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Anal_Wavelet_Orient_byTarget.m
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% |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
% INFORMATION
% |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
% SPECTOGRAM
% A spectogram is a 3d figure that plots time on the x-axis, frequency on the
% y-axis, and shows you the power or phase-locking value for each point.
% We compute spectograms if we have power and phase information, averaged
% across trials, for at least one electrode.
% This can help us understand the changes of power and phase throughout the
% trial.
% |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
% :::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
% |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
% Variables working with:
% ersp(i_sub,i_cond,i_perm,i_chan,:,:)
% itc(i_sub,i_cond,i_perm,i_chan,:,:)
% powbase,times,freqs
% The variables ersp and itc will be a 6D variable:
% (participants x conditions x events x electrodes x frequencies x timepoints)
% (participants x sets x events x electrodes x frequencies x timepoints)
% :::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
% period = 1/EEG.srate;
% time (in s) = [EEG.event.latency]*period
% :::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
eeglab redraw
% |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
% :::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
% |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
%% Load previously processed target-aligned epoch data
% data has been converted to log scale
all_erspN = struct2cell(load('all_erspN.mat')); %gets loaded as a struct
all_erspN = all_erspN{1};
% load behavior data
load('ALLEEG_filt_byTargets_v3.mat');
% load settings
load('filt_byTargets_v3_Settings.mat');
% /////////////////////////////////////////////////////////////////////////
% -------------------------------------------------------------------------
%% Load previously processed catch trial data
% assumes that the catch trials have been processed with the same
% parameters as the all_ersp data (check their settings in exp to make sure)
catch_trials_v1_wav = load("all_ersp_catch_trials.mat");
all_ersp_byC = catch_trials_v1_wav.all_ersp;
% Check to make sure their frequency and time scales are the same
% output should be 0 or they are not the same!
sum(catch_trials_v1_wav.freqs ~= freqs)
sum(catch_trials_v1_wav.times ~= times)
clear catch_trials_v1_wav
% /////////////////////////////////////////////////////////////////////////
% -------------------------------------------------------------------------
%% $$$$$$$ BEH Data $$$$$$$
% Get BEH data for trials excluding trials that were rejected in the EEG
% preprocessing of the epochs
resp_errdeg = cell(length(exp.participants),1); %pre-allocate
for i_part = 1:length(exp.participants) % --------------------
[n,m] = size(ALLEEG(i_part).rejtrial);
% Get list of rejected trials
pip = 1;
for ni = 1:n %for when there are more than 1 column
for mi = 1:m
if ~isempty(ALLEEG(i_part).rejtrial(ni,mi).ids)
rejlist{pip} = ALLEEG(i_part).rejtrial(ni,mi).ids;
pip = 1 + pip;
end
end
clear mi
end
if pip > 1 %if trials were rejected
err_deg_tmp = ALLEEG(i_part).error_deg; %start with all the errors
% each set of rejected trials needs to be removed in order
% sequentially
for mi = 1:length(rejlist)
tmplist = [rejlist{mi}];
err_deg_tmp(tmplist) = []; %removes the trials
clear tmplist
end
clear mi
elseif pip == 1 %if no trials were rejected, rejlist variable not created
err_deg_tmp = ALLEEG(i_part).error_deg;
end
% create variable with selected BEH
resp_errdeg{i_part} = err_deg_tmp;
clear rejlist n m err_deg_tmp pip ni
end
clear i_part
% -------------------------------------------------------------------------
% /////////////////////////////////////////////////////////////////////////
% Fit errors to mixed model
model1 = StandardMixtureModel();
model2 = WithBias(StandardMixtureModel);
model_out = cell(1,length(exp.participants)); %pre-allocate
for ii = 1:length(exp.participants)
error_deg{ii} = resp_errdeg{ii};
% error_deg{ii} = ALLEEG(ii).error_deg; %comment out if loaded data above
% model_out{ii} = MemFit(error_deg{ii},model1);
% MemFit(error_deg{ii},{model1,model2}); %comparing models
