%% 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