = strcat('Sig',num2str(II));
end
end
%%
for Band_Index = 1:size(EEGBandPass,1)
%% Calculate the power envelope
filtabs = BeamedBPed{Band_Index};
for i = 1 : length(filtabs.trial)
filtabs.trial{i} = abs(filtabs.trial{i}); %Envelope
end
%% Generation of standard HRF/convolution (run in FSL)
%Create hrf at the correct sampling interval - 0 input effectively removes the second gamma function from this
[hrf] = doubleGammaHrf(1/filtabs.fsample,[6 16],[1 1],0,20);%,tp,beta,rt,len)
hrf_time =( 0 :1/filtabs.fsample:20)';
hrf_time(end) = [];
figure;
plot(hrf_time, hrf);
title('Standard HRF');
%%
% %Now convolve power envelope with the hrf
% filthrf = filtabs;
% filthrf = rmfield(filthrf, 'trial') ;
% i = 1 ;
% for i = 1 : length(filtabs.trial)
% filthrf.trial{i} = zeros(size(filtabs.trial{i},1), size(conv(filtabs.trial{i}(1,:),hrf),2)) ;
% for Comp_Index = 1:size(filtabs.trial{1},1)
% filthrf.trial{i}(Comp_Index,:) = conv(filtabs.trial{i}(Comp_Index,:),hrf) *1; %Envelope
% end
% end
% %plot power envelop plus convolved version
% figure
% hold on
% plot(real(filtabs.trial{1}(1,:)), 'g')
% plot(real(filthrf.trial{1}(1,:)), 'r')
%%
%NTRs = 437;
NTRs = 243;
%This demonstrates warm up effects in the convolution and suggests that restarting the convolution for every bad trial means losing a lot of data to warm-up effects.
%function "nanconv" on MATLAB website might be an option on the entire time series. Here I have just used linear interpolation to
%glue trials together ..... looks ugly but things will get downsampled and convolved later......
%Approach - Glue all the sections together, interpolate (bad trials)
MaxSampleIndex = filtabs.sampleinfo(end,2);
Tau = filtabs.time{1}(1,2) - filtabs.time{1}(1,1 )
timee = [1:MaxSampleIndex ] ;
timee = timee*Tau ;
SamplesPerTR = MaxSampleIndex./ NTRs;
rawvec = NaN([1 MaxSampleIndex]);
%%
filthrf2 = filtabs;
filthrf2 = rmfield(filthrf2, 'trial') ;
filthrf2 = rmfield(filthrf2, 'time') ;
filthrf2 = rmfield(filthrf2, 'sampleinfo') ;
i = 1 ;
%%
filthrf2.trial{1} = zeros(size(filtabs.trial{1},1), MaxSampleIndex) ;
for Comp_Index = 1:size(filtabs.trial{1},1)
if length(filtabs.trial) > 1
for i = 1: length(filtabs.trial) - 1
rawvec(filtabs.sampleinfo(i,1):filtabs.sampleinfo(i,2)) = filtabs.trial{i}(Comp_Index,:);
Means{i}(1,2) = mean(rawvec(filtabs.sampleinfo(i,2) - 2.0*filtabs.fsample : filtabs.sampleinfo(i,2))) ;
Means{i}(1,1) = mean(rawvec(filtabs.sampleinfo(i,1): filtabs.sampleinfo(i,1) + 2.0*filtabs.fsample)) ;
Vars{i}(1,2) = mean(rawvec(filtabs.sampleinfo(i,2) - 2.0*filtabs.fsample : filtabs.sampleinfo(i,2))) ;
Vars{i}(1,1) = mean(rawvec(filtabs.sampleinfo(i,1): filtabs.sampleinfo(i,1) + 2.0*filtabs.fsample)) ;
Size_NAN = size(filtabs.sampleinfo(i,2)+1:filtabs.sampleinfo(i+1,1),2) ;
