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Timelimit17_Logtransform.m
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Timelimit17_Logtransform.m
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%% Logtransformation
%=========================================================================%
% AUTHOR: Bianca Trovo ([email protected])
% DATE: created on May 2019; modified in June 2019
% EXPERIMENT: Timelimit_2018
%{
SCOPE: compute the
OUTPUT: R{condi}or P{condi} in
FIXME:
%}
%=========================================================================%
%% Housekeeping
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% clear workspace (if needed)
if input('clear all? (1/0) ... ')
clearvars; close all;
end
% set paths (if needed)
BT_setpath
% choose subj & go to the right folder
BT_getsubj
clear LevelAnalysis name numlines prompt subj_folders
%% More specific paths (maybe set this in the start script)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
behavioral_folder= [results_Path, '/Behaviour']; % it can be also current_subj_folder
if ~exist(fullfile(behavioral_folder)); mkdir(fullfile(behavioral_folder)); end;
timeseries_folder= [results_Path, '/Timeseries']; % it can be also current_subj_folder
if ~exist(fullfile(timeseries_folder)); mkdir(fullfile(timeseries_folder)); end;
cd(timeseries_folder);
powerspectra_folder= [results_Path, '/Powerspect']; % it can be also current_subj_folder
if ~exist(fullfile(powerspectra_folder)); mkdir(fullfile(powerspectra_folder)); end;
cd(powerspectra_folder);
correlation_folder= [results_Path, '/Correlations']; % it can be also current_subj_folder
if ~exist(fullfile(correlation_folder)); mkdir(fullfile(correlation_folder)); end;
regression_folder= [results_Path, '/Regressions']; % it can be also current_subj_folder
if ~exist(fullfile(regression_folder)); mkdir(fullfile(regression_folder)); end;
statistics_folder= [results_Path, '/Statistics']; % it can be also current_subj_folder
if ~exist(fullfile(statistics_folder)); mkdir(fullfile(statistics_folder)); end;
%% 1a) Behavioral: CORRELATION between response times (Y) vs conditions (X)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Load in useful data:
% Y= Response times (alias Waiting Times): containing alredy log
% transformed data
load('pickupBehav');
% X= Conditions (alias Time limits): do Log transformation in following
% loop
for subi=1:nSubjs;
cd(powerspectra_folder);
fname_Cond= sprintf('subj%02d_usefulinfo',subi);
pickupCond(subi) = load(fname_Cond);
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Compute correlation here:
cd(correlation_folder);
R= zeros(nSubjs,1); P= zeros(nSubjs,1);
for subi=1:nSubjs;
% Just re-converting Inf values into an integer number
pickupCond(subi).newcond(pickupCond(subi).newcond==Inf) = 32;
pickupCond(subi).Logcond= log(pickupCond(subi).newcond);
X= [pickupCond(subi).Logcond' ones(length(pickupCond(subi).Logcond'),1)];
Y= pickupBehav(subi).RESPTIMES'; % LogRESPS
if isequal(length(Y(:,1)),length(X(:,1)))==1; disp('X & Y have correct dimensions'); else disp('X & Y DO NOT have compatible dimensions'); end;
% profile on
R(:)= 0; P(:)=0;
% [B(:,i,k,j),BINT,R] = regress(Y(:,i,k,j),X);
[tmpR,tmpP] = corrcoef(Y(:),X(:,1));
R = tmpR(1,2); P= tmpP(1,2);
filename= [sprintf('subj%02d_Corr_Bhv_semiLog', subi)]; % add one if all trials mixed by condition
save(filename,'R','P','-v7.3');
% profile off;
% profile viewer;
disp(['Subject ' num2str(subi) ' done']);
