%% Step 2 - load pre-proc'd flankers data clear all; clc addpath('Z:\EXPERIMENTS\Flankers t2t\'); datalocation='Y:\EEG_Data\Flankers OCDII\Processed Data\'; % Data are here savedir='Y:\EEG_Data\Flankers OCDII\ERPs\'; % Save processed data here cd(datalocation); % Load up [xls_data,xls_hdr]=xlsread('Z:\EXPERIMENTS\Flankers t2t\info.xlsx'); % Data are 60 chans * 2000 time * N epochs | Ref'd to linked mastoids FILZ=dir('*_FLANKERS.mat'); for si=length(FILZ):-1:1 subname=FILZ(si).name; subno=str2num(subname(1:3)); clc % Only run the latter on subjs pre-selected to be MUY BUENO (N=46) if ~isempty(find(xls_data(:,1)==subno)) % Only run if not already done if 1 % ~(exist([savedir,num2str(subno),'_ERPs.mat'])==2) % Display disp(['DOING: ',num2str(subno)]); % Get Subj Info tempdat=xls_data(find(xls_data(:,1)==subno),:); tempdat_hdr=xls_hdr(find(xls_data(:,1)==subno)+1,:); % +1 b/c of header row SUBJINFO(1)=subno; SUBJINFO(2)=tempdat(1); SUBJINFO(3)=tempdat(4); SUBJINFO(4)=tempdat(6); SUBJINFO(5)=tempdat(7); SUBJINFO(6)=tempdat(9); SUBJINFO_HDR={'EEG_ID','INFO_ID','Error%','Sex','Age','OCI_Score'}; % if ~isnan(tempdat(5)) bad_ICAs_To_Remove=tempdat(5); elseif isnan(tempdat(5)) bad_ICAs_To_Remove=str2num(cell2mat(tempdat_hdr(5))); end clear tempdat tempdat_hdr; % Load EEG load([datalocation,subname]); % Remove the bad ICAs: EEG = pop_subcomp( EEG, bad_ICAs_To_Remove, 0); % Get the good info out of the epochs VECTOR(1:size(EEG.epoch,2),1:13)=NaN; for ai=1:size(EEG.epoch,2) VECTOR(ai,1)=EEG.epoch(ai).TRIALNUM; VECTOR(ai,2)=EEG.epoch(ai).STIM; VECTOR(ai,3)=EEG.epoch(ai).CONGRU; VECTOR(ai,4)=EEG.epoch(ai).RightLeft; VECTOR(ai,5)=EEG.epoch(ai).RT; VECTOR(ai,6)=EEG.epoch(ai).ACC; % ^^^^^^^^^^^^^^^^^^ OOPS if ~isempty(EEG.epoch(ai).PREVTRIAL) % ^^^^^^^^^^^^^^^^^^ VECTOR(ai,7)=EEG.epoch(ai).PREVTRIAL.TRIALNUM; VECTOR(ai,8)=EEG.epoch(ai).PREVTRIAL.STIM; VECTOR(ai,9)=EEG.epoch(ai).PREVTRIAL.CONGRU; VECTOR(ai,10)=EEG.epoch(ai).PREVTRIAL.RightLeft; VECTOR(ai,11)=EEG.epoch(ai).PREVTRIAL.RT; VECTOR(ai,12)=EEG.epoch(ai).PREVTRIAL.ACC; VECTOR(ai,13)=EEG.epoch(ai).PREVTRIAL.BlockStart; end end % Let's just do this for ERPs - keep EEG.data structure unfiltered dims=size(EEG.data); FILT.data=eegfilt(EEG.data,500,[],20); FILT.data=reshape(FILT.data,dims(1),dims(2),dims(3)); % Set Params tx=-2000:2:1998; b1=find(tx==-200); b2=find(tx==0); t1=find(tx==-500); t2=find(tx==1000); N2topo1=find(tx==200); N2topo2=find(tx==350); N2toporangetot=200:2:350; P3topo1=find(tx==400); P3topo2=find(tx==600); P3toporangetot=400:2:600; tx2disp=-500:2:1000; % Basecor your ERPs here so they are pretty. BASE=squeeze( mean(FILT.data(:,b1:b2,:),2) ); for ai=1:dims(1) FILT.data(ai,:,:)=squeeze(FILT.data(ai,:,:))-repmat( BASE(ai,:),dims(2),1 ); end % $$$$$$$$$$$$$$$$$$$ Parse condis and save ERPs $$$$$$$$$$$$$$$$$$$ % % If blockstart, then NaN the row VECTOR(VECTOR(:,13)==1,[3,4,9,10])=NaN; % If error, then NaN the row VECTOR(VECTOR(:,6)==0,[3,4,9,10])=NaN; % If post-error, then NaN the row temp=VECTOR(:,6)==0; VECTOR(logical([0;temp(1:end-1)]),[3,4,9,10])=NaN; clear temp; % If RT<200 or NaN, then NaN the row