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plotERPspelke.m
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plotERPspelke.m
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cd(pwd);
for subi=1:nSubjs;
fname_TimeS= sprintf('subj%02d_TimeS_cond',subi);
pickupTimeS(subi) = load(fname_TimeS);
end
% %
TimeSmatrix=[];
for subi= 1:nSubjs; %nGoodSubjects
for k= 1:5
TimeSmatrix{subi,k}= pickupTimeS(subi).avg_cond{k}; %pickupSub(i).avg_EEG{k}
end
end
%
save TimeSmatrix TimeSmatrix
%
Grand_ER=[];
for k= 1:5
cfg=[];
% cfg.keepindividual= 'yes';
% cfg.channel = {'EEG020','EEG021','EEG029','EEG030','EEG031','EEG039','EEG040'};
Grand_ER{k}= ft_timelockgrandaverage(cfg,TimeSmatrix{:,k});
% Grand_Freq_ROI{k}= mean(Grand_Freq{k}.powspctrm,1);
end
save Grand_ER_Ind Grand_ER
%% EELKE's way (http://eelkespaak.nl/blog/customizing-common-m-eeg-plots-part-1-the-event-related-potential-field-erp-f/)
figure('color','white');
for condi=1:5
tl = Grand_ER{condi};
% Find the index of the channels of interest in the data
chaninds = match_str(tl.label, {'EEG030'});
% Compute average over channels
erp = tl.avg(chaninds,:); % add squeeze if indiv subjs | tl.individual
% Plot
% figure();
hold on;
plot(tl.time, erp);
micro = 1e-6;
erp_microV = erp ./ micro;
% figure();
% colors = {[0 0 1],[1 0 0],[0 1 0],[0 0 0],[1 1 0]};
Colors= {[0.8500 0.3250 0.0980],[0.9290 0.6940 0.1250],[0.4660 0.6740 0.1880],[0 0.4470 0.7410],[0.4940 0.1840 0.5560]};
plot(tl.time, erp_microV,'LineWidth',2.5); % 'Color',Colors{condi}
% xlim([-3.0 1.0]);
xlabel('Time (s)');
ylabel('Amplitude electric potential (\mu V)');
%grid
% Change axis
% ax = gca(); % this Gets the Current Axis so we can set properties
% ax.XAxisLocation = 'origin';
% ax.YAxisLocation = 'origin';
% %ax.TickDir = 'out';
%
% % Remove the box around the plot, while we're at it:
% box off;
%
% % And move the x-axis label to underneath the axis:
% ax.XLabel.Position(2) = -30;
clear tl erp
end
%%
addpath(genpath('/Users/bt_neurospin/matlab/kakearney-boundedline-pkg-50f7e4b'));
conds= [2 4 8 16 Inf];
mycolormap= [0.8500 0.3250 0.0980; 0.9290 0.6940 0.1250; 0.4660 0.6740 0.1880; 0 0.4470 0.7410; 0.4940 0.1840 0.5560]; %linespec
channels= [20 28 30]; %20= FC1; 28= C3; 30= CZ (EEG cap 60 electrodes)
chan_names= {'FC1','C3','Cz'};
ROI = [20 21 29 30 31 39 40];
chaninds= [30 20 28 {'EEG020','EEG021','EEG029','EEG030','EEG031','EEG039','EEG040'}];
if chaninds == 30
chan_names= {'Cz'}
elseif chaninds == 20
chan_names= {'FC1'}
elseif chaninds == 28
chan_names== {'C3'}
else chaninds== {'EEG020','EEG021','EEG029','EEG030','EEG031','EEG039','EEG040'}; %Undefined operator '==' for input arguments of type 'cell'.
chan_names= {'ROI'}
end
f1=figure('color','white');
for condi=1:length(conds);
% figure('color','white');
tl = Grand_ER{condi};
% colors= Colors{condi};
% Find the index of the channels of interest in the data
%chaninds = match_str(tl.label, {'EEG020','EEG021','EEG029','EEG030','EEG031','EEG039','EEG040'});
chaninds = match_str(tl.label, {'EEG030'});
% Compute average over channels
erp = squeeze(mean(tl.individual(:,chaninds,:),1)); % add squeeze if indiv subjs | tl.individual
micro = 1e-6;
erp_microV = erp ./ micro;
% First, get the individual trial data, averaged over our channels of
% interest:
trialdata = squeeze(mean(tl.individual(:,chaninds,:), 2)) ./ micro;
% Use the standard deviation over trials as error bounds:
bounds = sem(trialdata, 1); %sem or std?