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Reload obtained correlations values in a matrix:
for subi=1:nSubjs;
fname_Corr= sprintf('subj%02d_Corr_Bhv_semiLog',subi);
pickupCorrs(subi) = load(fname_Corr);
end
save 'pickupCorrs_Behav_semiLog' pickupCorrs;
% Plot correlations:
% a=2; r=2;n=5;
% s = a*r.^(0:n-1);
% set(gca,'xtick',s, 'xticklabel',{'2s','4s','8s','16s','Inf'});
% xlabel('Conditions (sec)');
% ylabel('Waiting Times (sec)');
% plot(s,pickupCorrs(:).R);
%
% for subi= 1:nSubjs
%
% plot(pickupBehav(1).RESPTIMES,
%
% end
%% 1a) Behavioral: REGRESSION between response times (Y) vs conditions (X)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Load in useful data:
% Y= Response times (alias Waiting Times): containing alredy log
% transformed data
load('pickupBehav');
% X= Conditions (alias Time limits): do Log transformation in following
% loop
for subi=1:nSubjs;
cd(powerspectra_folder);
fname_Cond= sprintf('subj%02d_usefulinfo',subi);
pickupCond(subi) = load(fname_Cond);
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Compute regression here:
cd(regression_folder);
B= zeros(nSubjs,1); R= zeros(nSubjs,1); stats= zeros(nSubjs,1);
for subi=1:nSubjs;
% Just re-converting Inf values into an integer number
pickupCond(subi).newcond(pickupCond(subi).newcond==Inf) = 32;
pickupCond(subi).Logcond= log(pickupCond(subi).newcond);
X= [pickupCond(subi).Logcond' ones(length(pickupCond(subi).Logcond'),1)];
Y= pickupBehav(subi).RESPTIMES'; % LogRESPS
if isequal(length(Y(:,1)),length(X(:,1)))==1; disp('X & Y have correct dimensions'); else disp('X & Y DO NOT have compatible dimensions'); end;
% profile on
%
% B(:)= 0; R(:)=0; stats(:)=0;
[B,~,R,~,stats]= regress(Y,X);
% [tmpR,tmpP] = corrcoef(Y(:),X(:,1));
% B= tmpB(1,2); R{:}= tmpR(1,:);
filename= [sprintf('subj%02d_Regr_Bhv_semiLog', subi)]; % add one if all trials mixed by condition
save(filename,'B','R', 'stats','-v7.3');
% profile off;
% profile viewer;
disp(['Subject ' num2str(subi) ' done']);
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Reload obtained regressions values in a matrix:
for subi=1:nSubjs;
fname_Regr= sprintf('subj%02d_Regr_Bhv_semiLog',subi);
pickupRegr(subi) = load(fname_Regr);
end
save 'pickupRegr_Behav_semiLog' 'pickupRegr';
% Plot regressions:
% ~~~~~
%% PREPARE FOR TIME-SERIES
cd(timeseries_folder);
load('pickupER');
for subi=1:nSubjs;
fname_ER= sprintf('subj%02d_TimeS_bytrial',subi);
pickupER(subi) = load(fname_ER);
end
nChans= 60;
nTrls= length)
(:,1)); % you need other lines of code (see below)
pickup_minER= [];
pickup_posER= [];
pickup_LogER= [];
% Aaron's way
for subi=1:nSubjs;
pickup_minER(subi)= min(pickupER(subi).avg_trl.trial(:));
pickup_minER= pickup_minER';
pickup_posER(subi).avg_trl.trial(:)= [pickupER(subi).avg_trl.trial(:) - pickup_minER(subi)];
% pickup_LogER(subi).avg_trl.trial(:)= log(pickup_posER(subi).avg_trl.trial(:));
end
% my way
for k= 1:length(pickupER(subi).avg_trl.trial(:,1))
for i=1:60
Min_ER{subi}(k,i)= min(pickupER(subi).avg_trl.trial(k,i,:));
pickup_posER(subi).avg_trl.trial(k,i,:)= pickupER(subi).avg_trl.trial(k,i,:)+ abs(Min_ER{subi}(k,i));
if isequal(length(pickup_posER(subi).avg_trl.trial(k,i,:)),length(pickupER(subi).avg_trl.trial(k,i,:)))==1; disp('X & Y have correct dimensions'); else disp('X & Y DO NOT have compatible dimensions'); end;
pickup_LogER(subi).avg_trl.trial(k,i,:)= log(pickup_posER(subi).avg_trl.trial(k,i,:));
end
end
% trying new way
for subi=1:nSubjs;