rawvec(filtabs.sampleinfo(i,2)+1:filtabs.sampleinfo(i+1,1)) = abs(randn(1,Size_NAN)*mean(Means{i})) ;%+ linspace(Means{i}(1,1), Means{i}(1,2), Size_NAN) ;
end
i = i + 1
rawvec(filtabs.sampleinfo(i,1):filtabs.sampleinfo(i,2)) = filtabs.trial{i}(Comp_Index,:);
Means{i}(1,2) = mean(rawvec(filtabs.sampleinfo(i,2) - 2.0*filtabs.fsample : filtabs.sampleinfo(i,2))) ;
Means{i}(1,1) = mean(rawvec(filtabs.sampleinfo(i,1): filtabs.sampleinfo(i,1) + 2.0*filtabs.fsample)) ;
Vars{i}(1,2) = mean(rawvec(filtabs.sampleinfo(i,2) - 2.0*filtabs.fsample : filtabs.sampleinfo(i,2))) ;
Vars{i}(1,1) = mean(rawvec(filtabs.sampleinfo(i,1): filtabs.sampleinfo(i,1) + 2.0*filtabs.fsample)) ;
Size_NAN = size(filtabs.sampleinfo(i,2)+1:length(rawvec),2) ;
rawvec(filtabs.sampleinfo(i,2)+1:end) = abs(randn(1,Size_NAN)*mean(Means{i})) ;
else
i = 1
rawvec(filtabs.sampleinfo(i,1):filtabs.sampleinfo(i,2)) = filtabs.trial{i}(Comp_Index,:);
Means{i}(1,2) = mean(rawvec(filtabs.sampleinfo(i,2) - 2.0*filtabs.fsample : filtabs.sampleinfo(i,2))) ;
Means{i}(1,1) = mean(rawvec(filtabs.sampleinfo(i,1): filtabs.sampleinfo(i,1) + 2.0*filtabs.fsample)) ;
Vars{i}(1,2) = mean(rawvec(filtabs.sampleinfo(i,2) - 2.0*filtabs.fsample : filtabs.sampleinfo(i,2))) ;
Vars{i}(1,1) = mean(rawvec(filtabs.sampleinfo(i,1): filtabs.sampleinfo(i,1) + 2.0*filtabs.fsample)) ;
Size_NAN = size(filtabs.sampleinfo(i,2)+1:length(rawvec),2) ;
rawvec(filtabs.sampleinfo(i,2)+1:end) = abs(randn(1,Size_NAN)*mean(Means{i})) ;
end
X = find(~isnan(rawvec));
Xq = find(isnan(rawvec));
V = rawvec(X);
Vq = interp1(X,V,Xq, 'linear'); %rough but ok since will downsample
interpvec = rawvec;
interpvec(Xq) = Vq;
filthrf2.trial{1}(Comp_Index,:) = interpvec ;
end
%%
filthrf2.time{1} = timee ;
filthrf2.sampleinfo(1) = 1 ;
filthrf2.sampleinfo(2) = filtabs.sampleinfo(end,2) ;
filthrf3 = trial2continuous(filthrf2) ;
% X = find(~isnan(rawvec));
% Xq = find(isnan(rawvec));
% V = rawvec(X);
% Vq = interp1(X,V,Xq, 'linear'); %rough but ok since will downsample
%
% interpvec = rawvec;
% interpvec(Xq) = Vq;
%
%
% %Plot the interpolation result
% figure
% %inds = [Xq(1) Xq(1)+ 10000]; %Plot a section of data around a missing trial
% %plot(timee(inds(1):inds(2)),interpvec(inds(1):inds(2)), 'r')
% plot(timee,interpvec,'g')
% hold on
% %plot(rawvec(inds(1):inds(2)))
% plot(timee, rawvec)
%
% title('Quick linear interpolation of missing trial');
%% Now convolve with the hrf
postconv = conv(interpvec, hrf);
figure
hold on
plot(timee, interpvec, 'b')
%plot(timee, postconv(125*10:end-125*10),'r')
timee2 = [Tau:Tau:10 timee+10 Tau+10+timee(end):Tau:20+timee(end) - Tau] ;
plot(timee2, postconv,'r')
i = 1 ;
filtconv.trial{1} = zeros(size(filtabs.trial{i},1), length(postconv)) ;