VECTOR(VECTOR(:,5)<200,[3,4,9,10])=NaN; VECTOR(isnan(VECTOR(:,5)),[3,4,9,10])=NaN; % If any >2, all are NaN temp=sum((VECTOR(:,[3,4,9,10])>2)')'; VECTOR(temp>0,[3,4,9,10])=NaN; % If any are NaN, all are NaN temp=sum(isnan(VECTOR(:,[3,4,9,10]))')'; VECTOR(temp>0,[3,4,9,10])=NaN; for ai=1:length(VECTOR) if ~isnan(VECTOR(ai,3)) % ---------------------- congruent (1) or conflict (0) if VECTOR(ai,9)==1 && VECTOR(ai,3)==1, GRATTON=1; % cC elseif VECTOR(ai,9)==1 && VECTOR(ai,3)==0, GRATTON=2; % cI elseif VECTOR(ai,9)==0 && VECTOR(ai,3)==1, GRATTON=3; % iC elseif VECTOR(ai,9)==0 && VECTOR(ai,3)==0, GRATTON=4; % iI end % ---------------------- [Right=1, Left=2] if VECTOR(ai,10)==1 && VECTOR(ai,4)==1, RESPS=1; % rR elseif VECTOR(ai,10)==2 && VECTOR(ai,4)==2, RESPS=1; % lL elseif VECTOR(ai,10)==1 && VECTOR(ai,4)==2, RESPS=10; % rL elseif VECTOR(ai,10)==2 && VECTOR(ai,4)==1, RESPS=10; % lR end % ---------------------- VECTOR(ai,14)=GRATTON; VECTOR(ai,15)=RESPS; VECTOR(ai,16)=GRATTON.*RESPS; clear GRATTON RESPS end end UNIQUES=unique(VECTOR(:,16)); UNIQUES=UNIQUES(UNIQUES>0); for ai=1:8 ERP(:,:,ai)=squeeze(mean(FILT.data(:,:,VECTOR(:,16)==UNIQUES(ai)),3)); end % ---------------- single trial corr for ai=1:8 for chani=1:60 rho=corr(squeeze(FILT.data(chani,:,VECTOR(:,16)==UNIQUES(ai)))', VECTOR(VECTOR(:,16)==UNIQUES(ai),5) ,'type','Spearman','rows','complete'); ERP_Corr(chani,:,ai)=rho; end end % ---------------- limit based on lowest epoch count for ai=1:8 TEMP_by_set{ai}=FILT.data(:,:,VECTOR(:,16)==UNIQUES(ai)); TEMP_by_set_SIZE(ai)=size(TEMP_by_set{ai},3); end TEMP_minsize=min(TEMP_by_set_SIZE); % ---- for ai=1:8 TEMP_TRIALS=1:TEMP_by_set_SIZE(ai); TEMP_TRIALS=shuffle(TEMP_TRIALS); ERP_minsize(:,:,ai)=squeeze(mean( TEMP_by_set{ai}(:,:,TEMP_TRIALS(1:TEMP_minsize)) ,3)); clear TEMP_TRIALS end clear TEMP_by_set TEMP_by_set_SIZE TEMP_minsize TEMP_by_set_SIZE; % ------------------ Now for response locked for respi=1:dims(3) RT=VECTOR(respi,5); if isnan(RT) response=zeros(dims(1),dims(2)) ; % repmat(NaN,dims(1),dims(2)); else FILT_response=cat(2,FILT.data(:, floor(RT/2):end ,respi), zeros(dims(1),floor(RT/2)-1) ); response=cat(2,FILT.data(:, floor(RT/2):end ,respi), zeros(dims(1),floor(RT/2)-1) ); end FILT.RESP(:,:,respi)=response; EEG.RESP(:,:,respi)=response; clear FILT_response response; end % Stuff L_C3=26; R_C4=30; for ai=1:length(VECTOR) if ~isnan(VECTOR(ai,4)) % ---------------------- [Right=1, Left=2] if VECTOR(ai,4)==1, active_site=L_C3; inactive_site=R_C4; % Right response, left cortex active elseif VECTOR(ai,4)==2, active_site=R_C4; inactive_site=L_C3; % Left response, right cortex active end % ---------------------- FILT.RESP_ActInact(:,ai)=FILT.RESP(active_site,:,ai)-FILT.RESP(inactive_site,:,ai); FILT.STIM_ActInact(:,ai)=FILT.data(active_site,:,ai)-FILT.data(inactive_site,:,ai); else FILT.RESP_ActInact(:,ai)=repmat(NaN,1,dims(2)); FILT.STIM_ActInact(:,ai)=repmat(NaN,1,dims(2)); end end % ERP-i-fy for ai=1:8 ERP_RESP(:,ai)=squeeze(mean(FILT.RESP_ActInact(:,VECTOR(:,16)==UNIQUES(ai)),2)); ERP_RESP_All(:,:,ai)=squeeze(mean(FILT.RESP(:,:,VECTOR(:,16)==UNIQUES(ai)),3)); ERP_LRP_to_STIM(:,ai)=squeeze(mean(FILT.STIM_ActInact(:,VECTOR(:,16)==UNIQUES(ai)),2)); end % ---------------- single trial corr for ai=1:8 rho=corr(squeeze(FILT.RESP_ActInact(:,VECTOR(:,16)==UNIQUES(ai)))', VECTOR(VECTOR(:,16)==UNIQUES(ai),5) ,'type','Spearman','rows','complete'); ERP_RESP_Corr(:,ai)=rho; end % ---------------- limit based on lowest epoch count for ai=1:8 TEMP_by_set{ai}=FILT.RESP_ActInact(:,VECTOR(:,16)==UNIQUES(ai)); TEMP_by_set_SIZE(ai)=size(TEMP_by_set{ai},2); end TEMP_minsize=min(TEMP_by_set_SIZE); % ---- for ai=1:8 TEMP_TRIALS=1:TEMP_by_set_SIZE(ai); TEMP_TRIALS=shuffle(TEMP_TRIALS); ERP_LRP_minsize(:,ai)=squeeze(mean( TEMP_by_set{ai}(:,TEMP_TRIALS(1:TEMP_minsize)) ,2)); clear TEMP_TRIALS end clear TEMP_by_set TEMP_by_set_SIZE TEMP_minsize TEMP_by_set_SIZE; % ^^^^^^^^^^^^^^^^ TF % 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 dims = size(EEG.data); n_wavelet = length(t); n_data = dims(2)*dims(3); n_convolution = n_wavelet+n_data-1; n_conv_pow2 = pow2(nextpow2(n_convolution)); half_of_wavelet_size = (n_wavelet-1)/2; % same times, new baseline - also resp is -1000 to 500 tx=-2000:2:1998; b1=find(tx==-300); b2=find(tx==-200); t1=find(tx==-500); t2=find(tx==1000); rt1=find(tx==-500); rt2=find(tx==1000); % OK, made this like cue to get lotsa post-response activity % get FFT of data TF_chani=19; EEG_fft = fft(reshape(EEG.data(TF_chani,:,:),1,n_data),n_conv_pow2); RESP_fft = fft(reshape(EEG.RESP(TF_chani,:,:),1,n_data),n_conv_pow2); for fi=1:num_freqs wavelet = fft( exp(2*1i*pi*frex(fi).*t) .* exp(-t.^2./(2*(s(fi)^2))) , n_conv_pow2 ); % sqrt(1/(s(fi)*sqrt(pi))) * % convolution EEG_conv = ifft(wavelet.*EEG_fft); EEG_conv = EEG_conv(1:n_convolution); EEG_conv = EEG_conv(half_of_wavelet_size+1:end-half_of_wavelet_size); EEG_conv = reshape(EEG_conv,dims(2),dims(3)); % convolution RESP_conv = ifft(wavelet.*RESP_fft); RESP_conv = RESP_conv(1:n_convolution); RESP_conv = RESP_conv(half_of_wavelet_size+1:end-half_of_wavelet_size); RESP_conv = reshape(RESP_conv,dims(2),dims(3)); % Get baseline BASE = mean(mean(abs(EEG_conv(b1:b2,:)).^2)); for ai=1:8 % Get power by condi temp_POWER1 = mean(abs(EEG_conv(t1:t2,VECTOR(:,16)==UNIQUES(ai))).^2,2); temp_POWER2 = mean(abs(RESP_conv(rt1:rt2,VECTOR(:,16)==UNIQUES(ai))).^2,2); % dB correct power by base (different time windows) POWER{1}(fi,:,ai) = 10*( log10(temp_POWER1) - log10(repmat(BASE,size(tx2disp,2),1)) ) ; POWER{2}(fi,:,ai) = 10*( log10(temp_POWER2) - log10(repmat(BASE,size(tx2disp,2),1)) ) ; % Get ITPC by condi (different time windows) ITPC{1}(fi,:,ai) = abs(mean(exp(1i*( angle(EEG_conv(t1:t2,VECTOR(:,16)==UNIQUES(ai))) )),2)); ITPC{2}(fi,:,ai) = abs(mean(exp(1i*( angle(RESP_conv(t1:t2,VECTOR(:,16)==UNIQUES(ai))) )),2)); clear temp_POWER1 temp_POWER2; end clear wavelet EEG_conv RESP_conv BASE; end % ^^^^^^^^^^^^^^^^ save([savedir,num2str(subno),'_ERPs_2018_Revision.mat'],'SUBJINFO','SUBJINFO_HDR','ERP','ERP_RESP','ERP_RESP_All','VECTOR','POWER','ITPC',... 'ERP_LRP_to_STIM','ERP_minsize','ERP_Corr','ERP_RESP_Corr','ERP_LRP_minsize'); end end clearvars -except FILZ xls_data xls_hdr si datalocation savedir; end %%