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Timelimit02_ERP_stats.m
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Timelimit02_ERP_stats.m
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%% Compute STATS on time-series within/between subject
%=========================================================================%
% AUTHOR: Bianca Trovo ([email protected])
% DATE: re_created on July 2019
% EXPERIMENT: Timelimit_2018
%{
SCOPE:
OUTPUT:
FIXME: **
%}
%=========================================================================%
%% START of the script
%% 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;
regression_folder2= [regression_folder, '/RegressionsContr']; % it can be also current_subj_folder
if ~exist(fullfile(regression_folder2)); mkdir(fullfile(regression_folder2)); end;
statistics_folder= [results_Path, '/Statistics']; % it can be also current_subj_folder
if ~exist(fullfile(statistics_folder)); mkdir(fullfile(statistics_folder)); end;
%% 0) Other tests tried
%% 1a) CORRELATION between RP amplitudes (Y) vs conditions (X) before log transformation (raw data)
% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% % 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);
% for subi=1:nSubjs;
%
% fname_ER= sprintf('subj%02d_TimeS_bytrial',subi);
% pickupER(subi) = load(fname_ER);
%
% end
%
% % save 'pickupER' pickupER;
%
% % 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
%
% % save 'pickupCond' pickupCond;
%
% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% % Compute correlations here:
%
% cd(regression_folder);
%
% for subi=1:nSubjs;
%
% % Just re-converting Inf values into an integer number
% pickupCond(subi).newcond(pickupCond(subi).newcond==Inf) = 32;
%
% X= [pickupCond(subi).newcond' ones(length(pickupCond(subi).newcond'),1)];
% Y= pickupER(subi).avg_trl.trial; %.avg or .trial?
% 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_ER1', 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(regression_folder);
%
% R_all= {};
% for subi=1:nSubjs;
%
% fname_Corr= sprintf('subj%02d_Corr_ER1',subi);
% pickupCorrs(subi) = load(fname_Corr);
%
% templateER.avg = squeeze(pickupCorrs(subi).R); %squeeze to collapse trials dimension
% R_all{subi}= templateER;
%
% end
%
%
% cd(regression_folder);
% save('R_all_ER1', 'R_all','templateER');
%
%
% 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);
%% 1b) CORRELATION between RP amplitudes (Y) vs log-transformed 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('pickupER');
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:
correlation_folder2= [correlation_folder, '/CorrelationsContr']; % it can be also current_subj_folder
if ~exist(fullfile(correlation_folder2)); mkdir(fullfile(correlation_folder2)); end;
cd(correlation_folder2);
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_CONTRcorr_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);
cd(contrTS_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_folder2);
R_all= {};
for subi=1:nSubjs;
fname_Corr= sprintf('subj%02d_CONTRcorr_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_folder2);
save('R_all_ER1Contr_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);
%% 2a) 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 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);
% 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);
% for subi=1:nSubjs;
%
% fname_BehavData= sprintf('subj%02d_WaitingTimes',subi);
% pickupBehav(subi) = load(fname_BehavData);
%
% end
%
% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% % Compute correlation here:
%
% cd(correlation_folder2);
%
% for subi=1:nSubjs;
%
% X= [pickupBehav(subi).RESPTIMES' ones(length(pickupBehav(subi).RESPTIMES'),1)];
% Y= pickupER(subi).avg_trl.trial; %.avg or .trial?
% 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_ER2', 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(data_Path);
%
% 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);
%
% R_all= {};
% for subi=1:nSubjs;
%
% fname_Corr= sprintf('subj%02d_Corr_ER2',subi);
% pickupCorrs(subi) = load(fname_Corr);
%
% templateER.avg = squeeze(pickupCorrs(subi).R); %squeeze to collapse trials dimension
% R_all{subi}= templateER;
%
% end
%
%
% cd(regression_folder);
% save('R_all_ER2', 'R_all','templateER');
%
%
% 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) CORRELATION between RP amplitudes (Y) vs log-transformed 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_folder2);
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_ER2Contr_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);
cd(contrTS_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_folder2);
R_all= {};
for subi=1:nSubjs;
fname_Corr= sprintf('subj%02d_Corr_ER2Contr_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_folder2);
save('R_all_ER2Contr_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);
%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%% 1a) REGRESSION between RP amplitudes (Y) vs conditions (X), 'raw'
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
%
% % 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('pickupER');
%
% % 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;
%
% % Just re-converting Inf values into an integer number
% pickupCond(subi).newcond(pickupCond(subi).newcond==Inf) = 32;
%
% X= [pickupCond(subi).RESPTIMES' ones(length(pickupCond(subi).RESPTIMES'),1)];
% Y= pickupER(subi).avg_trl.trial; %.avg or .trial?
% 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_ER1', 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_ER1',subi);
% pickupRegr(subi) = load(fname_Regr);
%
% templateER.avg = squeeze(pickupRegr(subi).B); %squeeze to collapse trials dimension
% B_all{subi}= templateER;
%
% end
%
% statsB_all= {};
% for subi=1:nSubjs;
%
% fname_Regr= sprintf('subj%02d_Regr_ER1',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_ER1', 'B_all', 'statsB_all','templateER','-v7.3');
%
%
% GAVG_B= ft_timelockgrandaverage([], B_all{1});
%
% % 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';
% figure
% ft_multiplotER(cfg,GAVG_B);
%% 1b) REGRESSION between RP amplitudes (Y) vs log-transformed 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) 'of Subject ' num2str(subi)]);
end
filename= [sprintf('subj%02d_Regr_ER1_Log-log', 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);
cd(contrTS_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_ER1_Log-log',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_ER1_Log-log', '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);
%% 2a) REGRESSION between RP amplitudes (Y) vs log-transformed 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).RESPTIMES' ones(length(pickupBehav(subi).RESPTIMES'),1)];
% Y= pickupER(subi).avg_trl.trial; %.avg or .trial?
% 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_ER2', 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(data_Path);
%
% 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);
%
% R_all= {};
% for subi=1:nSubjs;
%
% fname_Corr= sprintf('subj%02d_Regr_ER2',subi);
% pickupRegr(subi) = load(fname_Corr);
%
% templateER.avg = squeeze(pickupRegr(subi).R); %squeeze to collapse trials dimension
% B_all{subi}= templateER;
%
% end
%
%
% cd(regression_folder);
% save('B_all_ER2', 'B_all','templateER');
%
%
% GAVG_R= ft_timelockgrandaverage([], B_all{:});
% save 'GAVG_R_ER2' GAVG_R;
%
% % 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';
% figure
% ft_multiplotER(cfg,GAVG_R);
%% 2b) REGRESSION between RP amplitudes (Y) vs log-transformed 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: