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%% Step 2 Oddball
clear all; clc
addpath('Z:\EXPERIMENTS\mTBICoBRE\EEG\');
savedir='Z:\EXPERIMENTS\mTBICoBRE\EEG\3AOB Processed\';
load('Z:\EXPERIMENTS\mTBICoBRE\EEG\BV_Chanlocs_60.mat');
% ########## For Cavanagh data
datadir='Z:\EXPERIMENTS\mTBICoBRE\EEG\3AOB Preproc\';
[D_DAT,D_HDR,D_ALL]=xlsread('Z:\EXPERIMENTS\mTBICoBRE\ANALYSIS\QUALITY_CHECK.xlsx','ODDBALL_ICAs');
FILEENDER='_3AOB.mat';
% % % ########## For Quinn data
% % datadir='Z:\EXPERIMENTS\mTBICoBRE\EEG\QUINN 3AOB Preproc\';
% % [D_DAT,D_HDR,D_ALL]=xlsread('Z:\EXPERIMENTS\mTBICoBRE\ANALYSIS\QUINN_QUALITY_CHECK.xlsx','ODDBALL_ICAs');
% % FILEENDER='_QUINN_3AOB.mat';
cd(datadir);
% ############# Set Params
srate=500;
tx=-2000:1000/srate:1998;
B1=find(tx==-300); B2=find(tx==-200);
T1=find(tx==-500); T2=find(tx==1000);
tx2disp=-500:2:1000;
% #############
for si=1:length(D_DAT)
for sess=1:size(D_DAT,2)-1 % should be '2' for Quinn data, '3' for Cavanagh data
subno=D_DAT(si,1);
skip=0;
INFO=D_ALL{si+1,sess+1}; % +1's b/c of subno column and header row
disp(['TRYOUT ',num2str(subno),' S',num2str(sess)]);
if isnumeric(INFO), bad_ICAs_To_Remove=INFO; end
if isnan(INFO), skip=1; end % not done yet
if strmatch('BAD',INFO), skip=1; end % Bad data
if ~isnumeric(INFO), bad_ICAs_To_Remove=str2num(INFO); end
% Don't repeat if already done
if exist([savedir,num2str(subno),'_',num2str(sess),'_3AOB_TFandERPs_L.mat'])==2, skip=1; end
if skip==0
load([num2str(subno),'_',num2str(sess),FILEENDER]); disp(['DOING: ',num2str(subno),'_',num2str(sess),'_3AOB.mat']);
% Remove the bad ICAs:
disp(['BAD ICAS: ', num2str(bad_ICAs_To_Remove)]);
EEG = pop_subcomp( EEG, bad_ICAs_To_Remove, 0);
% Get the good info out of the epochs
for ai=1:size(EEG.epoch,2)
% Initialize
EEG.epoch(ai).EEG=NaN;
for bi=1:size(EEG.epoch(ai).eventlatency,2)
% Get STIMTYPE
if EEG.epoch(ai).eventlatency{bi}==0 && isempty(strmatch(EEG.epoch(ai).eventtype{bi},'N999')); % If this bi is the event
% Get StimType
FullName=EEG.epoch(ai).eventtype{bi};
EEG.epoch(ai).EEG=str2num(FullName(2:end)) ;
clear FullName
VECTOR(ai,1)=EEG.epoch(ai).EEG; All_STIM={'S201','S200','S202'}; % Std, Target, Novel
end
end
end
% Only as many STD as NOV
N_n=sum(VECTOR(:,1)==202);
temp_idxs=find(VECTOR(:,1)==201);
temp_idxs=shuffle(temp_idxs);
VECTOR(temp_idxs(N_n+1:end),1)=999; clear temp_idxs;
% Save trial counts
TRL_ct(1)=sum(VECTOR(:,1)==201);
TRL_ct(2)=sum(VECTOR(:,1)==200);
TRL_ct(3)=sum(VECTOR(:,1)==202);
%%
% $$$$$$$$$$$$$$$$$$$$$$$ $$$$$$$$$$$$$$$$$$$$$$$
% $$$$$$$$$$$$$$$$$$$$$$$ Time-Freq
