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Power_RawLog_ERS_Plots.m
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Power_RawLog_ERS_Plots.m
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% |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
% INFORMATION
% |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
% Need to have loaded the single trial data using LoadProcData_OrientTask.m
% Should end up with all_ersp cell variable
% Code below is for regular trials (not catch trials) that has been aligned
% to target onset
% |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
% :::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
% |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
%% Load settings
load('filt_byTargets_v4_Settings.mat'); %setting for target-aligned trials except catch trials
%% Location to save power data
saveLocation = [exp.dataLocation '\ProcessData\']; % set save directory of data set
%% Location to save figures
saveFig = [pwd '\Figures\ERS\']; % set save directory of data set
% if folder doesn't exist yet, create one
if ~exist(saveFig)
mkdir(saveFig);
end
%% Load saved behavioral data
load([exp.dataLocation '\ProcessData\ALLEEG_' exp.settings '.mat'])
%initialize EEGLAB
[ALLEEG EEG CURRENTSET ALLCOM] = eeglab;
%% Load raw power data
% Data name is for dataset provided. This name will be different if data is
% processed again
load([pwd 'all_ersp_v4.mat'])
% |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
% #########################################################################
% /////////////////////////////////////////////////////////////////////////
%% Raw ERS Values (log scaled)
% /////////////////////////////////////////////////////////////////////////
% #########################################################################
% --For data with targets--
all_erspR = cell(length(exp.participants),length(exp.singletrialselecs)); %pre-allocate
for i_part = 1:length(exp.participants) % --
for ii = 1:length(exp.singletrialselecs)
i_elect = exp.singletrialselecs(ii); %for doing only a selection of electrodes
% all_ersp is (participant x electrode).trials(freq x time x trial)
tmp_ersp = abs(all_ersp{i_part,i_elect}).^2;
for i_trial = 1:size(tmp_ersp,3)
all_erspR{i_part,i_elect}.trials(:,:,i_trial) = log10(tmp_ersp(:,:,i_trial)); %dB converted
end
clear i_trial
end
clear ii i_elect tmp_ersp
end
clear i_part
% /////////////////////////////////////////////////////////////////////////
%% Save Data
chanlocs = EEG.chanlocs; %going to want to save electrode locations
% this is large file so it will take some time to save
save([saveLocation 'all_ersp_R_v4.mat'],'all_erspR','chanlocs','-v7.3')
% /////////////////////////////////////////////////////////////////////////
%% OR Load Processed Data If Exists
all_erspR = struct2cell(load([saveLocation 'all_ersp_R_v4.mat'],'all_erspR')); %gets loaded as a struct
all_erspR = all_erspR{1};
chanlocs = struct2cell(load([saveLocation 'all_ersp_R_v4.mat'],'chanlocs'));
chanlocs = chanlocs{1};
% /////////////////////////////////////////////////////////////////////////
% #########################################################################
% /////////////////////////////////////////////////////////////////////////
%% Useful Plots
% /////////////////////////////////////////////////////////////////////////
% #########################################################################
% /////////////////////////////////////////////////////////////////////////
%% # ERS: Power by Model SD #
% /////////////////////////////////////////////////////////////////////////
% ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
% Load previously created data (if it has been created)
load([saveLocation 'pwrR_AvG_v4.mat']);
% ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
%% Create ERS by errors
x_errdeg_m = cell(1,length(exp.participants)); %pre-allocate
n_errdeg_m = cell(1,length(exp.participants)); %pre-allocate
x_pwr = cell(1,length(exp.singletrialselecs)); %pre-allocate
n_pwr = cell(1,length(exp.singletrialselecs)); %pre-allocate
errlims = cell(1,length(exp.participants)); %pre-allocate
for i_part = 1:length(exp.participants)
% Get upper and lower limits based on model fit
errlims{i_part}(1) = -(model_out{1,i_part}(2)); %negative value
errlims{i_part}(2) = model_out{1,i_part}(2);
% Get errors values
x_errdeg_m{i_part} = resp_errdeg{i_part}(resp_errdeg{i_part}<(errlims{i_part}(2)*0.75) & resp_errdeg{i_part}>(errlims{i_part}(1)*0.75)); %small errors
n_errdeg_m{i_part} = resp_errdeg{i_part}([find(resp_errdeg{i_part}>=(errlims{i_part}(2)*1.5)) find(resp_errdeg{i_part}<=(errlims{i_part}(1)*1.5))]);
% Calculate power
for ii = 1:length(exp.singletrialselecs)
i_elect = exp.singletrialselecs(ii); %for doing only a selection of electrodes
% all_ersp is (participant x electrode).trials(freq x time x trial)
part_ersp = all_erspR{i_part,i_elect}.trials; %get single subject's baseline corrected power
% Get trials with small errors
x_pwr{1,i_elect}(i_part,:,:) = squeeze(mean(part_ersp(:,:,[...