model_out{ii} = MLE(error_deg{ii},model1); %fits without plotting
% model_out{ii} = MLE(error_deg{ii},model2); %fits without plotting
% model_out{ii} = model_out{ii}(2:3);
end
clear ii error_deg model1 model2
% -------------------------------------------------------------------------
% /////////////////////////////////////////////////////////////////////////
% @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
% $$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$
%% >>>>>>>>>>>>>>>>>>>> POWER ANALYSES <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
% $$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$
% @@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@
% #########################################################################
% /////////////////////////////////////////////////////////////////////////
%% Baseline correction of power by trial and frequency band
% /////////////////////////////////////////////////////////////////////////
% #########################################################################
% Right now it is set-up to subtract mean power across the epoch at each
% frequency from each time point at that same frequency
% note: does not include the catch trial data
tic %see how long this takes
all_erspN = 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});
for i_trial = 1:size(tmp_ersp,3)
for fq = 1:length(freqs)
% bl_freq = mean(tmp_ersp(fq,:,i_trial),2); %average each trial
bl_freq = mean(mean(tmp_ersp(fq,:,:),2),3); %average all trials
all_erspN{i_part,i_elect}.trials(fq,:,i_trial) = tmp_ersp(fq,:,i_trial)-bl_freq;
end
clear fq bl_freq
end
end
clear ii i_elect tmp_ersp
end
clear i_part
toc %see how long this takes
% #########################################################################
% #########################################################################
% /////////////////////////////////////////////////////////////////////////
%% OR use raw ERS values (log scaled)
% /////////////////////////////////////////////////////////////////////////
% --For data with targets--
all_erspN = 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});
for i_trial = 1:size(tmp_ersp,3)
all_erspN{i_part,i_elect}.trials(:,:,i_trial) = 10*log10(tmp_ersp(:,:,i_trial)); %dB converted
end
clear i_trial
end
clear ii i_elect tmp_ersp
end
clear i_part
% --For catch trial data--
all_erspC = 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_byC{i_part,i_elect});
for i_trial = 1:size(tmp_ersp,3)
all_erspC{i_part,i_elect}.trials(:,:,i_trial) = 10*log10(tmp_ersp(:,:,i_trial)); %dB converted
end
clear i_trial
end
clear ii i_elect tmp_ersp
end
clear i_part
% #########################################################################
% /////////////////////////////////////////////////////////////////////////
%% ERS: Power by errors
% /////////////////////////////////////////////////////////////////////////
% #########################################################################
% 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}.maxPosterior(2)); %negative value
% errlims{i_part}(2) = model_out{1,i_part}.maxPosterior(2);
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_erspN{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
% /////////////////////////////////////////////////////////////////////////
% Create ERS for catch trials
c_pwr = cell(1,length(exp.singletrialselecs)); %pre-allocate
for i_part = 1:length(exp.participants) % ----------------------
% 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_erspC{i_part,i_elect}.trials; %get single subject's baseline corrected power
c_pwr{1,i_elect}(i_part,:,:) = squeeze(mean(part_ersp(:,:,:),3));
end
clear part_ersp i_elect
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
err_trl_count(:,4) = cell2mat({ALLEEG(1:end).catch_trials}); %catch trials
% #########################################################################
% #########################################################################
% :::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
% /////////////////////////////////////////////////////////////////////////
%% >>> Plot spectogram across subjects
% /////////////////////////////////////////////////////////////////////////
% Raw ERS plots
% 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
