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%% Step2: PostProcessing data: ERP analysis; Time-Frequncy analaysis, ITPC analysis
% Import preprocessed data
clear; clc; close all;
datalocation='D:\Project_EEG_CC\CC_Results_step1\'; % Data are here
savedir = 'D:\Project_EEG_CC\CC_PD_Figures_Manuscript\CC_Manuscript\Manuscript_Scripts_PREDICT\Data\'; % save data here
cd(savedir);
load('D:\Project_EEG_CC\mFiles\ONOFF.mat','ONOFF')
load('D:\Project_EEG_CC\mFiles\BV_Chanlocs_60.mat');
[num_cc,txt_cc,raw_cc]=xlsread('D:\Project_EEG_CC\CC_ICAs.xlsx');
% subjects
PDsx=[801:811,813:823,824:829]; % 824 S2 CC is bad (mange in Step 3)
CTLsx=[8010,8070,8060,890:914]; % 911 S1 CC is bad (mange in Step 3)
%%%%%%%%% or run 824 afterwards since session 2 is bad OR use sessiosn 1
%%%%%%%%% for both OFF and ON
%% ##########################
for subj=[CTLsx(end:-1:1),PDsx(end:-1:1)]
for session=1:2
if (subj>850 && session==1) || subj<850 % If not ctl, do session 2
if 1 % exist([num2str(subj),'_Session_',num2str(session),'_PDDys_CC.mat'])~=2;
% ---------------- GET PD and Control DATA ---------------- ---------------- ----------------
% &&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&& % &&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&
disp([num2str(subj),'_Session_',num2str(session),'_PDDys_CC.mat'])
load([num2str(subj),'_Session_',num2str(session),'_PDDys_CC.mat'],'EEG','bad_chans','bad_epochs','bad_ICAs');
% Get Subj Info
temp1=cell2mat(raw_cc(find(num_cc(:,1)==subj),session+1));
if isnumeric(temp1)
bad_ICAs_To_Remove=temp1;
elseif strmatch('NaN',temp1)
bad_ICAs_To_Remove=NaN;
else
bad_ICAs_To_Remove=str2num(temp1);
end
clear temp1;
% Remove the (presumptive) bad ICAs:
if ~(isnan(bad_ICAs_To_Remove))
EEG = pop_subcomp( EEG, bad_ICAs_To_Remove, 0);
end
clear bad_ICAs_To_Remove;
% % &&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&& GET Epochs % &&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&
CONGRU=[111,112,113,114,211,212,213,214];
INCONGRU=[121,122,123,124,221,222,223,224];
CORRECT=[101,102];
ERROR=[103,104];
REW=8;
PUN=9;
% Get the good info out of the epochs
for aai=1:size(EEG.epoch,2)
EEG.epoch(aai).TYPE=NaN; EEG.epoch(aai).RESP=NaN; EEG.epoch(aai).RT=NaN;
RESP_VECTOR(aai,1:2)=NaN;
for bbi=1:size(EEG.epoch(aai).eventlatency,2)
% Get STIMTYPE
if EEG.epoch(aai).eventlatency{bbi}==0 % If this bi is the event
% Get StimType
FullName=EEG.epoch(aai).eventtype{bbi};
% IF TRN CUE
if any(str2num(FullName(2:end))==[CONGRU,INCONGRU])
EEG.epoch(aai).TYPE=str2num(FullName(2:end)) ;
if any(str2num(FullName(2:end))==CONGRU)
VECTOR(aai)=5;
elseif any(str2num(FullName(2:end))==INCONGRU)