find((resp_errdeg{i_part}<(errlims{i_part}(2)*0.75) & resp_errdeg{i_part}>(errlims{i_part}(1)*0.75)))] ),3));
% Get trials with large errors
n_pwr{1,i_elect}(i_part,:,:) = squeeze(mean(part_ersp(:,:,[...
find(resp_errdeg{i_part}>=(errlims{i_part}(2)*1.5)) find(resp_errdeg{i_part}<=(errlims{i_part}(1)*1.5))] ),3));
clear part_ersp i_elect
end
end
clear ii i_part
% /////////////////////////////////////////////////////////////////////////
% #########################################################################
%% Gets a count of trials
err_trl_count(:,1) = cellfun(@numel,x_errdeg_m); %small errors
err_trl_count(:,2) = cellfun(@numel,n_errdeg_m); %large errors
% err_trl_count(:,3) = cell2mat({ALLEEG(1:end).trials}); %total trial count
% ########################################################################
% /////////////////////////////////////////////////////////////////////////
%% Save data if not saved yet
save([saveLocation 'pwrR_AvG_v4.mat'],'errlims','n_errdeg_m','n_pwr','x_errdeg_m','x_pwr');
% /////////////////////////////////////////////////////////////////////////
% &&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&
%% Plot spectogram across subjects &&
% &&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&
% Raw ERS plots
cmap = redblue(256); %create colormap colors
savename = 'SpecPlotR_';
for ii = 1:length(exp.singletrialselecs)
i_elect = exp.singletrialselecs(ii); %for doing only a selection of electrodes
%mean across subjects
plot_ers_x = squeeze(mean(x_pwr{1,i_elect}(:,:,:),1)); %small errors
plot_ers_n = squeeze(mean(n_pwr{1,i_elect}(:,:,:),1)); %large errors
CLim = [4.5 6.5]; %set power scale of plot
% Plot Small Errors
figure('Position', [1 1 1685 405]); colormap(cmap) %open a new figure
subplot(1,2,1)
imagesc(times,freqs,plot_ers_x,CLim);
title(['Accurate: ' exp.singtrlelec_name{ii}]); set(gca,'Ydir','Normal')
line([0 0],[min(freqs) max(freqs)],'Color','k','LineStyle','--','LineWidth',1.5) %vertical line
line([567 567],[min(freqs) max(freqs)],'color','m','LineStyle','--','LineWidth',1.5) %vertical line for response screen onset
xlim([-700 800]); xticks(-600:200:800)
ylim([2 40]); yticks(5:5:40)
% xlim([-200 800]); xticks(-200:100:800) %match ERPs
ylabel('Freqency (Hz)'); xlabel('Time (ms)');
t = colorbar('peer',gca);
set(get(t,'ylabel'),'String', 'Log Power');
% Plot Large Errors
subplot(1,2,2)
imagesc(times,freqs,plot_ers_n,CLim);
title(['Guesses: ' exp.singtrlelec_name{ii}]); set(gca,'Ydir','Normal')
line([0 0],[min(freqs) max(freqs)],'Color','k','LineStyle','--','LineWidth',1.5) %vertical line
line([567 567],[min(freqs) max(freqs)],'color','m','LineStyle','--','LineWidth',1.5) %vertical line for response screen onset
xlim([-700 800]); xticks(-600:200:800)
ylim([2 40]); yticks(5:5:40)
% xlim([-200 800]); xticks(-200:100:800) %match ERPs
ylabel('Freqency (Hz)'); xlabel('Time (ms)');
t = colorbar('peer',gca);
set(get(t,'ylabel'),'String', 'Log Power');
savefig([saveFig savename exp.singtrlelec_name{ii}])
clear plot_ers_x plot_ers_n CLim t
end
clear ii i_elect cmap savename
% :::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
% :::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
% Difference ERS plot
cmap = redblue(256); %create colormap colors
savename = 'SpecPlot_DifZ_';
for ii = 1:length(exp.singletrialselecs)
% for ii = 1:5
i_elect = exp.singletrialselecs(ii); %for doing only a selection of electrodes
%mean across subjects
plot_ers_x = squeeze(mean(x_pwr{1,i_elect}(:,:,:),1)); %small errors
plot_ers_n = squeeze(mean(n_pwr{1,i_elect}(:,:,:),1)); %large errors
CLim = [-0.1 0.1]; %set power scale of plot
% Plot Accurate-Guesses
figure; colormap(cmap) %open a new figure
imagesc(times,freqs,plot_ers_x-plot_ers_n,CLim);
title(['Accurate-Guesses: ' exp.singtrlelec_name{ii}]); set(gca,'Ydir','Normal')
line([0 0],[min(freqs) max(freqs)],'Color','k','LineStyle','--','LineWidth',1.5) %vertical line