plot_ers_c = squeeze(mean(c_pwr{1,i_elect}(:,:,:),1)); %catch trials
CLim = [20 35]; %set power scale of plot
% Plot Small Errors
figure('Position', [1 1 1685 405]); colormap('jet') %open a new figure
subplot(1,3,1)
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
ylim([3 35]); yticks(5:5:35)
xlim([-700 800]); xticks(-600:200:800)
ylabel('Freqency (Hz)'); xlabel('Time (ms)');
colorbar
% Plot Large Errors
subplot(1,3,2)
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
ylim([3 35]); yticks(5:5:35)
xlim([-700 800]); xticks(-600:200:800)
ylabel('Freqency (Hz)'); xlabel('Time (ms)');
colorbar
% Plot Catch Trials
subplot(1,3,3)
imagesc(times,freqs,plot_ers_c,CLim);
title(['No Target: ' 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
ylim([3 35]); yticks(5:5:35)
xlim([-700 800]); xticks(-600:200:800)
ylabel('Freqency (Hz)'); xlabel('Time (ms)');
colorbar
clear plot_ers_x plot_ers_n plot_ers_c CLim
end
clear ii i_elect
% :::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
% Difference ERS plots
% 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
plot_ers_c = squeeze(mean(c_pwr{1,i_elect}(:,:,:),1)); %catch trials
CLim = [-0.8 0.8]; %set power scale of plot
% Plot Accurate-Guesses
figure('Position', [1 1 1685 405]); colormap('jet') %open a new figure
subplot(1,3,1)
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
ylim([3 35]); yticks(5:5:35)
xlim([-700 800]); xticks(-600:200:800)
ylabel('Freqency (Hz)'); xlabel('Time (ms)');
colorbar
% Plot Guesses-No Target
subplot(1,3,2)
imagesc(times,freqs,plot_ers_x-plot_ers_c,CLim);
title(['Accurate-No Target: ' 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
ylim([3 35]); yticks(5:5:35)
xlim([-700 800]); xticks(-600:200:800)
ylabel('Freqency (Hz)'); xlabel('Time (ms)');
colorbar
% Plot Accurate-No Target
subplot(1,3,3)
imagesc(times,freqs,plot_ers_n-plot_ers_c,CLim);
title(['Guesses-No Target: ' 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
ylim([3 35]); yticks(5:5:35)
xlim([-700 800]); xticks(-600:200:800)
ylabel('Freqency (Hz)'); xlabel('Time (ms)');
colorbar
clear plot_ers_x plot_ers_n plot_ers_c CLim
end
clear ii i_elect
% -------------------------------------------------------------------------
% -------------------------------------------------------------------------
% &&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&
%% Plot spectogram for each subject &
% &&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&&
for i_part = 1:length(exp.participants)
figure('Position', [1 1 624 1016]); colormap('jet') %open a new figure
for ii = 1:5 %just central electrodes
% for ii = 1:length(exp.singletrialselecs)
i_elect = exp.singletrialselecs(ii); %for doing only a selection of electrodes
plot_ers_x = squeeze(x_pwr{1,i_elect}(i_part,:,:)); %small errors data for each subject
plot_ers_n = squeeze(n_pwr{1,i_elect}(i_part,:,:)); %large errors data for each subject
plot_ers_c = squeeze(c_pwr{1,i_elect}(i_part,:,:)); %large errors data for each subject
CLim = [0 2000]; %set power scale of plot
figure('Position', [1 1 1685 405]); colormap('jet') %open a new figure
% Plot Small Errors
subplot(1,3,1)
imagesc(times,freqs,plot_ers_x,CLim);
title('Small Errors'); 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
ylim([3 35]); yticks(5:5:35)
xlim([-700 800]); xticks(-600:200:800)
ylabel('Freq (Hz)'); xlabel('Time (ms)');
colorbar
% Plot Large Errors
subplot(1,3,2)
imagesc(times,freqs,plot_ers_n,CLim);
title('Large Errors'); 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
ylim([3 35]); yticks(5:5:35)
xlim([-700 800]); xticks(-600:200:800)
ylabel('Freq (Hz)'); xlabel('Time (ms)');
colorbar
% Plot Catch Trials
subplot(1,3,3)
imagesc(times,freqs,plot_ers_c);
title('No Target'); set(gca,'Ydir','Normal')
line([0 0],[min(freqs) max(freqs)],'Color','k','LineStyle','--','LineWidth',1.5) %vertical line for target onset
line([567 567],[min(freqs) max(freqs)],'color','m','LineStyle','--','LineWidth',1.5) %vertical line for response screen onset
ylim([3 35]); yticks(5:5:35)
xlim([-700 800]); xticks(-600:200:800)
ylabel('Freqency (Hz)'); xlabel('Time (ms)');
colorbar
clear plot_ers_x plot_ers_n plot_ers_c
end
% Overall subplot title
supertitle(['Subj ' num2str(exp.participants{i_part}) ': ' exp.singtrlelec_name{ii}],...