for Comp_Index = 1:size(filtabs.trial{1},1)
filtconv.trial{1}(Comp_Index,:) = conv(filthrf3.trial{1}(Comp_Index,:) , hrf);
end
filtconv.time{1} = timee2 ;
filtconv.label = filthrf3.label ;
filtconv.fsample = filthrf3.fsample ;
%% Reject the first 11 s and the last 22 s of the EEG
filtconv2 = filtconv ;
filtconv2.trial{1} = filtconv.trial{1}(:,filtconv.fsample*11 : end-filtconv.fsample*22) ;
filtconv2.time{1} = filtconv.time{1}(1,1:size(filtconv2.trial{1},2)) ;
%% Down sample the data
% postconv2 = postconv;
cfg = [];
cfg.resamplefs = 0.454545454545455
%sampleinfo = ceil(Data.sampleinfo/(Data.fsample/cfg.resamplefs));
%trialinfo = Data.trialinfo;
SourceSpaceERF{Band_Index} = ft_resampledata(cfg, filtconv2)
%% Band pass the convolve data
cfg_filt = [] ;
cfg_filt.bpfilter = 'yes';
cfg = [] ;
cfg.direction = 24 ;
No_Freq_Bands = 1 ;
EEG_data = SourceSpaceERF{Band_Index} ;
cfg_filt.bpfreq = [0.009 .045] ;
[EEG_data_Filtered] = ft_preprocessing(cfg_filt, EEG_data) ;
EEG1.Time_Series = EEG_data_Filtered.trial{1}(:,:) ;
% cfg_filt.bpfreq = [0.02 .065] ;
% [EEG_data_Filtered] = ft_preprocessing(cfg_filt, EEG_data) ;
% EEG2.Time_Series = EEG_data_Filtered.trial{1}(:,:) ;
%
% cfg_filt.bpfreq = [0.06 .11] ;
% [EEG_data_Filtered] = ft_preprocessing(cfg_filt, EEG_data) ;
% EEG3.Time_Series = EEG_data_Filtered.trial{1}(:,:) ;
Size_temp = size(EEG_data.trial{1},1) ;
EEG_total = EEG_data ;
EEG_total.trial{1} = zeros(Size_temp*No_Freq_Bands, size(EEG_data.trial{1},2)) ;
EEG_total.trial{1}(1 :Size_temp , :) = EEG1.Time_Series ;
% EEG_total.trial{1}(Size_temp + 1:Size_temp*2, :) = EEG2.Time_Series ;
% EEG_total.trial{1}(Size_temp*2 + 1:Size_temp*3, :) = EEG3.Time_Series ;
% EEG_total.Spatial = [EEG_data.Spatial EEG_data.Spatial EEG_data.Spatial];% EEG_data.Spatial];% EEG_data.Spatial];% EEG_data.Spatial] ;
% for I = 1:size(EEG_total.trial{1},1)
% EEG_total.label{I} = strcat('Sig',num2str(I));
% end
%
SourceSpaceERF{Band_Index} = EEG_total ;
end
%% Creating the total HRFed data
No_Vox = size(EEG_total.trial{1},1) ;
SourceSpaceERF_total = SourceSpaceERF{1} ;
SourceSpaceERF_total.trial{1} = zeros(Size_temp*size(EEGBandPass,1) , size(SourceSpaceERF{1}.trial{1},2)) ;
for Band_Index = 1:size(EEGBandPass,1)
SourceSpaceERF_total.trial{1}(1 + (Band_Index - 1)*No_Vox:No_Vox*Band_Index, :) = SourceSpaceERF{Band_Index}.trial{1} ;
end
for I = 1:size(SourceSpaceERF_total.trial{1},1)
SourceSpaceERF_total.label{I} = strcat('HRF',num2str(I));
end
%% Creating the time course of the paradigm ----____----____----
paradigm = [] ;
paradigm.time = filthrf3.time ;
paradigm.trial{1} = ones(No_Freq_Bands, size(filthrf3.time{1},2)) ;
paradigm.fsample = filthrf3.fsample ;
for I = 1:No_Freq_Bands
