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frp_sham_reading.m
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frp_sham_reading.m
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% fixations sham vs word
% eyeCA cleaned vs uncleaned data
% These FRPS have NOT been overlap corrected
clear all; close all
eeglab nogui % sets path defaults
%%%%%%%%%%%
% redo indivodual subject epoching etc? (time consuming)
do_processing = 1;
%%%%%%%%%%%
% use only file w reliable trigger
hasTriggerList =readtable('triggerSources.csv');
sublist = [19:59 101:166];
exclude = [20:26, 54, 73, 77]; % Subj to exclude because no eeg or no trigger etc. 54 i fixable - check message file 6
sublist = sublist(~ismember(sublist,exclude) & ismember(sublist,find(hasTriggerList.sdcard==1)));
dir_events = '~/Dropbox (Emotive Computing)/EyeMindLink/Processed/events/';
dir_pre = fullfile('..','..','Data','EEG_processed') ;
preprotype = 'no overlap correction';
dir_in = fullfile(dir_pre, 'EEG_processed');
dir_fif = fullfile(dir_pre, 'preprocessed_fif');
analysis = 'FRP_reading_sham_OPTICAT';
mkdir( dir_pre, analysis)
mkdir(fullfile(dir_pre, analysis, 'subavg'))
commands = {};
%%
if do_processing
for s = 1:length(sublist)
tic;
close all
pID = ['EML1_',sprintf('%03d',sublist(s))];
EEG = pop_loadset([pID '.set'], dir_in);
events = struct2table(EEG.event);
eye_events = events(contains(events.type, 'eye'),:);
%% info
% Read info txt to determine whether EEG+triggers are from SD card
% (default) or streamed (backup)
triginfo = readtxtfile(fullfile(dir_fif, [pID '-info.txt']));
% read events.csv for triggers and descriptions
logtrig = readtable(fullfile(dir_events,[pID '_events.csv']));
% copy the correct EEGsample column for use depending on triginfo
if contains(triginfo, 'LA0','IgnoreCase',false)
logtrig.eeg_use_sample = logtrig.eegSD_sample_est;
else
logtrig.eeg_use_sample = logtrig.eeg_sample_est;
end
%% make new event structure with fixations by reading vs sham
task_events = logtrig(:,{'EVENT','VAL','eeg_use_sample','duration_sec'});
task_events = renamevars(task_events,{'EVENT','eeg_use_sample','duration_sec'},{'type','latency','duration'});
task_events.duration = task_events.duration * EEG.srate; % into samples
task_events = task_events(task_events.VAL==7|task_events.VAL==20,:);
task_events.type(task_events.VAL==7) = {'reading'};
task_events.type(task_events.VAL==20) = {'sham'};
task_events.urevent=NaN(height(task_events),1);
task_events = removevars(task_events,'VAL');
for e = 1:height(task_events)
sel = find((eye_events.latency >= task_events.latency(e)) & (eye_events.latency < task_events.latency(e)+task_events.duration(e) ));
eye_events.type(sel) = strcat(eye_events.type(sel), '_WITHIN_', repmat(task_events.type(e),length(sel),1));
end
eye_events = sortrows([eye_events; task_events(:,{'type','latency','duration'})],'latency');
% remove NaN latency events
eye_events = eye_events(~isnan(eye_events.latency),:);
EEG.event = table2struct(eye_events);
EEG = eeg_checkset(EEG);
%% epoch into sham/reading trials
% Preprocess data
EEGp = pop_eegfiltnew(EEG,0.1,100);
EEGp = pop_resample(EEGp, 200);
EEGp = eeg_checkset(EEGp);
[EEG_epoched, epoch_ix] = pop_epoch(EEGp, {'fixation_either_eye_WITHIN_reading','fixation_either_eye_WITHIN_sham'},[-0.2,0.8]);
