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p4210_generatePlots.m
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% 10/22/2021 Makoto. Modified.
% 03/23/2021 Makoto. Modified.
% 03/15/2021 Makoto. Modified.
% 03/05/2021 Makoto. Used. Separated Sleep vs. Awake.
% 02/16/2021 Makoto. Sleep vs Awake separated. Box plot used.
% 02/08/2021 Makoto. Developed.
clear
load /data/mobi/Hiroki/p4200_select50subjects/summaryData
addpath /data/projects/makoto/Tools/RainCloudPlots-master/tutorial_matlab
addpath /data/mobi/Daisuke/code
%% 1. Histogram of ASR datapoint rejections.
% Obtain sleepAwakeIdx.
awakeIdx = find(pacGroupIdx==1 | pacGroupIdx==3);
sleepIdx = find(pacGroupIdx==2 | pacGroupIdx==4);
asrRejAwake = (1-subjectwiseCleaningStats(awakeIdx,1))*100;
asrRejSleep = (1-subjectwiseCleaningStats(sleepIdx,1))*100;
median(asrRejAwake) % 3.18 [0.89-6.78]
median(asrRejSleep) % 1.62 [0.33-7.00]
prctile(asrRejAwake, [25 75])
prctile(asrRejSleep, [25 75])
% Obtain sleep vs. awake num ICs rejected.
awakeAsrHist = histcounts(asrRejAwake, 0:1:40);
sleepAsrHist = histcounts(asrRejSleep, 0:1:40);
figure
barHandle = bar([awakeAsrHist;sleepAsrHist]', 'stacked');
barHandle(1).FaceColor = [1 0.76 0.66];
barHandle(2).FaceColor = [0.66 0.76 1];
barHandle(1).EdgeAlpha = 0.3;
barHandle(2).EdgeAlpha = 0.3;
title(sprintf('Histogram of rej. rate by ASR (Awake)\n(M=%.1f, SD=%.1f, range %.0f-%.1f)',...
mean(1-subjectwiseCleaningStats(:,1))*100, ...
std(1-subjectwiseCleaningStats(:,1))*100, ...
min(1-subjectwiseCleaningStats(:,1))*100, ...
max(1-subjectwiseCleaningStats(:,1))*100))
xlabel('Datapoint rej. rate (%)')
ylabel('Number of datasets')
xlim([0 40.5])
ylim([0 130])
axis square
legend({'Awake' 'Sleep'})
set(findall(gcf, '-property', 'fontsize'), 'fontsize', 24)
set(gcf, 'position', [1 2 1858 929])
print('/data/mobi/Hiroki/p4210_generatePlots/asrWindowRej', '-dsvg')
% figure
% histHandle = histogram([1-subjectwiseCleaningStats(:,1)]*100, 0:1:40, 'FaceColor', [0.66 0.76 1]);
% title(sprintf('ASR rejection rate (M=%.1f, SD=%.1f, range %.0f-%.1f)',...
% mean(1-subjectwiseCleaningStats(:,1))*100, ...
% std(1-subjectwiseCleaningStats(:,1))*100, ...
% min(1-subjectwiseCleaningStats(:,1))*100, ...
% max(1-subjectwiseCleaningStats(:,1))*100))
% xlabel('Datapoint rejection rate (%)')
% ylabel('Number of datasets')
% ylim([0 130])
% axis square
% set(findall(gcf, '-property', 'fontsize'), 'fontsize', 24)
% set(gcf, 'position', [1 2 1858 929])
% print('/data/mobi/Hiroki/p4210_generatePlots/asrWindowRej', '-dsvg')
%% 2. Number of ICs rejected.
