eegmp_analysis/code/e2_taskPLS_memory_erf_trace.m

clear all; cla; clc;

currentFile = mfilename('fullpath');
[pathstr,~,~] = fileparts(currentFile);
cd(fullfile(pathstr,'..'))
rootpath = pwd;

pn.data_eeg = fullfile(rootpath, '..', 'eegmp_preproc', 'data', 'outputs', 'eeg');
pn.data_erp = fullfile(rootpath, 'data', 'erp');
pn.data_erf = fullfile(rootpath, 'data', 'erf');
pn.data = fullfile(rootpath, 'data', 'stats'); mkdir(pn.data);
pn.tools = fullfile(rootpath, 'tools');
    addpath(fullfile(rootpath, '..', 'eegmp_preproc', 'tools', 'fieldtrip')); ft_defaults
    addpath(genpath(fullfile(pn.tools, '[MEG]PLS', 'MEGPLS_PIPELINE_v2.02b')))
    addpath(fullfile(pn.tools, 'BrewerMap'));
    addpath(fullfile(pn.tools, 'shadedErrorBar'));
pn.figures = fullfile(rootpath, 'figures');

%% add seed for reproducibility

rng(0, 'twister');

%% load erp

for ind_id = 1:33
    id = sprintf('sub-%03d', ind_id); disp(id)
    load(fullfile(pn.data_erf, [id,'_erf.mat']));
    for ind_option = 1:numel(conds.subsequent_memory)
        if ind_id == 1
             erpgroup.subsequent_memory.(conds.subsequent_memory{ind_option}) = erf.subsequent_memory{ind_option};
             erpgroup.subsequent_memory.(conds.subsequent_memory{ind_option}) = ...
                 rmfield(erpgroup.subsequent_memory.(conds.subsequent_memory{ind_option}), {'powspctrm'});
             erpgroup.subsequent_memory.(conds.subsequent_memory{ind_option}).dimord = 'sub_chan_freq_time';
             freq = erpgroup.subsequent_memory.(conds.subsequent_memory{ind_option}).freq;
        end
        idx_freq_t = freq >2 & freq <7;
        thetagroup.subsequent_memory.(conds.subsequent_memory{ind_option}).avg(ind_id,:,:,:) = squeeze(nanmean(erf.subsequent_memory{ind_option}.powspctrm(:,idx_freq_t,:),2));
        idx_freq_a = freq >8 & freq <20;
        alphagroup.subsequent_memory.(conds.subsequent_memory{ind_option}).avg(ind_id,:,:,:) = squeeze(nanmean(erf.subsequent_memory{ind_option}.powspctrm(:,idx_freq_a,:),2));
    end
end

time = erpgroup.subsequent_memory.subsequent_remembered.time;
channels = erpgroup.subsequent_memory.subsequent_forgotten.label;

%% get max. channels

load(fullfile(pn.data, 'e2_taskpls_erf.mat'),...
    'stat', 'result', 'lvdat', 'lv_evt_list', 'num_chans', 'num_freqs', 'num_time')

plotData.powspctrm = squeeze(nanmean(nanmean(stat.mask(:,stat.freq>8 & stat.freq<20,stat.time>.8 & stat.time<1).*...
    stat.prob(:,stat.freq>8 & stat.freq<20,stat.time>.8 & stat.time<1),3),2)); 
[~, alphaChanMin] = sort(plotData.powspctrm, 'ascend');

plotData.powspctrm = squeeze(nanmean(nanmean(stat.mask(:,stat.freq>2 & stat.freq<7,stat.time>0 & stat.time<1).*...
    stat.prob(:,stat.freq>2 & stat.freq<7,stat.time>0 & stat.time<1),3),2)); 

[~, thetaChan] = sort(plotData.powspctrm, 'descend');

%% visualize for frontal theta

mergeddata = cat(4, thetagroup.subsequent_memory.subsequent_remembered.avg, thetagroup.subsequent_memory.subsequent_forgotten.avg);