VECTOR(aai)=6;
end
% If anything is next
if size(EEG.epoch(aai).eventlatency,2)>=bbi
% If RESP
tempName=EEG.epoch(aai).eventtype{bbi+1};
if any(str2num(tempName(2:end))==[CORRECT,ERROR])
EEG.epoch(aai).RESP=str2num(tempName(2:end)) ;
EEG.epoch(aai).RT=EEG.epoch(aai).eventlatency{bbi+1};
RESP_VECTOR(aai,1)=str2num(tempName(2:end));
RESP_VECTOR(aai,2)=EEG.epoch(aai).eventlatency{bbi+1};
end
end
else
EEG.epoch(aai).TYPE=str2num(FullName(2:end)) ;
VECTOR(aai)=str2num(FullName(2:end));
end
clear FullName tempName
end
end
end
% Aggregate accelerometer data
EEG.X=EEG.X-repmat(mean(EEG.X),3250,1);
EEG.Y=EEG.Y-repmat(mean(EEG.Y),3250,1);
EEG.Z=EEG.Z-repmat(mean(EEG.Z),3250,1);
% Add to EEG.data as 61st channel - but not the rejected trials
if subj==824 && session==2, clear bad_epochs; bad_epochs{1}=zeros(1,size(EEG.data,3)); end % B/c 824 S2 is bad - hack this
EEG.data(61,:,:)=(EEG.X(:,bad_epochs{1}~=1).^2)+(EEG.Y(:,bad_epochs{1}~=1).^2)+(EEG.Z(:,bad_epochs{1}~=1).^2);
dims=size(EEG.data);
% Lock to Response, Stim, and Cue
respct=1;
for ai=1:size(EEG.epoch,2)
if any(RESP_VECTOR(ai,1)==[CORRECT,ERROR])
Cue_to_Resp=RESP_VECTOR(ai,2) ./ (1000/EEG.srate);
if isnan(Cue_to_Resp), Cue_to_Resp=1; end
EEG.resp(:,:,respct)=[squeeze(EEG.data(:,Cue_to_Resp:end,ai)),zeros(dims(1),(Cue_to_Resp-1))];
if any(RESP_VECTOR(ai,1)==CORRECT)
VECTOR_resp(respct,1)=1; VECTOR_resp(respct,2)=Cue_to_Resp;
elseif any(RESP_VECTOR(ai,1)==ERROR)
VECTOR_resp(respct,1)=2; VECTOR_resp(respct,2)=Cue_to_Resp;
end
respct=respct+1;
clear Cue_to_Resp;
end
end
clear RESP_VECTOR;
% % Set Times
tx=-1500:2:4998;
b1=find(tx==-300); b2=find(tx==-200); %% original
t1=find(tx==-500); t2=find(tx==1000);
tx2disp=-500:2:1000;
% ------------------------ Get the goods
X_CUE{1}=5; % CONGRU
X_CUE{2}=6; % INCONGRU
X_RESP{1}=1; % CORRECT RESP
X_RESP{2}=2; % ERROR RESP
% ---------- % ---------- % ----------
% ---------- TF stuff
% ---------- % ---------- % ----------
% 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
n_wavelet = length(t);
half_of_wavelet_size = (n_wavelet-1)/2; clear dims
% -------- cue/fb
dims{1} = size(EEG.data); n_data{1} = dims{1}(2)*dims{1}(3); n_convolution{1} = n_wavelet+n_data{1}-1; n_conv_pow2{1} = pow2(nextpow2(n_convolution{1}));
% -------- resp
dims{2} = size(EEG.resp); n_data{2} = dims{2}(2)*dims{2}(3); n_convolution{2} = n_wavelet+n_data{2}-1; n_conv_pow2{2} = pow2(nextpow2(n_convolution{2}));
CHANS= (1:60);
for chani=1:60
% get FFT of data
EEG_fft{1} = fft(reshape(EEG.data(CHANS(chani),:,:),1,n_data{1}),n_conv_pow2{1});
EEG_fft{2} = fft(reshape(EEG.resp(CHANS(chani),:,:),1,n_data{2}),n_conv_pow2{2});