'FontSize',10.5)
clear ii i_elect
end
clear i_part CLim
% -------------------------------------------------------------------------
% -------------------------------------------------------------------------
% /////////////////////////////////////////////////////////////////////////
%% Compute power in time and frequency windows for errors
% /////////////////////////////////////////////////////////////////////////
% -------------------------------------------------------------------------
clear x_pwr_win n_pwr_win c_pwr_win
%finds the frequencies you want
freqband = [8 14]; %alpha
% freqband = [8 11]; %low alpha
% freqband = [11 14]; %high alpha
% freqband = [3 8]; %theta
% freqband = [15 35]; %beta
freqlim = find(freqs>=(freqband(1)-0.5) & freqs<=(freqband(2)+0.5));
%finds the times you want from the timess variable
timewin = [-600 -400];
% timewin = [-400 -200];
% timewin = [-200 -100];
% timewin = [-200 0];
% timewin = [-100 0];
% timewin = [0 200];
% timewin = [200 400];
% timewin = [400 600];
% timewin = [300 600];
% timewin = [100 200];
% timewin = [200 300];
% timewin = [300 500];
% timewin = [400 500];
% timewin = [500 600];
timelim = find(times>=timewin(1) & times<=timewin(2));
x_pwr_win = cell(length(exp.singletrialselecs),1); %pre-allocate
n_pwr_win = cell(length(exp.singletrialselecs),1); %pre-allocate
c_pwr_win = cell(length(exp.singletrialselecs),1); %pre-allocate
for i_part = 1:length(exp.participants)
for ii = 1:5 %only central electrodes
% for ii = 1:length(exp.singletrialselecs)
i_elect = exp.singletrialselecs(ii); %for doing only a selection of electrodes
x_pwr_win{i_elect}(i_part) = mean(mean(x_pwr{1,i_elect}(i_part,freqlim,timelim),2),3); %small errors
n_pwr_win{i_elect}(i_part) = mean(mean(n_pwr{1,i_elect}(i_part,freqlim,timelim),2),3); %large errors
c_pwr_win{i_elect}(i_part) = mean(mean(c_pwr{1,i_elect}(i_part,freqlim,timelim),2),3); %no target
end
clear ii i_elect
end
clear i_part
% -------------------------------------------------------------------------
% +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
% -------------------------------------------------------------------------
%% Run nonparametric statistics
% for ii = 1:length(exp.singletrialselecs)
for ii = 1:5 %only central electrodes
i_elect = exp.singletrialselecs(ii); %for doing only a selection of electrodes
sgnrank_pwr_win_elect{ii} = exp.singtrlelec_name{ii}; %save name
% Sign rank test
% accurate v guesses
[p,h,stat] = signrank(x_pwr_win{i_elect}(:),n_pwr_win{i_elect}(:));
sgnrank_pwr_win.AvG(ii,1) = p;
sgnrank_pwr_win.AvG(ii,2) = h;
sgnrank_pwr_win_stats.AvG(ii,1) = stat;
clear h p stat
% accurate v catch trials
[p,h,stat] = signrank(x_pwr_win{i_elect}(:),c_pwr_win{i_elect}(:));
sgnrank_pwr_win.AvC(ii,1) = p;
sgnrank_pwr_win.AvC(ii,2) = h;
sgnrank_pwr_win_stats.AvC(ii,1) = stat;
clear h p stat
% accurate v catch trials
[p,h,stat] = signrank(n_pwr_win{i_elect}(:),c_pwr_win{i_elect}(:));
sgnrank_pwr_win.GvC(ii,1) = p;
sgnrank_pwr_win.GvC(ii,2) = h;
sgnrank_pwr_win_stats.GvC(ii,1) = stat;
clear h p stat i_elect
end
clear ii i_elect
% :::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
%% Run parametric statistics
% for ii = 1:length(exp.singletrialselecs)
% i_elect = exp.singletrialselecs(ii); %for doing only a selection of electrodes
% ttest_pwr.elect{ii} = exp.singtrlelec_name{ii}; %save name
% % t-test
% [h,p,ci,stat] = ttest(x_pwr_win{i_elect}(:),n_pwr_win{i_elect}(:));
% ttest_pwr_win(ii,1) = h;
% ttest_pwr_win(ii,2) = p;
% ttest_pwr_ci_win(ii,1) = ci(1);
% ttest_pwr_ci_win(ii,2) = ci(2);
% ttest_pwr_stats_win(ii,1).electrode = stat;
% clear h p ci stat
% end
% clear ii i_elect
% $$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$
% $$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$
%% Run Permutation test
nperms = 10000; %number of permutations used to estimate null distribution
% re-set values
permtest.zval_obs = [];
permtest.p_z = [];
permtest.p_n = [];
% Permutation test at each electrode
for ii = 1:5 %test central electrodes only
i_elect = exp.singletrialselecs(ii); %for doing only a selection of electrodes
permtest.elect{ii,1} = exp.singtrlelec_name{i_elect};
obs_pwr = n_pwr_win{i_elect}(:); %large error
n_resp = length(obs_pwr);
% Make distribution of null-hypothesis test statistic
zval_perm = zeros(1,nperms); %pre-allocate
for i_perm = 1:nperms
order_resp = randperm(n_resp); %randomly set order of data