% boundedline will replace the call to plot():
b=boundedline(tl.time, erp_microV, bounds, 'cmap', mycolormap(condi,:),'alpha'); % alpha makes bounds transparent
b.LineWidth= 2;
%stattimes= [-1.0320 -0.316];
% stattimes1= linspace(-0.742,-0.26,60);
% % stattimes2= linspace(-1.398,-1.098,40);
% y1= linspace(1,1,60);
% % y2= linspace(1,1,40);
% txt1 = {'p < .05'};
% t1=text(-0.75,2,txt1,'FontSize',25);
% % txt2 = {'p < .05'};
% % t2=text(-1.4,2,txt2,'FontSize',25);
% l1=line(stattimes1,y1); %,'Color','black','Marker','*'
% l1.Color= 'black';
% l1.LineWidth= 3;
% l1.Marker= '*';
% l1.MarkerSize= 10;
% darkGrey1 = [0.4 0.4 0.4]; darkGrey2 = [0.2 0.2 0.2]; darkGrey3 = [0.1 0.1 0.1];
% % l2=line(stattimes2,y2); %,'Color','black','Marker','*'
% % l2.Color= darkGrey1;
% % l2.LineWidth= 2;
% % l2.Marker= '*';
% % l2.MarkerSize= 10;
% Add all our previous improvements:
xlabel('Time (s)','FontSize',34);
ylabel('ERP amplitude (\muV)','FontSize',34);
title(cellstr(chan_names),'FontSize',34);
set(gca,'FontSize',34);
% legend(subset,'FontSize',12)
% ax = gca();
% ax.XAxisLocation = 'origin';
% ax.YAxisLocation = 'origin';
% ax.TickDir = 'out';
% box off;
% ax.XLabel.Position(2) = -60;
hold on;
hold off;
end
f2=figure('color','white');
for condi=1:length(conds);
% figure('color','white');
tl = Grand_ER{condi};
% colors= Colors{condi};
% Find the index of the channels of interest in the data
chaninds = match_str(tl.label, {'EEG030'});
% Compute average over channels
erp = squeeze(mean(tl.individual(:,chaninds,:),1)); % add squeeze if indiv subjs | tl.individual
micro = 1e-6;
erp_microV = erp ./ micro;
% First, get the individual trial data, averaged over our channels of
% interest:
trialdata = squeeze(mean(tl.individual(:,chaninds,:), 2)) ./ micro;
% Use the standard deviation over trials as error bounds:
bounds = sem(trialdata, 1); %sem or std?
% boundedline will replace the call to plot():
b=boundedline(tl.time, erp_microV, bounds, 'cmap', mycolormap(condi,:),'alpha'); % alpha makes bounds transparent
b.LineWidth= 2;
stattimes1= linspace(-0.742,-0.26,60);%linspace(-0.722,-0.448,40);
%stattimes2= linspace(-1.868,-1.63,20);
y1= linspace(1,1,60);
% y2= linspace(1,1,20);
txt1 = {'p < .05'};
t1=text(-0.75,2,txt1,'FontSize',25);
% txt2 = {'p < .05'};
% t2=text(-1.87,4,txt2,'FontSize',25);
l1=line(stattimes1,y1); %,'Color','black','Marker','*'
l1.Color= 'black';
l1.LineWidth= 3;
l1.Marker= '*';
l1.MarkerSize= 10;
darkGrey1 = [0.4 0.4 0.4]; darkGrey2 = [0.2 0.2 0.2]; darkGrey3 = [0.1 0.1 0.1];
% l2=line(stattimes2,y2); %,'Color','black','Marker','*'
% l2.Color= darkGrey2;
% l2.LineWidth= 2;
% l2.Marker= '*';
% l2.MarkerSize= 10;
% Add all our previous improvements:
xlabel('Time (s)','FontSize',34);
ylabel('ERP amplitude (\muV)','FontSize',34);
title(cellstr(chan_names),'FontSize',34);
set(gca,'FontSize',34);
% lgd= legend(,'FontSize',12)
% ax = gca();
% ax.XAxisLocation = 'origin';
% ax.YAxisLocation = 'origin';
% ax.TickDir = 'out';
% box off;
% ax.XLabel.Position(2) = -60;
hold on;
hold off;
end
%% per individual subjects (code not working)
nSubjs=22;
condi=5;
for subi=1:length(nSubjs);
figure('color','white');
tl = Grand_ER{condi};
% colors= Colors{condi};
% Find the index of the channels of interest in the data
%chaninds = match_str(tl.label, {'EEG020','EEG021','EEG029','EEG030','EEG031','EEG039','EEG040'});
chaninds = match_str(tl.label, {'EEG030'});
% Compute average over channels
erp = squeeze(tl.individual(subi,chaninds,:)); % add squeeze if indiv subjs | tl.individual
micro = 1e-6;
erp_microV = erp ./ micro;
% First, get the individual trial data, averaged over our channels of
% interest:
trialdata = (tl.individual(subi,:) ./ micro);
% Use the standard deviation over trials as error bounds:
bounds = std(trialdata, 1); %sem or std?
% boundedline will replace the call to plot():
b=boundedline(tl.time, erp_microV, bounds, 'cmap', mycolormap(condi,:),'alpha'); % alpha makes bounds transparent
b.LineWidth= 2;
% Add all our previous improvements:
xlabel('Time (s)','FontSize',34);
ylabel('ERP amplitude (\muV)','FontSize',34);
title(['subj ' num2str(subi) cellstr(chan_names)],'FontSize',34);
set(gca,'FontSize',34);
% legend(subset,'FontSize',12)
% ax = gca();
% ax.XAxisLocation = 'origin';
% ax.YAxisLocation = 'origin';
% ax.TickDir = 'out';
% box off;
% ax.XLabel.Position(2) = -60;
% hold on;
%filename= ['RPprofile_subj_' num2str(subi) '.png'];
% hold off;
end