% Obtain sleepAwakeIdx.
awakeIdx = find(pacGroupIdx==1 | pacGroupIdx==3);
sleepIdx = find(pacGroupIdx==2 | pacGroupIdx==4);
% Obtain sleep vs. awake num ICs rejected.
awakeHistClean1 = histcounts(21-subjectwiseCleaningStats(awakeIdx,3), 0:1:21);
sleepHistClean1 = histcounts(21-subjectwiseCleaningStats(sleepIdx,3), 0:1:21);
awakeHistClean2 = histcounts(21-subjectwiseCleaningStats(awakeIdx,4), 0:1:21);
sleepHistClean2 = histcounts(21-subjectwiseCleaningStats(sleepIdx,4), 0:1:21);
% Generate input data to the stacked bars.
figure
subplot(1,2,1)
barHandle = bar([awakeHistClean1;sleepHistClean1]', 'stacked');
barHandle(1).FaceColor = [1 0.76 0.66];
barHandle(2).FaceColor = [0.66 0.76 1];
title(sprintf('Clean 1:\nNumber of ICs rejected\nM=%.1f (SD=%.1f, range %.0f-%.1f)',...
mean(21-subjectwiseCleaningStats(:,3)), ...
std(21-subjectwiseCleaningStats(:,3)), ...
min(21-subjectwiseCleaningStats(:,3)), ...
max(21-subjectwiseCleaningStats(:,3))))
xlabel('Number of ICs rejected')
ylabel('Number of datasets')
axis square
xlim([0 21])
legend({'Awake' 'Sleep'})
subplot(1,2,2)
barHandle = bar([awakeHistClean2;sleepHistClean2]', 'stacked');
barHandle(1).FaceColor = [1 0.76 0.66];
barHandle(2).FaceColor = [0.66 0.76 1];
title(sprintf('Clean 2:\nNumber of ICs rejected\nM=%.1f (SD=%.1f, range %.0f-%.1f)',...
mean(21-subjectwiseCleaningStats(:,4)), ...
std(21-subjectwiseCleaningStats(:,4)), ...
min(21-subjectwiseCleaningStats(:,4)), ...
max(21-subjectwiseCleaningStats(:,4))))
xlabel('Number of ICs rejected')
ylabel('Number of datasets')
axis square
xlim([0 21])
set(findall(gcf, '-property', 'fontsize'), 'fontsize', 24)
set(gcf, 'position', [1 2 1858 929])
print('/data/mobi/Hiroki/p4210_generatePlots/numICsRej', '-dsvg')
% figure
% subplot(1,2,1)
% histHandle = histogram(21-subjectwiseCleaningStats(:,3), 0:1:21, 'FaceColor', [0.66 0.76 1]);
% title(sprintf('Cleaning Level 1:\nNumber of ICs rejected\nM=%.1f (SD=%.1f, range %.0f-%.1f)',...
% mean(21-subjectwiseCleaningStats(:,3)), ...
% std(21-subjectwiseCleaningStats(:,3)), ...
% min(21-subjectwiseCleaningStats(:,3)), ...
% max(21-subjectwiseCleaningStats(:,3))))
% xlabel('Number of ICs rejected')
% ylabel('Number of datasets')
% axis square
% xlim([1 21])
%
% subplot(1,2,2)
% histHandle = histogram(21-subjectwiseCleaningStats(:,4), 0:1:21, 'FaceColor', [0.66 0.76 1]);
% title(sprintf('Cleaning Level 2:\nNumber of ICs rejected\nM=%.1f (SD=%.1f, range %.0f-%.1f))',...
% mean(21-subjectwiseCleaningStats(:,4)), ...
% std(21-subjectwiseCleaningStats(:,4)), ...
% min(21-subjectwiseCleaningStats(:,4)), ...
% max(21-subjectwiseCleaningStats(:,4))))
% xlabel('Number of ICs')
% ylabel('Number of datasets')
% axis square
% set(findall(gcf, '-property', 'fontsize'), 'fontsize', 24)
% set(gcf, 'position', [1 2 1858 929])
% xlim([1 21])
% print('/data/mobi/Hiroki/p4210_generatePlots/numICsRej', '-dsvg')
%% 2.1 Number of ICs rejected redone.