[p,h,stat] = signrank(x_pwr_win{i_elect}(:),n_pwr_win{i_elect}(order_resp));
zval_perm(i_perm) = stat.zval;
clear order_resp p h stat
end
clear i_perm pval
% Get observed zval value
[p,h,stat] = signrank(x_pwr_win{i_elect}(:),obs_pwr);
permtest.obs_zval(ii) = stat.zval;
% Plot null distribution
% figure; histogram(zval_perm)
% Get p-value based on Z distribution
% **null distribution needs to be at least approximately Gaussian
% *can use 2-tail only when null distribution is Gaussian, else 1-tail
% Z_val = (permtest.obs_zval(ii) - mean(zval_perm))/std(zval_perm);
% p_z = normcdf(Z_val); %lower-tail
% permtest.p_z(ii) = normcdf(Z_val,'upper'); %upper-tailed
[h,permtest.p_z(ii)] = ztest(permtest.obs_zval(ii), mean(zval_perm), std(zval_perm)); %two-tailed
% Get p-value based on count
% permtest.p_n(ii) = sum(permtest.obs_zval(ii) < zval_perm)/nperms; %upper-tailed
permtest.p_n(ii) = sum(abs(permtest.obs_zval(ii)) < abs(zval_perm))/nperms; %two-tailed
clear Z_val h pval zval_perm obs_pwer p stat i_elect
end
clear n_resp nperms ii
% :::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
%% Descriptive statistics
ii = 3;
i_elect = exp.singletrialselecs(ii); %for doing only a selection of electrodes
% nanmean(x_pwr_win{i_elect}(:))
% nanmean(n_pwr_win{i_elect}(:))
%
% nanstd(x_pwr_win{i_elect}(:))
% nanstd(n_pwr_win{i_elect}(:))
bar_vals = [nanmean(x_pwr_win{i_elect}(:)) nanmean(n_pwr_win{i_elect}(:))];
bar_errs = [(nanstd(x_pwr_win{i_elect}(:))/sqrt(length(exp.participants)))...
(nanstd(n_pwr_win{i_elect}(:))/sqrt(length(exp.participants)))];
% Bar graph
figure;
barweb(bar_vals, bar_errs);
% ylim([0.1 0.2]);
legend('Accurate','Guesses');
title([exp.singtrlelec_name{ii} ': ' num2str(timewin(1)) ' to ' num2str(timewin(2)) ' ms; ' num2str(freqband(1)) '-' num2str(freqband(2)) ' Hz'])
clear bar_vals bar_errs
% -------------------------------------------------------------------------
% /////////////////////////////////////////////////////////////////////////
%% Correlate power with errors
% /////////////////////////////////////////////////////////////////////////
% -------------------------------------------------------------------------
% :::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
% Compute power in time and frequency windows
% :::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
clear current_power
%finds the frequencies you want
% freqband = [7 13]; %alpha
freqband = [8 14]; %alpha
% freqband = [7 10]; %low alpha
% freqband = [10 14]; %high alpha
% freqband = [4 7]; %theta
freqlim = find(freqs>=(freqband(1)-0.5) & freqs<=(freqband(2)+0.5));
%finds the times you want from the timess variable
% timewin = [-600 -400];
% timewin = [-400 -200];
timewin = [-200 0];
% timewin = [0 200];
% timewin = [200 400];
% timewin = [400 600];
timelim = find(times>=timewin(1) & times<=timewin(2));
current_power = cell(length(exp.participants),length(exp.singletrialselecs)); %pre-allocate
for i_part = 1:length(exp.participants)
% for ii = 1:5 %only central electrodes
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_ersp{i_part,i_elect}; %get single subject's ersp
part_ersp = all_erspN{i_part,i_elect}.trials; %get single subject's ersp
for i_trial = 1:(size(part_ersp,3))
% current_power{i_part,i_elect}(i_trial) = squeeze(mean(mean(abs(part_ersp(freqlim,timelim,i_trial)),1),2));
current_power{i_part,i_elect}(i_trial) = squeeze(mean(mean(part_ersp(freqlim,timelim,i_trial),1),2));
end
clear part_ersp i_trial
% plot trial power on a histogram
% figure; hist(current_power{i_part,i_elect},30)
% ylabel('Count');
% title(['Subj ' num2str(exp.participants{i_part}) '-' exp.singtrlelec_name{ii}])
end
end
clear i_elect i_part timelim
% /////////////////////////////////////////////////////////////////////////
%% Correlate power with degrees error each subject
% /////////////////////////////////////////////////////////////////////////
% Loop through each Participant & plot correlation
for i_part = 1:length(exp.participants)
for ii = 1 %only central electrodes
% for ii = 1:length(exp.singletrialselecs)
i_elect = exp.singletrialselecs(ii); %for doing only a selection of electrodes
[rho,pval] = circ_corrcl(circ_ang2rad(resp_errdeg{i_part}),current_power{i_part,i_elect});
% correlation betwen power and errors and plot
figure; polarscatter(circ_ang2rad(resp_errdeg{i_part}),current_power{i_part,i_elect})
title(['Subj ' num2str(exp.participants{i_part}) '-' exp.singtrlelec_name{ii}...
