scripts/Step2_CC_PostProcess_CORRECT_Trials.m

%% Step2: PostProcessing data for CORRECT trials only: ERP analysis; Time-Frequncy analaysis, ITPC analysis
% Import preprocessed data. This analysis will look results for correct trials of Congruent
% and Incongruent trials

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\CORRECT\'; % 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])  %%%%%%%%%%%%%%%%%%%%%% get Data only for CORRECT trials no Error trials
                        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;
             
       % %%%%%%%% Look for Congruent and Incongruent individually for Correct Response
        for ii = 1:size(EEG.epoch,2)
        RESP_VECTOR2(ii,1)= EEG.epoch(ii).TYPE;
        RESP_VECTOR2(ii,2)= EEG.epoch(ii).RESP;
        RESP_VECTOR2(ii,3)= EEG.epoch(ii).RT./ (1000/EEG.srate);
        end
        RESP_VECTOR3 = RESP_VECTOR2;
        %%% look for the NaN and delete that row
        RESP_VECTOR3(isnan(RESP_VECTOR3(:,2)),:) = [];

        %%%%% look for Error only and delete it
        ErrId = find (RESP_VECTOR3(:,2)==103 | RESP_VECTOR3(:,2)==104);

        RESP_VECTOR3(ErrId,2) = NaN;
        RESP_VECTOR3(isnan(RESP_VECTOR3(:,2)),:) = [];
        RESP_VECTOR3(:,2) = 1;

        %%%% now look for Cong and Incong in Only CORRECT trials
        RESP_VECTOR_STIM = RESP_VECTOR3;
        %%%% for Congruent correct trials 
        RESP_VECTOR_STIM(RESP_VECTOR_STIM(:,1)>110 & RESP_VECTOR_STIM(:,1)<115) = 1;
        RESP_VECTOR_STIM(RESP_VECTOR_STIM(:,1)>210 & RESP_VECTOR_STIM(:,1)<215) = 1;

        %%%% for Inongruent correct trials 
        RESP_VECTOR_STIM(RESP_VECTOR_STIM(:,1)>120 & RESP_VECTOR_STIM(:,1)<125) = 2;
        RESP_VECTOR_STIM(RESP_VECTOR_STIM(:,1)>220 & RESP_VECTOR_STIM(:,1)<225) = 2;
        
        %%%% creat VECTOR_resp for Cong and Incong
        VECTOR_respCI = RESP_VECTOR_STIM;
        VECTOR_respCI(:,2) = [];
               
               % 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_RESP{1}=1; % Congruent...Correct RESP
                X_RESP{2}=2; % Incongruent..Correct 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); %   [36,21,7,22];  % FCz, Cz, C3, C4
                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) = mean(temp_POWER{2}(:,VECTOR_respCI==X_RESP{condi}),2);
                             % -------------------
                            ITPC(chani,fi,:,condi) = abs(mean(exp(1i*(  angle(EEG_multi_conv{2}(t1:t2,VECTOR_respCI==X_RESP{condi}))  )),2));
                        end   
                         
                        % dB correct power by base
                        for condi=1:2
                            POWER(chani,fi,:,condi) = 10*( log10(temp_POWER_avg(:,condi)) - log10(repmat(BASE,size(temp_POWER_avg(:,condi),1),1)) );
                        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
                % ---------- % ---------- % ----------  
                
                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 ------------>
                BASE2=squeeze(  mean(EEG.resp(:,b1:b2,:),2)  );
                for chani=1:dims(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,2)=mean(EEG.resp(1:60,t1:t2, VECTOR_respCI==X_RESP{condi} ),3);
                end
                 
               %%%%% save results                                              
               save([savedir,num2str(subj),'_Session_',num2str(session),'_PDDys_CC_ALL_GOODS.mat'],'ERPs','VECTOR','VECTOR_resp','VECTOR_respCI','POWER','ITPC','Baselines');
               
               clearvars -except datalocation ONOFF PDsx CTLsx session subj num_cc txt_cc raw_cc savedir
                
            end
        end
    end
end