epoch_reading = pop_rmbase(pop_epoch(EEGp,{'fixation_either_eye_WITHIN_reading'},[-0.2 0.8]),[-100 0]);
epoch_sham = pop_rmbase(pop_epoch(EEGp,{'fixation_either_eye_WITHIN_sham'},[-0.2 0.8]),[-100 0]);
% subsample to equate n epochs
[n_epoch_bal, which_smaller]= min([length(epoch_sham.epoch), length(epoch_reading.epoch)]);
if which_smaller == 1
v = 1:length(epoch_reading.epoch);
epoch_subix = v(randperm(n_epoch_bal));
epoch_reading = pop_select(epoch_reading, 'trial',epoch_subix);
else
v = 1:length(epoch_sham.epoch);
epoch_subix = v(randperm(n_epoch_bal));
epoch_sham = pop_select(epoch_sham, 'trial',epoch_subix);
end
%% Plot epochs
% pop_erpimage(EEG_epoched,1)
subavg.reading = mean(epoch_reading.data,3);
subavg.sham = mean(epoch_sham.data,3);
subavg.time = epoch_sham.times;
subavg.channels = {epoch_sham.chanlocs.labels};
subavg.n_repeats = n_epoch_bal;
h2=figure('name','Fixation-related potentials by event type');clf
ax(1)=subplot(2,1,1);
plot(epoch_reading.times, mean(epoch_reading.data,3))
ylabel('Fixation-related ERP')
xlabel('Time after fixation [ms]')
title([pID ': Reading' ], 'Interpreter','none')
ax(2)=subplot(2,1,2);
plot(epoch_sham.times, mean(epoch_sham.data,3))
ylabel('Fixation-related ERP')
xlabel('Time after fixation [ms]')
title([pID ': Sham' ], 'Interpreter','none')
legend({EEG_epoched.chanlocs.labels})
set(h2,'Position',[100 100 1000 660])
linkaxes(ax)
saveas(h2, fullfile(dir_pre, analysis, [pID '_' analysis '.png']))
%% Save data
pop_saveset(EEG_epoched, 'filename',pID, 'filepath',fullfile(dir_pre, analysis), 'savemode','onefile')
save(fullfile(dir_pre, analysis,'subavg', [pID '.mat']), 'subavg');
% % EEGLAB STUDY commands
% command_i = {'index' s 'load' fullfile(fullfile(dir_pre, analysis, [pID '.set'])) 'subject' pID };
% commands{s} = command_i;
% [ALLEEG, EEG, CURRENTSET] = eeg_store(ALLEEG, EEG_epoched);
toc
end
end
% Compute group stats
subfiles = dir(fullfile(dir_pre, analysis, 'subavg'));
subfiles=subfiles(~ismember({subfiles.name},{'.','..'})); % damn u matlab
load(fullfile(subfiles(1).folder, subfiles(1).name) ); % read once to get dims for init
allavg = struct('reading',zeros([1 size(subavg.reading)]),...
'sham',zeros([1 size(subavg.sham)]),...
'time',subavg.time,...
'channels',[]);
for i = 1:length(subfiles)
load(fullfile(subfiles(i).folder, subfiles(i).name) );
allavg.reading(i,:,:) = subavg.reading;
allavg.sham(i,:,:) = subavg.sham;
allavg.time = subavg.time;
allavg.channels = subavg.channels;
allavg.reading_rms(i,:) = sqrt(nanmean(subavg.reading.^2,1));
allavg.sham_rms(i,:) = sqrt(nanmean(subavg.sham.^2,1));
end
group.time = allavg.time;
group.channels = allavg.channels;
nchan = length(group.channels);
group.reading.avg = squeeze(nanmean(allavg.reading,1));
group.reading.std = squeeze(nanstd(allavg.reading,0,1));
% bootstrap estimate of SE over subjects
for c=1:nchan
[~,group.reading.se(c,:),~] = fBootstrapMean(squeeze(allavg.reading(:,c,:))',500);
end
for c=1:nchan
[~,group.reading.se(c,:),~] = fBootstrapMean(squeeze(allavg.reading(:,c,:))',500);
end
group.sham.avg = squeeze(nanmean(allavg.sham,1));
group.sham.std = squeeze(nanstd(allavg.sham,0,1));
% bootstrap estimate of mean and SE of RMS
[rms,sdev,~]=fBootstrapRMS(allavg.reading_rms',500);