paradigm.label{I} = strcat('Pardig',num2str(I));
end
for sample_Index = 1:9
Start_temp = (sample_Index-1)*60*paradigm.fsample ;
paradigm.trial{1}(:, Start_temp + 1 : Start_temp + 30*paradigm.fsample ) = 0 ;
end
paradigm2 = paradigm ;
paradigm2.trial{1} = zeros(size(paradigm.trial{1},1), length(postconv)) ;
for I = 1:No_Freq_Bands
paradigm2.trial{1}(I,:) = conv( paradigm.trial{1}(I,:), hrf);
end
paradigm2.trial{1} = paradigm2.trial{1}(:,paradigm2.fsample*11 : end-paradigm2.fsample*22) ;
paradigm2.time{1} = paradigm2.time{1}(1,1:size(paradigm2.trial{1},2)) ;
cfg = [];
cfg.resamplefs = 0.454545454545455
paradigm2 = ft_resampledata(cfg, paradigm2)
paradigm3 = paradigm2 ;
cfg_filt = [] ;
cfg_filt.bpfilter = 'yes';
cfg_filt.bpfreq = [0.009 .042] ;
[paradigm_filtered] = ft_preprocessing(cfg_filt, paradigm2) ;
paradigm3.trial{1}(1,:) = paradigm_filtered.trial{1}(1,:) ;
cfg_filt.bpfreq = [0.02 .065] ;
[paradigm_filtered] = ft_preprocessing(cfg_filt, paradigm2) ;
paradigm3.trial{1}(2,:) = paradigm_filtered.trial{1}(1,:) ;
cfg_filt.bpfreq = [0.06 .11] ;
[paradigm_filtered] = ft_preprocessing(cfg_filt, paradigm2) ;
paradigm3.trial{1}(3,:) = paradigm_filtered.trial{1}(1,:) ;
paradigm3.label{2} = 'Pardig2' ;
paradigm3.label{3} = 'Pardig3' ;
%% Reduce the size of the data using SVD
cfg_Mul = [] ;
cfg_Mul.bandpass = [ .01 0.02] ;% 0.02 0.065; 0.06 0.11; ] % Defining the filter bands in Hz
cfg_Mul.bandRanks = [10];% 10; 10] ; % Manually define how many principal components from each band should be inlcuded
% cfg_Mul.pca = 'svd' ;%[] ; % or use 'svd' for the purpuse of artifact removal.
% Choosing the ICA algorithm
% SOBI gives the best decomposition with the multi-ICA algorithm
cfg_Mul.method = 'svd' ; % Other methods 'runica', 'fastica', 'binica', 'pca', 'svd', 'jader', 'varimax', 'dss', 'cca', 'sobi', 'white'
cfg_Mul.pca = 'ft_svd' ;
cfg_Mul.elec.label = SourceSpaceERF_total.label ;
[Multi_ICA_EEG] = MultiBand_ICA_Simplified(cfg_Mul, SourceSpaceERF_total);
%% Correct the spatial maps of the ICs based on the number of the bands
Multi_ICA_EEG.SpatialICs = Multi_ICA_EEG.SpatialICs ;
% for I = 1:size(Multi_ICA_EEG.SpatialICs,2)
% Multi_ICA_EEG.Spatial(:,I) =
% Multi_ICA_EEG.SpatialPCs =
% end
%
for Comp_Index = 1:size(Multi_ICA_EEG.SpatialICs,2)
Multi_ICA_EEG.SpatialICs(:,Comp_Index) = Multi_ICA_EEG.SpatialICs(:,Comp_Index)/max(abs(Multi_ICA_EEG.SpatialICs(:,Comp_Index))) ;
end
%% fMRI fMRI fMRI fMRI fMRI fMRI fMRI fMRI fMRI fMRI fMRI fMRI fMRI fMRI fMRI fMRI fMRI fMRI fMRI fMRI fMRI fMRI fMRI fMRI fMRI fMRI fMRI fMRI fMRI fMRI fMRI
%% To produce the time-series of the ICA of the MELODIC
% Load the ICA file and then load the original data
[FileName,PathName] = uigetfile('*', 'Select the fMRI_filtered_func');