for fi=1:num_freqs
wavelet{1} = fft( exp(2*1i*pi*frex(fi).*t) .* exp(-t.^2./(2*(s(fi)^2))) , n_conv_pow2{1} );
wavelet{2} = fft( exp(2*1i*pi*frex(fi).*t) .* exp(-t.^2./(2*(s(fi)^2))) , n_conv_pow2{2} );
% convolution
for convo=1:2
EEG_conv = ifft(wavelet{convo}.*EEG_fft{convo});
EEG_conv = EEG_conv(1:n_convolution{convo});
EEG_conv = EEG_conv(half_of_wavelet_size+1:end-half_of_wavelet_size);
EEG_multi_conv{convo} = reshape(EEG_conv,dims{convo}(2),dims{convo}(3)); clear EEG_conv;
temp_POWER{convo} = abs(EEG_multi_conv{convo}(t1:t2,:)).^2;
end
% Baseline from pre-cue {1}
BASE = mean(mean(abs( EEG_multi_conv{1}(b1:b2,VECTOR<7)).^2));
% Average FIRST
for condi=1:2
temp_POWER_avg(:,condi,1) = mean(temp_POWER{1}(:,VECTOR==X_CUE{condi}),2);
temp_POWER_avg(:,condi,2) = mean(temp_POWER{2}(:,VECTOR_resp==X_RESP{condi}),2);
% -------------------
ITPC(chani,fi,:,condi,1) = abs(mean(exp(1i*( angle(EEG_multi_conv{1}(t1:t2,VECTOR==X_CUE{condi})) )),2));
ITPC(chani,fi,:,condi,2) = abs(mean(exp(1i*( angle(EEG_multi_conv{2}(t1:t2,VECTOR_resp==X_RESP{condi})) )),2));
end
% dB correct power by base
for condi=1:2
for event=1:2
POWER(chani,fi,:,condi,event) = 10*( log10(temp_POWER_avg(:,condi,event)) - log10(repmat(BASE,size(temp_POWER_avg(:,condi,event),1),1)) );
end
end
% Actually, save these for later
Baselines(chani,fi,1)=BASE;
clear temp* EEG_multi_conv wavelet BASE PE;
end
clear *_fft;
end
% % ---------- % ---------- % ----------
% ---------- ERP stuff
% ---------- % ---------- % ----------
% Filter for ERPs
dims=size(EEG.data); EEG.data=eegfilt(EEG.data,500,[],20); EEG.data=reshape(EEG.data,dims(1),dims(2),dims(3));
dims=size(EEG.resp); EEG.resp=eegfilt(EEG.resp,500,[],20); EEG.resp=reshape(EEG.resp,dims(1),dims(2),dims(3));
% Basecor your ERPs here so they are pretty ------------>
% You can also remove this if you want...
BASE1=squeeze( mean(EEG.data(:,b1:b2,:),2) );
BASE2=squeeze( mean(EEG.resp(:,b1:b2,:),2) );
for chani=1:dims(1)
EEG.data(chani,:,:)=squeeze(EEG.data(chani,:,:))-repmat( BASE1(chani,:),dims(2),1 );
EEG.resp(chani,:,:)=squeeze(EEG.resp(chani,:,:))-repmat( BASE2(chani,:),dims(2),1 );
end
for condi=1:2
% Mean for ERPs
ERPs(1:60,:,condi,1)=mean(EEG.data(1:60,t1:t2, VECTOR==X_CUE{condi} ),3);
ERPs(1:60,:,condi,2)=mean(EEG.resp(1:60,t1:t2, VECTOR_resp==X_RESP{condi} ),3);
end
% %
%%%%% save results
save([savedir,num2str(subj),'_Session_',num2str(session),'_PDDys_CC_ALL_GOODS.mat'],'ERPs','VECTOR','VECTOR_resp','POWER','ITPC','Baselines');
clearvars -except datalocation ONOFF PDsx CTLsx session subj num_cc txt_cc raw_cc savedir
end
end
end
end
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