pickup_minER(subi)= min(pickupER(subi).avg_trl.trial(:));
pickup_minER= pickup_minER';
for k= 1:length(pickupER(subi).avg_trl.trial(:,1))
for i=1:60
pickup_posER(subi).avg_trl.trial(k,i,:)= pickupER(subi).avg_trl.trial(k,i,:)- pickup_minER(subi);
if isequal(length(pickup_posER(subi).avg_trl.trial(k,i,:)),length(pickupER(subi).avg_trl.trial(k,i,:)))==1; disp('X & Y have correct dimensions'); else disp('X & Y DO NOT have compatible dimensions'); end;
pickup_LogER(subi).avg_trl.trial(k,i,:)= log(pickup_posER(subi).avg_trl.trial(k,i,:));
disp(['Channel' num2str(i) 'done']);
end
disp(['Trial' num2str(k) 'done']);
end
disp(['Subject' num2str(subi) 'done']);
end
cd(timeseries_folder);
save('pickup_LogERtrl', 'pickup_LogER','-v7.3');
%% 2a) Time-series: CORRELATION between RP amplitudes (Y) vs conditions (X)
% Set parameters and right path:
nChans= 60;
nTimes= 2001; % corresponds to [-3 +1]s
R= zeros(nChans,nTimes); P= zeros(nChans,nTimes);
% clear data_Path;
% % set new data_Path
% data_Path= timeseries_folder; % need to fix it in the start up script
% Load in useful data
% Y= Time-locked amplitudes (alias RP avg)
cd(timeseries_folder);
load('pickup_LogER');
% for subi=1:nSubjs;
%
% fname_ER= sprintf('subj%02d_TimeS_bytrial',subi);
% pickupER(subi) = load(fname_ER);
%
% end
% save('pickupER','pickupER','-v7.3'); % For variables larger than 2GB use MAT-file version 7.3 or later.
% X= Conditions (alias Time limits)
cd(powerspectra_folder);
load('pickupCond');
% for subi=1:nSubjs;
%
% fname_Cond= sprintf('subj%02d_usefulinfo',subi);
% pickupCond(subi) = load(fname_Cond);
%
% end
% save 'pickupCond' pickupCond;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Compute correlations here:
cd(correlation_folder);
for subi=1:nSubjs;
% Just re-converting Inf values into an integer number
pickupCond(subi).newcond(pickupCond(subi).newcond==Inf) = 32;
pickupCond(subi).Logcond= log(pickupCond(subi).newcond);
X= [pickupCond(subi).Logcond' ones(length(pickupCond(subi).Logcond'),1)];
Y= pickupER(subi).avg_trl.trial; %.avg or .trial? /// pickup_LogER
if isequal(length(Y(:,1)),length(X(:,1)))==1; disp('X & Y have correct dimensions'); else disp('X & Y DO NOT have compatible dimensions'); end;
% profile on
R(:)= 0; P(:)=0;
for i=1:nChans
for j= 1:nTimes
% [B(:,i,k,j),BINT,R] = regress(Y(:,i,k,j),X);
[tmpR,tmpP] = corrcoef(Y(:,i,j),X(:,1));
R(i,j) = tmpR(1,2); P(i,j) = tmpP(1,2);
end
disp(['We are at channel' num2str(i)]);
end
filename= [sprintf('subj%02d_Corr_ER1Log_semiLogX', subi)]; % add one if all trials mixed by condition
save(filename,'R','P','-v7.3');
% profile off;
% profile viewer;
disp(['Subject ' num2str(subi) ' done']);
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Compute Grandaverages of correlations here:
cd(timeseries_folder);
load subj22_TimeS_one; % all conditions in one, trials averaged
templateER= avg_one; % fake Fieldtrip structure for inserting correlation values
% make a matrix with all the subjects
cd(correlation_folder);
R_all= {};
for subi=1:nSubjs;
fname_Corr= sprintf('subj%02d_Corr_ER1Log_semiLogX',subi);
pickupCorrs(subi) = load(fname_Corr);
templateER.avg = squeeze(pickupCorrs(subi).R); %squeeze to collapse trials dimension
R_all{subi}= templateER;
end
cd(correlation_folder);
save('R_all_ER1Log_semiLogX', 'R_all','templateER','-v7.3');
GAVG_R= ft_timelockgrandaverage([], R_all{:});
% Plot correlations:
cfg=[];
% cfg.preproc.lpfilter='yes';
% cfg.preproc.lpfreq= 2; % Filter away alpha frequencies (8-12Hz): put 7 or 1 hz.