': rho=' num2str(round(rho,2)) ' pval=' num2str(round(pval,2))])
clear x y rho pval pog
end
clear i_elect ii
end
clear i_part
% /////////////////////////////////////////////////////////////////////////
%% Correlate power with degrees error
% /////////////////////////////////////////////////////////////////////////
% Create one big array of power
all_currentpwr = cell(1,length(exp.singletrialselecs)); %pre-allocate
for ii = 1:length(exp.singletrialselecs)
i_elect = exp.singletrialselecs(ii); %for doing only a selection of electrodes
all_currentpwr{1,i_elect} = cat(2,current_power{1:end,i_elect});
end
clear ii i_elect
% Put all the response errors across subjects into vector
resp_errdeg_cat = cat(2,resp_errdeg{1:end});
% :::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
% Plot correlation overall all subjects and trials
for ii = 1:5 %only central electrodes
% for ii = 1:length(exp.singletrialselecs)
i_elect = exp.singletrialselecs(ii); %for doing only a selection of electrodes
[rho,pval] = circ_corrcl(circ_ang2rad(resp_errdeg_cat),all_currentpwr{1,i_elect});
% correlation betwen power and errors and plot
figure; polarscatter(circ_ang2rad(resp_errdeg_cat),all_currentpwr{1,i_elect},'filled','MarkerFaceAlpha',.5)
title([exp.singtrlelec_name{ii} ': ' num2str(timewin(1)) ' to ' num2str(timewin(2)) ' ms; ' num2str(freqband(1)) '-' num2str(freqband(2)) ' Hz'...
': rho=' num2str(round(rho,2)) ' pval=' num2str(round(pval,2))])
clear x y rho pval
end
clear i_elect ii
% :::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
% /////////////////////////////////////////////////////////////////////////
%% Correlate power with degrees error each subject in one plot
% /////////////////////////////////////////////////////////////////////////
% Loop through each Participant & plot correlation
figure('Position', [1 1 1893 402])
for i_part = 1:length(exp.participants) % --------------
for ii = 1 %only central electrodes
% for ii = 1:length(exp.singletrialselecs)
i_elect = exp.singletrialselecs(ii); %for doing only a selection of electrodes
[rho,pval] = circ_corrcl(circ_ang2rad(resp_errdeg{i_part}),current_power{i_part,i_elect});
% correlation betwen power and errors and plot
subtightplot(2,13,i_part,[0.01 0.03],[0.001 0.001],[0.05 0.05])
polarscatter(circ_ang2rad(resp_errdeg{i_part}),current_power{i_part,i_elect},16)
% title(['Subj ' num2str(exp.participants{i_part}) ': rho=' num2str(round(rho,2)) ' pval=' num2str(round(pval,2))])
clear x y rho pval
end
clear i_elect ii
end
clear i_part
% :::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
% $$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$
% -------------------------------------------------------------------------
% --- Permutation test ---
% -------------------------------------------------------------------------
% $$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$$
nperms = 10000; %number of permutations used to estimate null distribution
resp_errrad_cat = circ_ang2rad(resp_errdeg_cat); %convert to radians for circular corr
n_resp = length(resp_errrad_cat); %number of observations to permute
% re-set values
permtest.rho_obs = [];
permtest.p_z = [];
permtest.p_n = [];
% Permutation test at each electrode
for ii = 1:5 %test central electrodes only
i_elect = exp.singletrialselecs(ii); %for doing only a selection of electrodes
permtest.elect{ii,1} = exp.singtrlelec_name{i_elect};
obs_power = all_currentpwr{1,i_elect}; %get power data from electrode
% Make distribution of null-hypothesis test statistic
rho_perm = zeros(1,nperms); %pre-allocate
for i_perm = 1:nperms
order_resp = randperm(n_resp); %randomly set order of data
[rho_perm(i_perm), pval] = circ_corrcl(resp_errrad_cat(order_resp),obs_power);
clear order_resp
end
clear i_perm pval i_elect
% Get observed rho value
[permtest.rho_obs(ii), pval] = circ_corrcl(resp_errrad_cat,obs_power);
% Plot null distribution
% figure; histogram(rho_perm)
% Get p-value based on Z distribution
% **null distribution needs to be at least approximately Gaussian
% *can use 2-tail only when null distribution is Gaussian, else 1-tail
Z_val = (permtest.rho_obs(ii) - mean(rho_perm))/std(rho_perm);
% p_z = normcdf(Z_val); %lower-tail
permtest.p_z(ii) = normcdf(Z_val,'upper'); %upper-tailed
% [h,p_z] = ztest(rho_obs, mean(rho_perm), std(rho_perm)); %two-tailed
% Get p-value based on count
permtest.p_n(ii) = sum(permtest.rho_obs(ii) < rho_perm)/nperms; %upper-tailed
% p_n = sum(abs(rho_obs) < abs(rho_perm))/nperms; %two-tailed
clear Z_val h pval rho_perm obs_power
end
clear n_resp nperms ii
% '''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''
%% ''''''''''''''''''''''' Topographys ''''''''''''''''''''''''''''''
% '''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''
% List electrodes to get topograph plots (need all of them)
elect_topplot = [1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32];
% el_erp_names = {'M2';'Oz';'Pz';'Cz';'FCz';'Fz';'O1';'O2';'PO3';'PO4';'P7';'P8';'P5';'P6';'P3';'P4';'CP5';...