line([567 567],[min(freqs) max(freqs)],'color','m','LineStyle','--','LineWidth',1.5) %vertical line for response screen onset
xlim([-700 800]); xticks(-600:200:800)
ylim([2 40]); yticks(5:5:40)
% xlim([-200 800]); xticks(-200:100:800) %match ERPs
ylabel('Freqency (Hz)'); xlabel('Time (ms)');
t = colorbar('peer',gca);
t.Ticks = [-0.3:0.1:0.3]; %make sure colorbar contains ticks
set(get(t,'ylabel'),'String', 'Log Power');
savefig([saveFig savename exp.singtrlelec_name{ii}])
clear plot_ers_x plot_ers_n CLim t
end
clear ii i_elect cmap savename
% :::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
% :::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
% Grand average difference ERS plot
plot_ers_x = NaN(length(exp.singletrialselecs),length(freqs),length(times)); %pre-allocate
plot_ers_n = NaN(length(exp.singletrialselecs),length(freqs),length(times)); %pre-allocate
for ii = 1:length(exp.singletrialselecs)
i_elect = exp.singletrialselecs(ii); %for doing only a selection of electrodes
%mean across subjects
plot_ers_x(i_elect,:,:) = squeeze(mean(x_pwr{1,i_elect}(:,:,:),1)); %small errors
plot_ers_n(i_elect,:,:) = squeeze(mean(n_pwr{1,i_elect}(:,:,:),1)); %large errors
end
clear ii i_elect
%Grand Average
plot_x_avg = squeeze(nanmean(plot_ers_x,1)); %small errors
plot_n_avg = squeeze(nanmean(plot_ers_n,1)); %large errors
clear plot_ers_x plot_ers_n
% Raw ERS plots
cmap = jet; %create colormap colors
CLim = [4.5 6.5]; %set power scale of plot
% Plot Small Errors
figure('Position', [1 1 1685 405]); colormap(cmap) %open a new figure
subplot(1,2,1)
imagesc(times,freqs,plot_x_avg,CLim);
title(['Accurate: Grand Avg']); set(gca,'Ydir','Normal')
line([0 0],[min(freqs) max(freqs)],'Color','k','LineStyle','--','LineWidth',1.5) %vertical line
line([567 567],[min(freqs) max(freqs)],'color','m','LineStyle','--','LineWidth',1.5) %vertical line for response screen onset
xlim([-700 800]); xticks(-600:200:800)
ylim([2 40]); yticks(5:5:40)
% xlim([-200 800]); xticks(-200:100:800) %match ERPs
ylabel('Freqency (Hz)'); xlabel('Time (ms)');
t = colorbar('peer',gca);
set(get(t,'ylabel'),'String', 'Log Power');
% Plot Large Errors
subplot(1,2,2)
imagesc(times,freqs,plot_n_avg,CLim);
title(['Guesses: Grand Avg']); set(gca,'Ydir','Normal')
line([0 0],[min(freqs) max(freqs)],'Color','k','LineStyle','--','LineWidth',1.5) %vertical line
line([567 567],[min(freqs) max(freqs)],'color','m','LineStyle','--','LineWidth',1.5) %vertical line for response screen onset
xlim([-700 800]); xticks(-600:200:800)
ylim([2 40]); yticks(5:5:40)
% xlim([-200 800]); xticks(-200:100:800) %match ERPs
ylabel('Freqency (Hz)'); xlabel('Time (ms)');
t = colorbar('peer',gca);
set(get(t,'ylabel'),'String', 'Log Power');
savefig([saveFig 'SpecPlotR_GrandAvg'])
clear cmap savename CLim t
% :::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
% Plot Accurate-Guesses
CLim = [-0.1 0.1]; %set power scale of plot
cmap = redblue(256); %create colormap colors
figure; colormap(cmap) %open a new figure
imagesc(times,freqs,plot_x_avg-plot_n_avg,CLim);
title('Accurate-Guesses: Grand Avg'); set(gca,'Ydir','Normal')
line([0 0],[min(freqs) max(freqs)],'Color','k','LineStyle','--','LineWidth',1.5) %vertical line
line([567 567],[min(freqs) max(freqs)],'color','m','LineStyle','--','LineWidth',1.5) %vertical line for response screen onset
xlim([-700 800]); xticks(-600:200:800)
ylim([2 40]); yticks(5:5:40)
% xlim([-200 800]); xticks(-200:100:800) %match ERPs
ylabel('Freqency (Hz)'); xlabel('Time (ms)');
t = colorbar('peer',gca);
t.Ticks = [-0.3:0.1:0.3]; %make sure colorbar contains ticks
set(get(t,'ylabel'),'String', 'Log Power');
savefig([saveFig 'SpecPlot_DifR_GrandAvg'])
clear plot_x_avg plot_n_avg CLim t cmap
% -------------------------------------------------------------------------
% -------------------------------------------------------------------------