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%% For SFFT analysis: import preprocessed REST and Task-related data
clear; clc; close all;
datalocation='D:\Project_EEG_CC\CC_Results_step1\'; % Data are here
cd(datalocation);
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');
[num_rest,txt_rest,raw_rest]=xlsread('D:\Project_EEG_CC\PD_Rest_Data\REST_ICAs.xlsx'); %% needed file
PDsx=[801:811,813:823,825:829]; % 824 S2 CC is bad (mange in Step 3) %%%%do 824 saperatly at the end
CTLsx=[8010,8070,8060,890:914]; % 911 S1 CC is bad (mange in Step 3)
%% ########################
for subj= [CTLsx(end:-1:1),PDsx(end:-1:1)] % [PDsx,CTLsx] %
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;
% ---------------- REST!!! ---------------- ---------------- ----------------
disp([num2str(subj),'_',num2str(session),'_PDDys_REST.mat'])
if subj==803 && session==1;
load([num2str(subj),'_',num2str(2),'_PDDys_REST.mat'],'EEG'); % 803 S1 is bad, use their S2 instead.
else
load([num2str(subj),'_',num2str(session),'_PDDys_REST.mat'],'EEG'); %%% needed those files
end
% Get Subj Info
temp1=cell2mat(raw_rest(find(num_rest(:,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;
REST=EEG; clear EEG;
%%% save data for channels Cue locked
CHANS=(1:60);
for chani = 1:60
mData_REST{:,chani} = nanmean(squeeze(REST.data(CHANS(chani),:,:)),2);
end
% ---------------- ---------------- ---------------- ---------------- ----------------
% &&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&& % &&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&
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;
% &&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&& % &&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&
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
% Set Times
tx=-1500:2:4998;
b1=find(tx==-1500); b2=find(tx==0); %%% I changed it
t1=find(tx==-1500); 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
%%% save data for channels Cue locked
CHANS=(1:60); % ALL Chans
for chani = 1:60
mData_Cue{:,chani} = nanmean(squeeze(EEG.data(CHANS(chani),:,:)),2);
end
save(['D:\Project_EEG_CC\TopoFigure\Data_Topo_new\',num2str(subj),'_Session_',num2str(session),'_PDDys_CC_ALL_TOPO.mat'],...
'VECTOR','VECTOR_resp','RESP_VECTOR','mData_Cue', 'mData_REST');
end
end
end
end
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