scripts/Step2_CC_PostProcess_SFFT.m

%% 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