% $$$$$$$$$$$$$$$$$$$$$$$ $$$$$$$$$$$$$$$$$$$$$$$
% Setup Wavelet Params
num_freqs=50;
frex=logspace(.01,1.7,num_freqs);
s=logspace(log10(3),log10(10),num_freqs)./(2*pi*frex);
t=-2:1/EEG.srate:2;
% Definte Convolution Parameters
dims = size(EEG.data);
n_wavelet = length(t);
n_data = dims(2)*dims(3);
n_convolution = n_wavelet+n_data-1;
n_conv_pow2 = pow2(nextpow2(n_convolution));
half_of_wavelet_size = (n_wavelet-1)/2;
% For Laplacian
X = [BV_Chanlocs_60.X]; Y = [BV_Chanlocs_60.Y]; Z = [BV_Chanlocs_60.Z];
% Pick channel
chans=[36,33,56]; % FCz, F5, F6
for REFi=1:2
if REFi==1, TAG='V';
elseif REFi==2, TAG='L';
[EEG.data,~,~] = laplacian_perrinX(EEG.data,X,Y,Z,[],1e-6);
end
% Get FFT of data
for chani=1:3
EEG_fft(chani,:) = fft(reshape(EEG.data(chans(chani),:,:),1,n_data),n_conv_pow2);
end
for fi=1:num_freqs
wavelet = fft( exp(2*1i*pi*frex(fi).*t) .* exp(-t.^2./(2*(s(fi)^2))) , n_conv_pow2 ); % sqrt(1/(s(fi)*sqrt(pi))) *
% convolution
for chani=1:3
temp_conv = ifft(wavelet.*EEG_fft(chani,:));
temp_conv = temp_conv(1:n_convolution);
temp_conv = temp_conv(half_of_wavelet_size+1:end-half_of_wavelet_size);
EEG_conv(chani,:,:) = reshape(temp_conv,dims(2),dims(3));
clear temp_conv;
% Common pre-EEG baseline
temp_BASE(chani,:) = mean(mean(abs(EEG_conv(chani,B1:B2,:)).^2,2),3);
end
for idx=1:3
if idx==1, idx_V=VECTOR(:,1)==201; % STD
elseif idx==2, idx_V=VECTOR(:,1)==200; % TARG
elseif idx==3, idx_V=VECTOR(:,1)==202; % NOV
end
for chani=1:3
temp_PWR = squeeze(mean(abs(EEG_conv(chani,T1:T2,idx_V)).^2,3));
POWER(chani,fi,:,idx) = 10* ( log10(temp_PWR') - log10(repmat(temp_BASE(chani,:),size(tx2disp,2),1)) );
ITPC(chani,fi,:,idx) = abs(mean(exp(1i*( angle(EEG_conv(chani,T1:T2,idx_V)) )),3));
if chani==1, seed=1; targ=2;
elseif chani==2, seed=1; targ=3;
elseif chani==3, seed=2; targ=3;
end
ISPC(chani,fi,:,idx) = abs(mean(exp(1i*( angle(EEG_conv(seed,T1:T2,idx_V)) - angle(EEG_conv(targ,T1:T2,idx_V)) )),3));
clear temp_PWR;
end
clear idx_V ;
end
clear wavelet idx_V temp_BASE EEG_conv;
end
%%
% $$$$$$$$$$$$$$$$$$$$$$$ $$$$$$$$$$$$$$$$$$$$$$$
% $$$$$$$$$$$$$$$$$$$$$$$ Theta Topo
% $$$$$$$$$$$$$$$$$$$$$$$ $$$$$$$$$$$$$$$$$$$$$$$
topofrex=4.5;
s=logspace(log10(3),log10(10),num_freqs)./(2*pi*topofrex);
wavelet = fft( exp(2*1i*pi*frex(fi).*t) .* exp(-t.^2./(2*(s(fi)^2))) , n_conv_pow2 ); % sqrt(1/(s(fi)*sqrt(pi))) *
seed=36;
EEG_fft_4TOPO = fft(reshape(EEG.data(seed,:,:),1,n_data),n_conv_pow2);
seed_EEG_conv_4TOPO = ifft(wavelet.*EEG_fft_4TOPO);
seed_EEG_conv_4TOPO = seed_EEG_conv_4TOPO(1:n_convolution);
seed_EEG_conv_4TOPO = seed_EEG_conv_4TOPO(half_of_wavelet_size+1:end-half_of_wavelet_size);