% 'CP6';'CP1';'CP2';'C3';'C4';'FC5';'FC6';'FC1';'FC2';'F7';'F8';'F3';'F4';'Fp1';'Fp2'};
% Set the range of time to consider
tWin{1} = [-600 -400];
tWin{2} = [-400 -200];
tWin{3} = [-200 0];
tWin{4} = [0 200];
tWin{5} = [200 400];
tWin{6} = [400 600];
% tWin{1} = [-600 -300];
% tWin{2} = [-300 0];
% tWin{3} = [0 300];
% tWin{4} = [300 600];
% Finds the frequencies you want from the freqs variable
freqband = [8 14]; %alpha
% freqband = [3 8]; %theta
freqlim = find(freqs>=(freqband(1)-0.5) & freqs<=(freqband(2)+0.5));
% Get mean power at frequency band for each electrode
pwr_top = NaN([length(times),length(elect_topplot),3]); %pre-allocate
for ii = 1:length(exp.singletrialselecs)
i_elect = exp.singletrialselecs(ii); %for doing only a selection of electrodes
pwr_top(:,i_elect,1) = squeeze(mean(mean(x_pwr{1,i_elect}(:,freqlim,:),2),1)); %small errors
pwr_top(:,i_elect,2) = squeeze(mean(mean(n_pwr{1,i_elect}(:,freqlim,:),2),1)); %large errors
pwr_top(:,i_elect,3) = squeeze(mean(mean(c_pwr{1,i_elect}(:,freqlim,:),2),1)); %no target
end
clear ii i_elect
% Get difference in mean power at frequency band for each electrode
pwr_top_diff = NaN([length(times),length(elect_topplot),3]); %pre-allocate
for ii = 1:length(exp.singletrialselecs)
i_elect = exp.singletrialselecs(ii); %for doing only a selection of electrodes
pwr_top_diff(:,i_elect,1) = pwr_top(:,i_elect,1)-pwr_top(:,i_elect,2); %small-large
pwr_top_diff(:,i_elect,2) = pwr_top(:,i_elect,1)-pwr_top(:,i_elect,3); %small-no target
pwr_top_diff(:,i_elect,3) = pwr_top(:,i_elect,2)-pwr_top(:,i_elect,3); %large-no target
end
clear ii i_elect
CLims = [-0.8 0.8]; %set power scale of plot
colormap('jet')
nconds = 3; %number of plots
% conds = {'Accurate';'Guess';'No Target'}; %labels for plots
conds = {'Accurate-Guesses';'Accurate-No Target';'Guesses-No Target'}; %labels for plots
for tw_i = 1:length(tWin) %loop through several time windows
itWin = tWin{tw_i}; %select each time range if looping
%finds the times you want from the times variable
time_window = find(times>= itWin(1),1):find(times>= itWin(2),1)-1;
figure('Color',[1 1 1],'Position',[1 1 941 349]); %need extra big figure
for i_cond = 1:nconds %loop through conditions to make plot of each
subtightplot(1,3,i_cond,[0.02,0.02],[0.05,0.07],[0.05,0.05]);
set(gca,'Color',[1 1 1]);
temp = mean(pwr_top_diff(time_window,:,i_cond),1)'; %mean power within time window
temp(1) = NaN; %so M2 is not included
topoplot(temp,ALLEEG(1).chanlocs,'whitebk','on','plotrad',0.6,'maplimits',CLims,...
