12345678910111213141516171819202122232425262728293031323334353637383940414243444546474849505152535455565758596061626364656667 |
- 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.tools = fullfile(rootpath, 'tools');
- addpath(fullfile(rootpath, '..', 'eegmp_preproc', 'tools', 'fieldtrip')); ft_defaults
- addpath(fullfile(pn.tools, 'BrewerMap'));
- addpath(fullfile(pn.tools, 'shadedErrorBar'));
-
- %% load erp
- for ind_id = 1:33
- id = sprintf('sub-%03d', ind_id); disp(id)
- load(fullfile(pn.data_erp, [id,'_erp_bl.mat']));
- for ind_option = 2:3
- if ind_id == 1
- erpgroup.subsequent_memory.(conds.subsequent_memory{ind_option}) = erp_bl.subsequent_memory{ind_option};
- erpgroup.subsequent_memory.(conds.subsequent_memory{ind_option}) = ...
- rmfield(erpgroup.subsequent_memory.(conds.subsequent_memory{ind_option}), {'avg', 'var', 'dof'});
- erpgroup.subsequent_memory.(conds.subsequent_memory{ind_option}).dimord = 'sub_chan_time';
- end
- erpgroup.subsequent_memory.(conds.subsequent_memory{ind_option}).avg(ind_id,:,:) = erp_bl.subsequent_memory{ind_option}.avg;
- end
- end
- time = erpgroup.subsequent_memory.subsequent_remembered.time;
- elec = erpgroup.subsequent_memory.subsequent_remembered.elec;
- channels = erpgroup.subsequent_memory.subsequent_remembered.label;
- mergeddata = cat(4, erpgroup.subsequent_memory.subsequent_forgotten.avg, ...
- erpgroup.subsequent_memory.subsequent_remembered.avg);
- smoothdur = 10; % 5 = 10 ms
- %% visualize differences
- idx_chans = [44,14,9,15];
- % avg across channels and conditions
- 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,smoothts(nanmean(curData,1),'b',smoothdur),smoothts(standError,'b',smoothdur),...
- '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,smoothts(nanmean(curData,1),'b',smoothdur),smoothts(standError,'b',smoothdur),...
- 'lineprops', {'color', 'r','linewidth', 2}, 'patchSaturation', .1);
- % ax = gca; ax.YDir = 'reverse';
- legend([l1.mainLine, l2.mainLine],{'forgotten', 'remembered'}, ...
- 'location', 'southwest'); legend('boxoff')
- xlabel('Time (s) from stim onset')
- xlim([-.25 1.9]); %ylim(YLim)
- ylabel({'ERP';'(microVolts)'});
- xlabel({'Time (s)'});
- set(findall(gcf,'-property','FontSize'),'FontSize',14)
|