seed_EEG_conv_4TOPO = reshape(seed_EEG_conv_4TOPO,dims(2),dims(3));
clear EEG_fft_4TOPO ;
% Common pre-EEG SEED baseline
seed_BASE = mean(mean(abs(seed_EEG_conv_4TOPO(B1:B2,:)).^2,1),2);
for chani=1:60
EEG_fft_4TOPO = fft(reshape(EEG.data(chani,:,:),1,n_data),n_conv_pow2);
EEG_conv_4TOPO = ifft(wavelet.*EEG_fft_4TOPO);
EEG_conv_4TOPO = EEG_conv_4TOPO(1:n_convolution);
EEG_conv_4TOPO = EEG_conv_4TOPO(half_of_wavelet_size+1:end-half_of_wavelet_size);
EEG_conv_4TOPO = reshape(EEG_conv_4TOPO,dims(2),dims(3));
% Common pre-EEG baseline
temp_BASE = mean(mean(abs(EEG_conv_4TOPO(B1:B2,:)).^2,1),2);
for idx=1:3
if idx==1, idx_V=VECTOR(:,1)==201; % STD
elseif idx==2, idx_V=VECTOR(:,1)==200; % TARG
elseif idx==3, idx_V=VECTOR(:,1)==202; % NOV
end
temp_PWR = squeeze(mean(abs(EEG_conv_4TOPO(T1:T2,idx_V)).^2,2));
POWER_TOPO(chani,:,idx) = 10* ( log10(temp_PWR) - log10(repmat(temp_BASE,size(tx2disp,2),1)) );
S4cor=10* ( log10(abs(seed_EEG_conv_4TOPO(T1:T2,idx_V)).^2) - log10(repmat(seed_BASE,size(tx2disp,2),sum(idx_V))) );
T4cor=10* ( log10(abs(EEG_conv_4TOPO(T1:T2,idx_V)).^2) - log10(repmat(temp_BASE,size(tx2disp,2),sum(idx_V))) );
CORREL_TOPO(chani,:,idx)= diag(corr(S4cor',T4cor','type','Spearman'));
SYNCH_TOPO(chani,:,idx) = abs(mean(exp(1i*( angle(seed_EEG_conv_4TOPO(T1:T2,idx_V)) - angle(EEG_conv_4TOPO(T1:T2,idx_V)) )),2));
clear idx_V temp_PWR S4cor T4cor;
end
clear EEG_fft_4TOPO EEG_conv_4TOPO TOPO_conv temp_BASE;
end
%%
% $$$$$$$$$$$$$$$$$$$$$$$ $$$$$$$$$$$$$$$$$$$$$$$
% $$$$$$$$$$$$$$$$$$$$$$$ ERPs
% $$$$$$$$$$$$$$$$$$$$$$$ $$$$$$$$$$$$$$$$$$$$$$$
% Filter
dims=size(EEG.data);
EEG.data=eegfilt(EEG.data,500,[],20);
EEG.data=eegfiltfft(EEG.data,500,.1,[]);
EEG.data=reshape(EEG.data,dims(1),dims(2),dims(3));
% Basecor your ERPs here so they are pretty.
EEG_BASE=squeeze( mean(EEG.data(:,find(tx==-200):find(tx==0),:),2) );
for ai=1:dims(1)
EEG.data(ai,:,:)=squeeze(EEG.data(ai,:,:))-repmat( EEG_BASE(ai,:),dims(2),1 );
end
% Get ERPs
for idx=1:3
if idx==1, idx_V=VECTOR(:,1)==201; % STD
elseif idx==2, idx_V=VECTOR(:,1)==200; % TARG
elseif idx==3, idx_V=VECTOR(:,1)==202; % NOV
end
ERP(:,:,idx)=squeeze(mean(EEG.data(:,find(tx==-500):find(tx==1000),idx_V),3));
clear DATA_erp idx_V ;
end
save([savedir,num2str(subno),'_',num2str(sess),'_3AOB_TFandERPs_',TAG,'.mat'],'ERP','ISPC','ITPC','POWER','VECTOR','SYNCH_TOPO','TRL_ct','POWER_TOPO','CORREL_TOPO');
clear ERP ISPC ITPC POWER RT;
end
clearvars -except datadir savedir FILEENDER BV_Chanlocs_60 D_DAT D_HDR D_ALL tx B1 B2 T1 T2 tx2disp si sess
end
end
end
%%
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