fMRI_Orig = load_untouch_nii(strcat([PathName FileName]))
[FileName,PathName] = uigetfile('*', 'Select the melodic_IC');
fMRI_ica_maps = load_untouch_nii(strcat([PathName FileName]))
[FileName,PathName] = uigetfile('*', 'Select the Mask');
Mask = load_untouch_nii(strcat([PathName FileName]))
%% Converting the 4-D matirx to a 2-D matrix
cfg = []
cfg.direction = 42 ;
fMRI = FourDTwoDConvert(cfg, fMRI_Orig, Mask)
fMRI_ica_maps2 = FourDTwoDConvert(cfg, fMRI_ica_maps, Mask)
%% Now multiply the data with the ICA weights to get the time-series of the components
ICA_Time_Series_fMRI = (fMRI_ica_maps2.Time_Series'*fMRI.Time_Series) ;
%% Dummy fMRI for fieldtrip
clear fMRI_data ;
fMRI_data.trial{1} = ICA_Time_Series_fMRI ;
% fMRI_data.trial{1} = fMRI.Time_Series ;
fMRI_data.time{1} = 0:2.2:(size(fMRI.Time_Series, 2))*2.2 -2.2 ;
fMRI_data.fsample = 1/2.2 ;
for I = 1:size(ICA_Time_Series_fMRI,1)
fMRI_data.label{I} = strcat('vox',num2str(I));
end
%% Prepare the structure for the fusion
% cfg2 = [] ;
% cfg2.bpfilter = 'yes' ;
% cfg2.bpfreq = [0.009 .11] ;
% [fMRI_data] = ft_preprocessing(cfg2, fMRI_data) ;
%
fMRI1 = fMRI_data ;
fMRI1.Spatial = fMRI_ica_maps2.Time_Series ;
%% fMRI Spatial map normalization
for Comp_Index = 1:size(fMRI1.Spatial,2)
fMRI1.Spatial(:,Comp_Index) = fMRI1.Spatial(:,Comp_Index)/max(abs(fMRI1.Spatial(:,Comp_Index))) ;
end
%% Rejection of the insignificant bands
fMRI_temp = fMRI_ica_maps2 ;
fMRI_temp.Time_Series = fMRI1.Spatial ;
cfg = []
cfg.direction = 24 ;
fMRI_temp = FourDTwoDConvert(cfg, fMRI_temp, Mask)
Zsize = size(fMRI_temp.img, 3)/No_Freq_Bands ;
for Comp_Index = 1:size(fMRI1.Spatial,2)
[row,col,v] = ind2sub(size(fMRI_temp.img(:,:,:,Comp_Index)), find(abs(fMRI_temp.img(:,:,:,Comp_Index)) == 1));
if v <= size(fMRI_temp.img, 3)/No_Freq_Bands % First band
fMRI_temp.img(:,:,Zsize+1:end, Comp_Index) = 0 ;
elseif (v <= Zsize*2) % Second band
fMRI_temp.img(:,:,[1:Zsize Zsize*2+1:end], Comp_Index) = 0 ;
elseif (v <= Zsize*3) % Third band
fMRI_temp.img(:,:, [1:Zsize*2], Comp_Index) = 0 ;
% fMRI_temp.img(:,:, [1:Zsize*2 Zsize*3+1:end], Comp_Index) = 0 ;
end
end
cfg = []
cfg.direction = 42 ;
fMRI_temp = FourDTwoDConvert(cfg, fMRI_temp, Mask)
%%
fMRI1.Spatial = fMRI_temp.Time_Series ;
%% Reject the first 11 s of the fMRI
fMRI1.trial{1} = fMRI1.trial{1}(:,fMRI1.fsample*11 + 1 : end) ;
fMRI1.time{1} = fMRI1.time{1}(1,1:size(fMRI1.trial{1},2)) ;
%% Fusion Fusion Fusion Fusion Fusion Fusion Fusion Fusion Fusion Fusion Fusion Fusion Fusion Fusion Fusion Fusion Fusion Fusion Fusion Fusion Fusion Fusion
clear fMRI2 ;
fMRI2 = fMRI1 ;
cfg2 = [];
cfg2.resamplefs = fMRI2.fsample*10;
fMRI2 = ft_resampledata(cfg2, fMRI2)
%EEG2 = filtconv2 ;
%EEG2 = EEG_PrVPo2 ;