cfg.layout = 'eeg_64_NM20884N.lay';
cfg.linewidth = 1.5;
cfg.showlabels= 'yes';
figure
ft_multiplotER(cfg,GAVG_R);
%% 2b) Time-series: CORRELATION between RP amplitudes (Y) vs response times (X)
% Set parameters and right path:
nChans= 60;
nTimes= 2001; % corresponds to [-3 +1]s
R= zeros(nChans,nTimes); P= zeros(nChans,nTimes);
% clear data_Path;
% % set new data_Path
% data_Path= timeseries_folder; % need to fix it in the start up script
% Load in useful data
% Y= Time-locked amplitudes (alias RP avg)
cd(timeseries_folder);
load('pickup_LogER');
% for subi=1:nSubjs;
%
% fname_ER= sprintf('subj%02d_TimeS_bytrial',subi);
% pickupER(subi) = load(fname_ER);
%
% end
% save('pickupER','pickupER','-v7.3'); % For variables larger than 2GB use MAT-file version 7.3 or later.
% X= Response times (alias Waiting Times)
cd(behavioral_folder);
load('pickupBehav');
% for subi=1:nSubjs;
%
% fname_BehavData= sprintf('subj%02d_WaitingTimes',subi);
% pickupBehav(subi) = load(fname_BehavData);
%
% end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Compute correlations here:
cd(correlation_folder);
for subi=1:nSubjs;
X= [pickupBehav(subi).LogRESPS' ones(length(pickupBehav(subi).LogRESPS'),1)];
Y= pickupER(subi).avg_trl.trial; %.avg or .trial? /// pickup_LogER
if isequal(length(Y(:,1)),length(X(:,1)))==1; disp('X & Y have correct dimensions'); else disp('X & Y DO NOT have compatible dimensions'); end;
% profile on
R(:)= 0; P(:)=0;
for i=1:nChans
for j= 1:nTimes
[tmpR,tmpP] = corrcoef(Y(:,i,j),X(:,1));
R(i,j) = tmpR(1,2); P(i,j) = tmpP(1,2);
end
disp(['We are at channel' num2str(i)]);
end
filename= [sprintf('subj%02d_Corr_ER2Log_semiLogX', subi)]; % add one if all trials mixed by condition
save(filename,'R','P','-v7.3');
% profile off;
% profile viewer;
disp(['Subject ' num2str(subi) ' done']);
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Compute Grandaverages of correlations here:
cd(timeseries_folder);
load subj22_TimeS_one; % all conditions in one, trials averaged
templateER= avg_one; % fake Fieldtrip structure for inserting correlation values
% make a matrix with all the subjects
cd(correlation_folder);
R_all= {};
for subi=1:nSubjs;
fname_Corr= sprintf('subj%02d_Corr_ER2Log_semiLogX',subi);
pickupCorrs(subi) = load(fname_Corr);
templateER.avg = squeeze(pickupCorrs(subi).R); %squeeze to collapse trials dimension
R_all{subi}= templateER;
end
cd(correlation_folder);
save('R_all_ER2Log_semiLogX', 'R_all','templateER','-v7.3');
GAVG_R= ft_timelockgrandaverage([], R_all{:});
% Plot correlations:
cfg=[];
% cfg.preproc.lpfilter='yes';
% cfg.preproc.lpfreq= 2; % Filter away alpha frequencies (8-12Hz): put 7 or 1 hz.
cfg.layout = 'eeg_64_NM20884N.lay';
cfg.linewidth = 1.5;
cfg.showlabels= 'yes';
figure
ft_multiplotER(cfg,GAVG_R);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% 2c) Time-series: REGRESSION between RP amplitudes (Y) vs conditions (X)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Set parameters and right path:
nChans= 60;
nTimes= 2001; % corresponds to [-3 +1]s
% B= zeros(2,nChans,nTimes); R= zeros(nChans,nTimes); stats= zeros(nChans,nTimes);
% clear data_Path;
% % set new data_Path
% data_Path= timeseries_folder; % need to fix it in the start up script
%
% Load in useful data
% Y= Time-locked amplitudes (alias RP avg)
cd(timeseries_folder);
load('pickup_LogER');
% for subi=1:nSubjs;
%
% fname_ER= sprintf('subj%02d_TimeS_bytrial',subi);
% pickupER(subi) = load(fname_ER);
%
% end
% save(filename,'pickupER','pickupER','-v7.3'); % For variables larger than 2GB use MAT-file version 7.3 or later.