group.reading.avgrms = rms;
group.reading.SErms = sdev;
[rms,sdev,~]=fBootstrapRMS(allavg.sham_rms',500);
group.sham.avgrms = rms;
group.sham.SErms = sdev;
% plot
ax=[];
h=figure(44);clf
ax(1)=subplot(1,2,1);
p1=plot(group.time, group.reading.avg,'LineWidth',2);
title('Reading')
ax(2)=subplot(1,2,2);
p2=plot(group.time, group.sham.avg,'LineWidth',2);
linkaxes(ax)
title('Sham')
set(p1, {'color'}, num2cell(turbo(nchan),2));
set(p2, {'color'}, num2cell(turbo(nchan),2));
legend(group.channels)
sgtitle(['FRPs for N=' num2str(length(subfiles)) ' ' preprotype])
% plot selected channel on same plot
% % Colours
clrs=[229 46 76
255 158 0
55 154 106
128 20 37
158 95 0
27 81 55
0 61 76]/255;
ax=[];
h=figure(55);clf
for c=1:nchan
ax(c)=subplot(2,4,c);
pp1=plot(group.time, group.reading.avg(c,:),'Color',clrs(1,:),'LineWidth',2);
hold on
pp2=plot(group.time, group.sham.avg(c,:),'Color',clrs(2,:),'LineWidth',2);
linkaxes(ax)
title(group.channels{c})
legend({'reading','sham'})
end
sgtitle(['FRPs for N=' num2str(length(subfiles)) ' ' preprotype])
% plot RMS
h=figure(88);clf
plot(group.time, group.reading.avgrms,'Color',clrs(1,:),'LineWidth',2);
hold on
b=group.reading.SErms; a=group.reading.avgrms;
Y = [b+a;flipud(-b+a)];
abscissa = group.time';
X = [abscissa;flipud(abscissa)];
h = fill(X,Y,clrs(1,:),'edgecolor','none','facealpha',0.2); hold on;
b=group.sham.SErms; a=group.sham.avgrms;
Y = [b+a;flipud(-b+a)];
abscissa = group.time';
X = [abscissa;flipud(abscissa)];
h = fill(X,Y,clrs(2,:),'edgecolor','none','facealpha',0.2); hold on;
plot(allavg.time, group.sham.avgrms,'Color',clrs(2,:),'LineWidth',2);
set(gca,'children',flipud(get(gca,'children'))); % Send shaded areas in background
legend({'reading','sham'})
title(['RMS (power) of FRPs for N=' num2str(length(subfiles)) ' ' preprotype])
% %TODO group level analyses
% [STUDY, ALLEEG] = std_editset( [], [], 'name','EML',...
% 'task', analysis,...
% 'filename', [analysis '.study'],'filepath', fullfile(dir_pre,analysis),...
% 'commands', { ...
% { 'index' 1 'load' fullfile(filepath, 's02','syn02-s253-clean.set') 'subject' 'S02' 'condition' 'synonyms' }, ...
% { 'index' 2 'load' fullfile(filepath, 's05', 'syn05-s253-clean.set') 'subject' 'S05' 'condition' 'synonyms' }, ...
% { 'index' 3 'load' fullfile(filepath, 's07', 'syn07-s253-clean.set') 'subject' 'S07' 'condition' 'synonyms' }, ...
% { 'index' 4 'load' fullfile(filepath, 's08', 'syn08-s253-clean.set') 'subject' 'S08' 'condition' 'synonyms' }, ...
% { 'index' 5 'load' fullfile(filepath, 's10', 'syn10-s253-clean.set') 'subject' 'S10' 'condition' 'synonyms' }, ...
% { 'index' 6 'load' fullfile(filepath, 's02', 'syn02-s254-clean.set') 'subject' 'S02' 'condition' 'non-synonyms' }, ...
% { 'index' 7 'load' fullfile(filepath, 's05', 'syn05-s254-clean.set') 'subject' 'S05' 'condition' 'non-synonyms' }, ...
% { 'index' 8 'load' fullfile(filepath, 's07', 'syn07-s254-clean.set') 'subject' 'S07' 'condition' 'non-synonyms' }, ...
% { 'index' 9 'load' fullfile(filepath, 's08', 'syn08-s254-clean.set') 'subject' 'S08' 'condition' 'non-synonyms' }, ...
% { 'index' 10 'load' fullfile(filepath, 's10', 'syn10-s254-clean.set') 'subject' 'S10' 'condition' 'non-synonyms' }, ...
% { 'dipselect' 0.15 } });