%% Step 2 Oddball clear all; clc addpath('Z:\EXPERIMENTS\mTBICoBRE\EEG\'); savedir='Z:\EXPERIMENTS\mTBICoBRE\EEG\3AOB Processed\'; load('Z:\EXPERIMENTS\mTBICoBRE\EEG\BV_Chanlocs_60.mat'); % ########## For Cavanagh data datadir='Z:\EXPERIMENTS\mTBICoBRE\EEG\3AOB Preproc\'; [D_DAT,D_HDR,D_ALL]=xlsread('Z:\EXPERIMENTS\mTBICoBRE\ANALYSIS\QUALITY_CHECK.xlsx','ODDBALL_ICAs'); FILEENDER='_3AOB.mat'; % % % ########## For Quinn data % % datadir='Z:\EXPERIMENTS\mTBICoBRE\EEG\QUINN 3AOB Preproc\'; % % [D_DAT,D_HDR,D_ALL]=xlsread('Z:\EXPERIMENTS\mTBICoBRE\ANALYSIS\QUINN_QUALITY_CHECK.xlsx','ODDBALL_ICAs'); % % FILEENDER='_QUINN_3AOB.mat'; cd(datadir); % ############# Set Params srate=500; tx=-2000:1000/srate:1998; B1=find(tx==-300); B2=find(tx==-200); T1=find(tx==-500); T2=find(tx==1000); tx2disp=-500:2:1000; % ############# for si=1:length(D_DAT) for sess=1:size(D_DAT,2)-1 % should be '2' for Quinn data, '3' for Cavanagh data subno=D_DAT(si,1); skip=0; INFO=D_ALL{si+1,sess+1}; % +1's b/c of subno column and header row disp(['TRYOUT ',num2str(subno),' S',num2str(sess)]); if isnumeric(INFO), bad_ICAs_To_Remove=INFO; end if isnan(INFO), skip=1; end % not done yet if strmatch('BAD',INFO), skip=1; end % Bad data if ~isnumeric(INFO), bad_ICAs_To_Remove=str2num(INFO); end % Don't repeat if already done if exist([savedir,num2str(subno),'_',num2str(sess),'_3AOB_TFandERPs_L.mat'])==2, skip=1; end if skip==0 load([num2str(subno),'_',num2str(sess),FILEENDER]); disp(['DOING: ',num2str(subno),'_',num2str(sess),'_3AOB.mat']); % Remove the bad ICAs: disp(['BAD ICAS: ', num2str(bad_ICAs_To_Remove)]); EEG = pop_subcomp( EEG, bad_ICAs_To_Remove, 0); % Get the good info out of the epochs for ai=1:size(EEG.epoch,2) % Initialize EEG.epoch(ai).EEG=NaN; for bi=1:size(EEG.epoch(ai).eventlatency,2) % Get STIMTYPE if EEG.epoch(ai).eventlatency{bi}==0 && isempty(strmatch(EEG.epoch(ai).eventtype{bi},'N999')); % If this bi is the event % Get StimType FullName=EEG.epoch(ai).eventtype{bi}; EEG.epoch(ai).EEG=str2num(FullName(2:end)) ; clear FullName VECTOR(ai,1)=EEG.epoch(ai).EEG; All_STIM={'S201','S200','S202'}; % Std, Target, Novel end end end % Only as many STD as NOV N_n=sum(VECTOR(:,1)==202); temp_idxs=find(VECTOR(:,1)==201); temp_idxs=shuffle(temp_idxs); VECTOR(temp_idxs(N_n+1:end),1)=999; clear temp_idxs; % Save trial counts TRL_ct(1)=sum(VECTOR(:,1)==201); TRL_ct(2)=sum(VECTOR(:,1)==200); TRL_ct(3)=sum(VECTOR(:,1)==202); %% % $$$$$$$$$$$$$$$$$$$$$$$ $$$$$$$$$$$$$$$$$$$$$$$ % $$$$$$$$$$$$$$$$$$$$$$$ Time-Freq % $$$$$$$$$$$$$$$$$$$$$$$ $$$$$$$$$$$$$$$$$$$$$$$ % Setup Wavelet Params num_freqs=50; frex=logspace(.01,1.7,num_freqs); s=logspace(log10(3),log10(10),num_freqs)./(2*pi*frex); t=-2:1/EEG.srate:2; % Definte Convolution Parameters dims = size(EEG.data); n_wavelet = length(t); n_data = dims(2)*dims(3); n_convolution = n_wavelet+n_data-1; n_conv_pow2 = pow2(nextpow2(n_convolution)); half_of_wavelet_size = (n_wavelet-1)/2; % For Laplacian X = [BV_Chanlocs_60.X]; Y = [BV_Chanlocs_60.Y]; Z = [BV_Chanlocs_60.Z]; % Pick channel chans=[36,33,56]; % FCz, F5, F6 for REFi=1:2 if REFi==1, TAG='V'; elseif REFi==2, TAG='L'; [EEG.data,~,~] = laplacian_perrinX(EEG.data,X,Y,Z,[],1e-6); end % Get FFT of data for chani=1:3 EEG_fft(chani,:) = fft(reshape(EEG.data(chans(chani),:,:),1,n_data),n_conv_pow2); end for fi=1:num_freqs wavelet = fft( exp(2*1i*pi*frex(fi).*t) .* exp(-t.^2./(2*(s(fi)^2))) , n_conv_pow2 ); % sqrt(1/(s(fi)*sqrt(pi))) * % convolution for chani=1:3 temp_conv = ifft(wavelet.*EEG_fft(chani,:)); temp_conv = temp_conv(1:n_convolution); temp_conv = temp_conv(half_of_wavelet_size+1:end-half_of_wavelet_size); EEG_conv(chani,:,:) = reshape(temp_conv,dims(2),dims(3)); clear temp_conv; % Common pre-EEG baseline temp_BASE(chani,:) = mean(mean(abs(EEG_conv(chani,B1:B2,:)).^2,2),3); end for idx=1:3 if idx==1, idx_V=VECTOR(:,1)==201; % STD elseif idx==2, idx_V=VECTOR(:,1)==200; % TARG elseif idx==3, idx_V=VECTOR(:,1)==202; % NOV end for chani=1:3 temp_PWR = squeeze(mean(abs(EEG_conv(chani,T1:T2,idx_V)).^2,3)); POWER(chani,fi,:,idx) = 10* ( log10(temp_PWR') - log10(repmat(temp_BASE(chani,:),size(tx2disp,2),1)) ); ITPC(chani,fi,:,idx) = abs(mean(exp(1i*( angle(EEG_conv(chani,T1:T2,idx_V)) )),3)); if chani==1, seed=1; targ=2; elseif chani==2, seed=1; targ=3; elseif chani==3, seed=2; targ=3; end ISPC(chani,fi,:,idx) = abs(mean(exp(1i*( angle(EEG_conv(seed,T1:T2,idx_V)) - angle(EEG_conv(targ,T1:T2,idx_V)) )),3)); clear temp_PWR; end clear idx_V ; end clear wavelet idx_V temp_BASE EEG_conv; end %% % $$$$$$$$$$$$$$$$$$$$$$$ $$$$$$$$$$$$$$$$$$$$$$$ % $$$$$$$$$$$$$$$$$$$$$$$ Theta Topo % $$$$$$$$$$$$$$$$$$$$$$$ $$$$$$$$$$$$$$$$$$$$$$$ topofrex=4.5; s=logspace(log10(3),log10(10),num_freqs)./(2*pi*topofrex); wavelet = fft( exp(2*1i*pi*frex(fi).*t) .* exp(-t.^2./(2*(s(fi)^2))) , n_conv_pow2 ); % sqrt(1/(s(fi)*sqrt(pi))) * seed=36; EEG_fft_4TOPO = fft(reshape(EEG.data(seed,:,:),1,n_data),n_conv_pow2); seed_EEG_conv_4TOPO = ifft(wavelet.