%
% X= Conditions (alias Time limits)
cd(powerspectra_folder);
load('pickupCond');
% for subi=1:nSubjs;
%
% fname_Cond= sprintf('subj%02d_usefulinfo',subi);
% pickupCond(subi) = load(fname_Cond);
%
% end
% save 'pickupCond' pickupCond;
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Compute regressions here:
cd(regression_folder);
for subi=1:nSubjs;
disp(['We are at subject' num2str(subi)]);
% Just re-converting Inf values into an integer number
pickupCond(subi).newcond(pickupCond(subi).newcond==Inf) = 32;
pickupCond(subi).Logcond= log(pickupCond(subi).newcond);
X= [pickupCond(subi).Logcond' ones(length(pickupCond(subi).Logcond'),1)];
Y= pickupER(subi).avg_trl.trial; %.avg or .trial? //// pickup_LogER
if isequal(length(Y(:,1)),length(X(:,1)))==1; disp('X & Y have correct dimensions'); else disp('X & Y DO NOT have compatible dimensions'); end;
% profile on
clear B R stats
for i=1:nChans
for j= 1:nTimes
[B(:,i,j),~,R(:,i,j),~,stats(:,i,j)] = regress(Y(:,i,j),X); % Multiple linear regression
% LM(:,i,j)= fitlm(X(:,1),Y(:,i,j)) % linear regression
end
disp(['We are at channel' num2str(i)]);
end
filename= [sprintf('subj%02d_Regr_ER1Log_semiLogX', subi)]; % add one if all trials mixed by condition CHANGE TO Log
save(filename,'B','R','stats','-v7.3');
% profile off;
% profile viewer;
disp(['Subject ' num2str(subi) ' done']);
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Compute Grandaverages of regressions here:
cd(timeseries_folder);
load subj22_TimeS_one; % all conditions in one, trials averaged
templateER= avg_one; % fake Fieldtrip structure for inserting correlation values
% make a matrix with all the subjects
cd(regression_folder);
B_all= {};
for subi=1:nSubjs;
fname_Regr= sprintf('subj%02d_Regr_ER1Log_semiLogX',subi);
pickupRegr(subi) = load(fname_Regr);
templateER.avg = squeeze(pickupRegr(subi).B(1,:,:)); %squeeze to collapse trials dimension
B_all{subi}= templateER;
end
% statsB_all= {};
% for subi=1:nSubjs;
%
% fname_Regr= sprintf('subj%02d_Regr_ER1Log',subi);
% pickupRegr(subi) = load(fname_Regr);
%
% templateER.avg = squeeze(pickupRegr(subi).stats); %squeeze to collapse trials dimension
% statsB_all{subi}= templateER;
%
% end
cd(regression_folder);
save('B_all_ER1Log_semiLogX', 'B_all','templateER','-v7.3');
GAVG_B= ft_timelockgrandaverage([], B_all{:});
save('GAVG_B_ER1_semiLogX', 'GAVG_B','-v7.3');
% Plot regressions:
cfg=[];
% cfg.preproc.lpfilter='yes';
% cfg.preproc.lpfreq= 2; % Filter away alpha frequencies (8-12Hz): put 7 or 1 hz.