%figure; imagesc(squeeze(nanmean(thetagroup.old.new.avg-thetagroup.old.old.avg,1)))

idx_chans = thetaChan(1); channels(idx_chans) %[33];
condAvg = squeeze(nanmean(nanmean(mergeddata(:,idx_chans,:,1:2),2),4));

h = figure('units','centimeters','position',[0 0 10 8]);
cla; hold on;
% new value = old value ? subject average + grand average
curData = squeeze(nanmean(mergeddata(:,idx_chans,:,1),2));
curData = curData-condAvg+repmat(nanmean(condAvg,1),size(condAvg,1),1);
standError = nanstd(curData,1)./sqrt(size(curData,1));
l1 = shadedErrorBar(time,nanmean(curData,1),standError, 'lineprops', ...
    {'color', 'k','linewidth', 2}, 'patchSaturation', .1);
curData = squeeze(nanmean(mergeddata(:,idx_chans,:,2),2));
curData = curData-condAvg+repmat(nanmean(condAvg,1),size(condAvg,1),1);
standError = nanstd(curData,1)./sqrt(size(curData,1));
l2 = shadedErrorBar(time,nanmean(curData,1),standError, 'lineprops', ...
    {'color', 'r','linewidth', 2}, 'patchSaturation', .1);
xlabel('Time (s) from stim onset')
legend([l1.mainLine, l2.mainLine],{'remembered', 'forgotten'}, ...
    'location', 'southeast'); legend('boxoff')
xlim([-.5 1.7]); %ylim(YLim)
ylabel({'theta power';'(log10)'});
xlabel({'Time (s)'});    
set(findall(gcf,'-property','FontSize'),'FontSize',12)

figureName = ['e2_memory_theta'];
saveas(h, fullfile(pn.figures, figureName), 'epsc');
saveas(h, fullfile(pn.figures, figureName), 'png');

%% visualize for posterior alpha

mergeddata = cat(4, alphagroup.subsequent_memory.subsequent_remembered.avg, alphagroup.subsequent_memory.subsequent_forgotten.avg);

idx_chans = alphaChanMin(1:6); channels(idx_chans) %[33,38,47];
condAvg = squeeze(nanmean(nanmean(mergeddata(:,idx_chans,:,1:2),2),4));

h = figure('units','centimeters','position',[0 0 10 8]);
cla; hold on;
% new value = old value ? subject average + grand average
curData = squeeze(nanmean(mergeddata(:,idx_chans,:,1),2));
curData = curData-condAvg+repmat(nanmean(condAvg,1),size(condAvg,1),1);
standError = nanstd(curData,1)./sqrt(size(curData,1));
l1 = shadedErrorBar(time,nanmean(curData,1),standError, 'lineprops', ...
    {'color', 'k','linewidth', 3}, 'patchSaturation', .1);
curData = squeeze(nanmean(mergeddata(:,idx_chans,:,2),2));
curData = curData-condAvg+repmat(nanmean(condAvg,1),size(condAvg,1),1);
standError = nanstd(curData,1)./sqrt(size(curData,1));
l2 = shadedErrorBar(time,nanmean(curData,1),standError, 'lineprops', ...
    {'color', 'r','linewidth', 3}, 'patchSaturation', .1);
xlabel('Time (s) from stim onset')
legend([l1.mainLine, l2.mainLine],{'remembered', 'forgotten'}, ...
    'location', 'northeast'); legend('boxoff')
xlim([-.5 1.75]); ylim([-5.325 -4.775])
ylabel({'alpha power';'(log10)'});
xlabel({'Time (s)'});    
set(findall(gcf,'-property','FontSize'),'FontSize',15)

figureName = ['e2_memory_alpha'];
saveas(h, fullfile(pn.figures, figureName), 'epsc');
saveas(h, fullfile(pn.figures, figureName), 'png');