%EEG2 = EEG_total ;
%EEG2.Spatial = EEG_total.Spatial ;
%EEG2.Spatial = [] ;
EEG2 = Multi_ICA_EEG.TemporalICs ;
EEG2.Spatial = (Multi_ICA_EEG.SpatialICs) ;
cfg2 = [];
cfg2.resamplefs = EEG2.fsample*10;
EEG2 = ft_resampledata(cfg2, EEG2)
% EEG2.Spatial = Multi_ICA_EEG.SpatialICs ;
cfg2 = [];
cfg2.resamplefs = paradigm3.fsample*10;
paradigm3_upsam = ft_resampledata(cfg2, paradigm3) ;
cfg_fus = [] ;
cfg_fus.method = 'sobi' ; % Other methods 'runica', 'fastica', 'binica', 'pca', 'svd', 'jader', 'varimax', 'dss', 'cca', 'sobi', 'white'
cfg_fus.sobi_delay = 6 ;
cfg_fus.rank_Total = [15] ;
cfg_fus.fMRI = fMRI2 ;
cfg_fus.EEG = EEG2 ;
cfg_fus.paradigm = paradigm3_upsam ;
%cfg_fus.rank_auto = [] ;
FusedStuff = fuse(cfg_fus) ;
%% Plot the time courses
cfg = [];
Data = EEG2 ;
cfg.viewmode = 'vertical';
cfg.channelcolormap = [0 0 0]; %Above two lines plot in blue (events will be black)
cfg.ploteventlabels = 'colorvalue';
Limm = 11.1000000101 ;
cfg.ylim = [-Limm Limm]; %sets yscale
cfg.blocksize = 541.2;
cfg.continuous = 'yes';
ft_databrowser(cfg,Data);
%% Plot EEG Source-space Spatial ICs via 3D scatter plot and the frequency of the component
Curren_comp = 2 ;
% Frequency plot of the components
%simple3Dmapping(EEG2.Spatial(:,Curren_comp), ForwardModel.grid.pos(ForwardModel.grid.inside,:))
for ii = 1:Band_Index
simple3Dmapping(Multi_ICA_EEG.SpatialICs(1 + (ii-1)*4345:ii*4345,Curren_comp), ForwardModel.grid.pos(ForwardModel.grid.inside,:))
%simple3Dmapping(FusedStuff.SpatialICs_EEG(1 + (ii-1)*4345:ii*4345,Curren_comp), ForwardModel.grid.pos(ForwardModel.grid.inside,:))
title(strcat(['Component ', num2str(Curren_comp) 'band', num2str(ii)]))
end
%simple3Dmapping(SourceSpaceStuff2.SpatialICs_Maps(:,Curren_comp), ForwardModel.grid.pos(ForwardModel.grid.inside,:))
% simple3Dmapping(FusedStuff.SpatialICs_EEG(:,Curren_comp), ForwardModel.grid.pos(ForwardModel.grid.inside,:))
set(gca,'XLim',[-90 90],'YLim',[-110 100],'ZLim',[-90 90])
% title(strcat(['Component ', num2str(Curren_comp)]))
%% Group maps of the EEG tomos
% cfg = [] ;
% cfg.Comps = [21] ;
% SourceICA_plotmaps(cfg, SourceSpaceStuff2, ForwardModel)
%% Map on the MRI image
Curren_comp = 1 ;
for ii = 2:2
Map_Temp = Multi_ICA_EEG.SpatialICs(1 + (ii-1)*4345:ii*4345,Curren_comp) ;
Inside_count = 0;
source = ForwardModel.grid;
for Vox_Index = 1 : size(ForwardModel.grid.inside,1)
if ForwardModel.grid.inside(Vox_Index) == 1
Inside_count = Inside_count + 1;
source.avg.Map(Vox_Index) = Map_Temp(Inside_count,1) ;
else
source.avg.Map(Vox_Index) = 0;
end
end
cfg = [];
cfg.parameter = 'Map';
cfg.interpmethod = 'nearest';
source_diff_int = ft_sourceinterpolate(cfg, source, ForwardModel.mri);
source_diff_int.coordsys = 'mni' ;
%And plot the results