cfg.layout = 'eeg_64_NM20884N.lay';
cfg.linewidth = 1.5;
cfg.showlabels= 'yes';
cfg.comment= 'no'
cfg.highlight= 'numbers'; %'numbers' %''labels'
%cfg.highlightchannel= {'EEG030'};
% cfg.highlightsymbol= 'x'; %default = 'o')
% cfg.highlightcolor= highlight marker color (default = [0 0 0] (black))
% cfg.highlightsize= 48;
cfg.highlightfontsize= 48;
cfg.colorbar= 'yes';
cfg.style= 'straight'; %both' or %'fill'
cfg.gridscale= 400;
cfg.xlim= [-0.714 -0.306];
figure
ft_topoplotER(cfg,GAVG_B);
%% 2d) Time-series: REGRESSION between RP amplitudes (Y) vs response times (X)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Set parameters and right path:
nChans= 60;
nTimes= 2001; % corresponds to [-3 +1]s
%
% clear data_Path; %set right path
% data_Path= timeseries_folder; % need to fix it in the start up script
%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Load in useful data:
% Y= Time-locked amplitudes (alias RP avg)
cd(timeseries_folder);
load('pickupER');
% for subi=1:nSubjs;
%
% fname_ER= sprintf('subj%02d_TimeS_bytrial',subi);
% pickupER(subi) = load(fname_ER);
%
% end
% X= Response times (alias Waiting Times)
cd(behavioral_folder);
load('pickupBehav');
% for subi=1:nSubjs;
%
% fname_BehavData= sprintf('subj%02d_WaitingTimes',subi);
% pickupBehav(subi) = load(fname_BehavData);
%
% end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Compute regressions here:
cd(regression_folder);
for subi=1:nSubjs;
X= [pickupBehav(subi).LogRESPS' ones(length(pickupBehav(subi).LogRESPS'),1)];
Y= pickupER(subi).avg_trl.trial; %.avg or .trial? /// pickup_LogER
if isequal(length(Y(:,1)),length(X(:,1)))==1; disp('X & Y have correct dimensions'); else disp('X & Y DO NOT have compatible dimensions'); end;
% profile on
clear B R stats;
for i=1:nChans
for j= 1:nTimes
[B(:,i,j),~,R(:,i,j),~,stats(:,i,j)] = regress(Y(:,i,j),X);
end
disp(['We are at channel' num2str(i)]);
end
filename= [sprintf('subj%02d_Regr_ER2Log_semiLogX', subi)]; % add one if all trials mixed by condition
save(filename,'B','R','stats','-v7.3');
% profile off;
% profile viewer;
disp(['Subject ' num2str(subi) ' done']);
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Compute Grandaverages of regressions here:
cd(timeseries_folder);
load subj22_TimeS_one; % all conditions in one, trials averaged
templateER= avg_one; % fake Fieldtrip structure for inserting correlation values
% make a matrix with all the subjects
cd(regression_folder);
B_all= {};
for subi=1:nSubjs;
fname_Regr= sprintf('subj%02d_Regr_ER2Log_semiLogX',subi);
pickupRegr(subi) = load(fname_Regr);
templateER.avg = squeeze(pickupRegr(subi).B(1,:,:)); %squeeze to collapse trials dimension
B_all{subi}= templateER;
end
cd(regression_folder);
save('B_all_ER2Log_semiLogX', 'B_all','templateER');
GAVG_B= ft_timelockgrandaverage([], B_all{:});
save('GAVG_B_ER2_semiLogX', 'GAVG_B','-v7.3');
% Plot regressions:
cfg=[];
% cfg.preproc.lpfilter='yes';
% cfg.preproc.lpfreq= 2; % Filter away alpha frequencies (8-12Hz): put 7 or 1 hz.
cfg.layout = 'eeg_64_NM20884N.lay';
cfg.linewidth = 1.5;
cfg.commet= 'no';
cfg.showlabels= 'yes';
cfg.highlight= 'numbers'; %'numbers' %''labels'
cfg.highlightchannel= {'EEG030'};
% cfg.highlightsymbol= 'x'; %default = 'o')
% cfg.highlightcolor= highlight marker color (default = [0 0 0] (black))
% cfg.highlightsize= 48;
cfg.highlightfontsize= 48;
cfg.colorbar= 'yes';
cfg.style= 'straight'; %both' or %'fill'
cfg.gridscale= 400;
cfg.xlim= [-0.742 -0.26];
figure
ft_topoplotER(cfg,GAVG_B);
%% PREPARE FOR TIME-FREQUENCY
% cd(powerspectra_folder);
% load('pickupTFRtrl');
%
% nChans= 60; nFreqs= 40; nTimes= 61;