'plotchans',elect_topplot,'emarker',{'.','k',11,1})
title(conds{i_cond});
t = colorbar('peer',gca);
set(get(t,'ylabel'),'String', 'Power (dB)');
clear temp
end
% Overall subplot title
supertitle([num2str(freqband(1)) '-' num2str(freqband(2)) ' Hz: ' num2str(itWin(1)) ' to ' num2str(itWin(2)) ' ms'],...
'FontSize',10.5)
clear itWin time_window i_cond
end
clear tw_i nconds conds
clear freqlim CLims pwr_top pwr_top_diff
% #########################################################################
% -------------------------------------------------------------------------
% /////////////////////////////////////////////////////////////////////////
%% Separate response errors by power
% /////////////////////////////////////////////////////////////////////////
% -------------------------------------------------------------------------
% #########################################################################
% :::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
% Compute power in time and frequency windows
% :::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
clear current_power
%finds the frequencies you want
% freqband = [8 14]; %alpha
% freqband = [8 11]; %low alpha
% freqband = [10 14]; %high alpha
freqband = [3 8]; %theta
freqlim = find(freqs>=(freqband(1)-0.5) & freqs<=(freqband(2)+0.5));
%finds the times you want from the timess variable
% timewin = [-500 -300];
timewin = [-600 -400];
% timewin = [-400 -200];
% timewin = [-200 0];
% timewin = [-100 0];
% timewin = [0 200];
% timewin = [200 400];
% timewin = [400 600];
% timewin = [300 600];
timelim = find(times>=timewin(1) & times<=timewin(2));
% -------------------------------------------------------------------------
% Calculate power in time window at freq band
current_power = cell(length(exp.participants),length(exp.singletrialselecs)); %pre-allocate
for i_part = 1:length(exp.participants)
for ii = 1:5 %only central electrodes
% 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_ersp{i_part,i_elect}; %get single subject's ersp
part_ersp = all_erspN{i_part,i_elect}.trials; %get single subject's ersp
for i_trial = 1:(size(part_ersp,3))
% current_power{i_part,i_elect}(i_trial) = squeeze(mean(mean(abs(part_ersp(freqlim,timelim,i_trial)),1),2));
current_power{i_part,i_elect}(i_trial) = squeeze(mean(mean(part_ersp(freqlim,timelim,i_trial),1),2));
end
clear part_ersp i_trial
% plot trial power on a histogram
% figure; hist(current_power{i_part,i_elect},30)
% ylabel('Count');
% title(['Subj ' num2str(exp.participants{i_part}) '-' exp.singtrlelec_name{ii}])
end
end
clear i_elect i_part timelim
% :::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
% :::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::::
%% Separate trials based on median band power
errdeg_Hpwr = cell(length(exp.participants),length(exp.singletrialselecs)); %pre-allocate
errdeg_Lpwr = cell(length(exp.participants),length(exp.singletrialselecs)); %pre-allocate
pwr_Hpwr = cell(length(exp.participants),length(exp.singletrialselecs)); %pre-allocate
pwr_Lpwr = cell(length(exp.participants),length(exp.singletrialselecs)); %pre-allocate
for i_part = 1:length(exp.participants)
for ii = 1:5 %only central electrodes
% for ii = 1:length(exp.singletrialselecs)
i_elect = exp.singletrialselecs(ii); %for doing only a selection of electrodes
% Get trials by median power split
mdn_pwr = nanmedian(current_power{i_part,i_elect});
errdeg_Hpwr{i_part,i_elect} = resp_errdeg{i_part}(current_power{i_part,i_elect}>=mdn_pwr);
errdeg_Lpwr{i_part,i_elect} = resp_errdeg{i_part}(current_power{i_part,i_elect}<mdn_pwr);
% get high/low power on trials
pwr_Hpwr{i_part,i_elect} = current_power{i_part,i_elect}(current_power{i_part,i_elect}>=mdn_pwr);
pwr_Lpwr{i_part,i_elect} = current_power{i_part,i_elect}(current_power{i_part,i_elect}<mdn_pwr);
clear i_elect mdn_pwr
end
clear ii
end
clear i_part
% +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
%% Fit errors to model by electrode and subject
model_out_Hpwr = cell(length(exp.participants),length(exp.singletrialselecs)); %pre-allocate