source_diff_int.coordsys = 'mni';
cfg = [];
cfg.funparameter = 'Map';
cfg.funcolorlim = 'maxabs';
%cfg.funcolorlim = [-10 10];
cfg.method = 'slice';
% cfg.method = 'ortho';
% cfg.atlas = [FIELDTRIPDIR '/template/atlas/aal/ROI_MNI_V4.nii'];
ft_sourceplot(cfg,source_diff_int);
%end
% Create the nii (nifti for FSL) from the 3D maps of the components
cfg = [];
cfg.filetype = 'nifti'
cfg.datatype = 'float';
cfg.parameter = 'Map'
cfg.filename = strcat(['Comp_', num2str(Curren_comp), '_Band_', num2str(ii), '_PLA_NBk_018.nii']);
ft_volumewrite(cfg, source_diff_int);
end
%
%% Convert the 2D iamges of spatial maps into the 4D structure
% These maps are the corresponding fMRI maps of fused data and are
% different from Multi_ICA_fMRI.SpatialICs. Some of these maps may not
% mean anything. Only by using the FusedStuff.SpatialICs you can
% understand how much the fMRI has contributed to every fused data
% and if contribution is high then the map is meaningful. Otherwise it
% would be random mixture of several Multi_ICA_fMRI.SpatialICs maps.
cfg = []
cfg.direction = 24 ;
%fMRI_saved = fMRI2 ;
%fMRI.Time_Series = Multi_ICA_fMRI.SpatialICs ;
% fMRI.Time_Series = fMRI1.Spatial ;
fMRI.Time_Series = FusedStuff.SpatialICs_fMRI ;
%fMRI.Time_Series = fMRI_ica_maps2.Time_Series*Multi_ICA_fMRI.SpatialICs ;
fMRI.Time_Series = (fMRI.Time_Series./(max(max(fMRI.Time_Series)))).*10 ;
fMRI_saved = FourDTwoDConvert(cfg, fMRI, Mask)
fMRI_saved.hdr.dime.dim(1,5) = size(fMRI_saved.Time_Series,2) ;
%% Convert the 2D iamges of spatial maps into the 4D structure and sum up the images of different bands
% These maps are the corresponding fMRI maps of fused data and are
% different from Multi_ICA_fMRI.SpatialICs. Some of these maps may not
% mean anything. Only by using the FusedStuff.SpatialICs you can
% understand how much the fMRI has contributed to every fused data
% and if contribution is high then the map is meaningful. Otherwise it
% would be random mixture of several Multi_ICA_fMRI.SpatialICs maps.
ZSize = size(fMRI_Orig.img,3)/No_Freq_Bands ;
cfg.direction = 24 ;
fMRI_Temp.Time_Series = FusedStuff.SpatialICs_fMRI ;
fMRI_Temp = FourDTwoDConvert(cfg, fMRI, Mask)
for I_I = 2:No_Freq_Bands
fMRI_Temp.img(:,:,1:ZSize,:) = fMRI_Temp.img(:,:,(ZSize)*(I_I-1) + 1 : ZSize*I_I , :) + fMRI_Temp.img(:,:,1:ZSize,:) ;
end
fMRI_Temp.img = fMRI_Temp.img(:,:,1:ZSize,:) ;
fMRI_saved = fMRI_Temp;
fMRI_saved.hdr.dime.dim(1,4) = ZSize ;
fMRI_saved.hdr.dime.dim(1,5) = size(fMRI_saved.img,4) ;
fMRI_saved = rmfield(fMRI_saved, 'Time_Series');
fMRI_saved = rmfield(fMRI_saved, 'Time_Series_MeanRem');
fMRI_saved = rmfield(fMRI_saved, 'Coordinate');
clear fMRI_Temp ;
%% Save the fMIR maps
save_untouch_nii(fMRI_saved, 'Fused_fMRI_Component')