% nTrls= length(pickupTFR(subi).avg_trl.trial(:,1)); % you need other lines of code (see below)
%
% pickup_posTFR= [];
% pickup_LogTFR= [];
%
% for subi=1:nSubjs;
%
% for k= 1:length(pickupTFR(subi).avg_trl.trial(:,1))
%
% for i=1:60
%
% Min_TFR{subi}(k,i)= min(pickupTFR(subi).TFR_trl.powspctrm(k,i,:,:));
% pickup_posTFR(subi).TFR_trl.trial(k,i,:)= pickupTFR(subi).TFR_trl.trial(k,i,:)+ abs(Min_TFR{subi}(k,i));
% if isequal(length(pickup_posTFR(subi).avg_trl.trial(k,i,:)),length(pickupER(subi).avg_trl.trial(k,i,:)))==1; disp('X & Y have correct dimensions'); else disp('X & Y DO NOT have compatible dimensions'); end;
%
% pickup_LogTFR(subi).avg_trl.trial(k,i,:)= log(pickup_posTFR(subi).avg_trl.trial(k,i,:));
%
% end
%
% end
%
% end
%
% cd(timeseries_folder);
% save('pickup_LogER', 'pickup_LogER','-v7.3');
%% 3a) Time-frequency: correlation between power-spectra (Y) vs conditions (X)
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Set parameters and right path:
nChans= 60;
nFreqs= 40;
nTimes= 61;
R= zeros(nChans,nFreqs,nTimes); P= zeros(nChans,nFreqs,nTimes);
% data_Path= powerspectra_folder; % need to fix it in the start up script
% cd(data_Path);
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Load in useful data:
% Y= Time-frequencies (alias TFR)
cd(powerspectra_folder);
for subi=1:nSubjs;
fname_TFR= sprintf('subj%02d_TFR_bytrial',subi);
pickupTFR(subi) = load(fname_TFR);
end
% save pickupTFRtrl pickupTFR;
% X= Conditions (alias Time limits)
cd(powerspectra_folder);
for subi=1:nSubjs;
fname_Cond= sprintf('subj%02d_usefulinfo',subi);
pickupCond(subi) = load(fname_Cond);
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Compute correlation here:
cd(correlation_folder);
for subi=1:nSubjs;
% profile on
R(:)= 0; P(:)=0;
% Just re-converting Inf values into an integer number
pickupCond(subi).newcond(pickupCond(subi).newcond==Inf) = 32;
pickupCond(subi).Logcond= log(pickupCond(subi).newcond);
X= [pickupCond(subi).Logcond' ones(length(pickupCond(subi).Logcond'),1)];
Y= pickupTFR(subi).TFR_trl.powspctrm;
if isequal(length(Y(:,1)),length(X(:,1)))==1; disp('X & Y have correct dimensions'); else disp('X & Y DO NOT have compatible dimensions'); end;
for i=1:nChans
for k= 1:nFreqs
for j= 1:nTimes
% [B(:,i,k,j),BINT,R] = regress(Y(:,i,k,j),X);
[tmpR,tmpP] = corrcoef(Y(:,i,k,j),X(:,1));
R(i,k,j)= tmpR(1,2); P(i,k,j) = tmpP(1,2);
end
end
disp(['We are at channel' num2str(i)]);
% save stuff
filename= [sprintf('subj%02d_Corr_TFR1_semiLogX', subi)]; % add one if all trials mixed by condition
save(filename,'R','P','-v7.3');
end%
% profile off;
% profile viewer;
disp(['Subject ' num2str(subi) ' done']);
end
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Compute Grandaverages of correlations here:
cd(powerspectra_folder);
load subj22_TFR_oneCorrect; % all conditions in one, trials averaged
templateTFR= TFR_one; % fake Fieldtrip structure for inserting correlation values
% make a matrix with all the subjects
cd(correlation_folder);
R_all= {};
for subi=1:nSubjs;
fname_Corr= sprintf('subj%02d_Corr_TFR1_semiLogX',subi);
pickupCorrs(subi) = load(fname_Corr);
templateTFR.powspctrm = squeeze(pickupCorrs(subi).R);
R_all{subi}= templateTFR;
end
cd(correlation_folder);
save('R_all_TFR1_semiLogX', 'R_all','templateTFR');
GAVG_R= ft_freqgrandaverage([], R_all{:});
% Plot correlations:
cfg=[];
% cfg.baseline= [-2 -1.5];
% cfg.baselinetype= 'relative';
cfg.zlim= [-0.05 -0.01];
% cfg.xlim= [-2.3 -1.3];
cfg.xlim= [-1.3 -0.7];
% cfg.ylim= [11 31];
cfg.ylim= [7 15];
cfg.layout= 'eeg_64_NM20884N.lay';
cfg.linewidth = 2;
cfg.showlabels= 'yes';
figure
% ft_multiplotTFR(cfg,GAVG_R);
ft_topoplotTFR(cfg,GAVG_R);