*EEG_fft_4TOPO); seed_EEG_conv_4TOPO = seed_EEG_conv_4TOPO(1:n_convolution); seed_EEG_conv_4TOPO = seed_EEG_conv_4TOPO(half_of_wavelet_size+1:end-half_of_wavelet_size); seed_EEG_conv_4TOPO = reshape(seed_EEG_conv_4TOPO,dims(2),dims(3)); clear EEG_fft_4TOPO ; % Common pre-EEG SEED baseline seed_BASE = mean(mean(abs(seed_EEG_conv_4TOPO(B1:B2,:)).^2,1),2); for chani=1:60 EEG_fft_4TOPO = fft(reshape(EEG.data(chani,:,:),1,n_data),n_conv_pow2); EEG_conv_4TOPO = ifft(wavelet.*EEG_fft_4TOPO); EEG_conv_4TOPO = EEG_conv_4TOPO(1:n_convolution); EEG_conv_4TOPO = EEG_conv_4TOPO(half_of_wavelet_size+1:end-half_of_wavelet_size); EEG_conv_4TOPO = reshape(EEG_conv_4TOPO,dims(2),dims(3)); % Common pre-EEG baseline temp_BASE = mean(mean(abs(EEG_conv_4TOPO(B1:B2,:)).^2,1),2); for idx=1:3 if idx==1, idx_V=VECTOR(:,1)==201; % STD elseif idx==2, idx_V=VECTOR(:,1)==200; % TARG elseif idx==3, idx_V=VECTOR(:,1)==202; % NOV end temp_PWR = squeeze(mean(abs(EEG_conv_4TOPO(T1:T2,idx_V)).^2,2)); POWER_TOPO(chani,:,idx) = 10* ( log10(temp_PWR) - log10(repmat(temp_BASE,size(tx2disp,2),1)) ); S4cor=10* ( log10(abs(seed_EEG_conv_4TOPO(T1:T2,idx_V)).^2) - log10(repmat(seed_BASE,size(tx2disp,2),sum(idx_V))) ); T4cor=10* ( log10(abs(EEG_conv_4TOPO(T1:T2,idx_V)).^2) - log10(repmat(temp_BASE,size(tx2disp,2),sum(idx_V))) ); CORREL_TOPO(chani,:,idx)= diag(corr(S4cor',T4cor','type','Spearman')); SYNCH_TOPO(chani,:,idx) = abs(mean(exp(1i*( angle(seed_EEG_conv_4TOPO(T1:T2,idx_V)) - angle(EEG_conv_4TOPO(T1:T2,idx_V)) )),2)); clear idx_V temp_PWR S4cor T4cor; end clear EEG_fft_4TOPO EEG_conv_4TOPO TOPO_conv temp_BASE; end %% % $$$$$$$$$$$$$$$$$$$$$$$ $$$$$$$$$$$$$$$$$$$$$$$ % $$$$$$$$$$$$$$$$$$$$$$$ ERPs % $$$$$$$$$$$$$$$$$$$$$$$ $$$$$$$$$$$$$$$$$$$$$$$ % Filter dims=size(EEG.data); EEG.data=eegfilt(EEG.data,500,[],20); EEG.data=eegfiltfft(EEG.data,500,.1,[]); EEG.data=reshape(EEG.data,dims(1),dims(2),dims(3)); % Basecor your ERPs here so they are pretty. EEG_BASE=squeeze( mean(EEG.data(:,find(tx==-200):find(tx==0),:),2) ); for ai=1:dims(1) EEG.data(ai,:,:)=squeeze(EEG.data(ai,:,:))-repmat( EEG_BASE(ai,:),dims(2),1 ); end % Get ERPs for idx=1:3 if idx==1, idx_V=VECTOR(:,1)==201; % STD elseif idx==2, idx_V=VECTOR(:,1)==200; % TARG elseif idx==3, idx_V=VECTOR(:,1)==202; % NOV end ERP(:,:,idx)=squeeze(mean(EEG.data(:,find(tx==-500):find(tx==1000),idx_V),3)); clear DATA_erp idx_V ; end save([savedir,num2str(subno),'_',num2str(sess),'_3AOB_TFandERPs_',TAG,'.mat'],'ERP','ISPC','ITPC','POWER','VECTOR','SYNCH_TOPO','TRL_ct','POWER_TOPO','CORREL_TOPO'); clear ERP ISPC ITPC POWER RT; end clearvars -except datadir savedir FILEENDER BV_Chanlocs_60 D_DAT D_HDR D_ALL tx B1 B2 T1 T2 tx2disp si sess end end end %%