% Obtain sleepAwakeIdx.
awakeIdx = find(pacGroupIdx==1 | pacGroupIdx==3);
sleepIdx = find(pacGroupIdx==2 | pacGroupIdx==4);
% Obtain sleep vs. awake num ICs rejected.
awakeHistClean1 = histcounts(21-subjectwiseCleaningStats(awakeIdx,3), 0:1:21);
sleepHistClean1 = histcounts(21-subjectwiseCleaningStats(sleepIdx,3), 0:1:21);
awakeHistClean2 = histcounts(21-subjectwiseCleaningStats(awakeIdx,4), 0:1:21);
sleepHistClean2 = histcounts(21-subjectwiseCleaningStats(sleepIdx,4), 0:1:21);
% Generate input data to the stacked bars.
figure
subplot(1,2,1)
barHandle1 = bar(awakeHistClean1);
hold on
barHandle2 = bar(sleepHistClean1);
barHandle1.FaceColor = [1 0.76 0.66];
barHandle2.FaceColor = [0.66 0.76 1];
barHandle1.FaceAlpha = 0.6;
barHandle2.FaceAlpha = 0.6;
title(sprintf('Clean 1:\nNumber of ICs rejected\nM=%.1f (SD=%.1f, range %.0f-%.1f)',...
mean(21-subjectwiseCleaningStats(:,3)), ...
std(21-subjectwiseCleaningStats(:,3)), ...
min(21-subjectwiseCleaningStats(:,3)), ...
max(21-subjectwiseCleaningStats(:,3))))
xlabel('Number of ICs rejected')
ylabel('Number of datasets')
axis square
xlim([0 21])
ylim([0 40])
legend({'Awake' 'Sleep'})
subplot(1,2,2)
barHandle1 = bar(awakeHistClean2);
hold on
barHandle2 = bar(sleepHistClean2);
barHandle1.FaceColor = [1 0.76 0.66];
barHandle2.FaceColor = [0.66 0.76 1];
barHandle1.FaceAlpha = 0.6;
barHandle2.FaceAlpha = 0.6;
title(sprintf('Clean 2:\nNumber of ICs rejected\nM=%.1f (SD=%.1f, range %.0f-%.1f)',...
mean(21-subjectwiseCleaningStats(:,4)), ...
std(21-subjectwiseCleaningStats(:,4)), ...
min(21-subjectwiseCleaningStats(:,4)), ...
max(21-subjectwiseCleaningStats(:,4))))
xlabel('Number of ICs rejected')
ylabel('Number of datasets')
axis square
xlim([0 21])
ylim([0 40])
legend({'Awake' 'Sleep'})
set(findall(gcf, '-property', 'fontsize'), 'fontsize', 24)
set(gcf, 'position', [1 2 1858 929])
print('/data/mobi/Hiroki/p4210_generatePlots/numICsRejSeparated', '-dsvg')
%% 3. Class label rate. subjIdx, icIdx, x, y, z, singleOrDouble, rv, dipoleDepth, classLabelProbability
% Obtain sleepAwakeIdx.
awakeIdx = find(pacGroupIdx==1 | pacGroupIdx==3);
sleepIdx = find(pacGroupIdx==2 | pacGroupIdx==4);
% Dimensions of icwiseData: subjIdx, icIdx, x, y, z, singleOrDouble, rv, dipoleDepth, classLabelProbability
subjIdx = icwiseData(:,1);
awakeIcIdx = find(ismember(subjIdx, awakeIdx));
sleepIcIdx = find(ismember(subjIdx, sleepIdx));
% For awake ICs.
[~,topLabelIdx] = max(icwiseData(awakeIcIdx,9:end), [], 2);
classLabelCounts = [sum(topLabelIdx==1) ...
sum(topLabelIdx==2) ...
sum(topLabelIdx==3) ...
sum(topLabelIdx==4) ...
sum(topLabelIdx==5) ...
sum(topLabelIdx==6) ...
sum(topLabelIdx==7)];
classLabelCountsSorted = sort(classLabelCounts, 'descend');
classLabelCountsSorted([2:7]) = classLabelCountsSorted([3 4 5 6 7 2]);
figure
subplot(1,2,1)
explode = [1 0 0 0 0 0 1];
pieHandle = pie(classLabelCountsSorted, explode);
% Obtain fancy colors from cbrewer.
addpath('/data/projects/makoto/Tools/cbrewer/cbrewer')
currentColormap = cbrewer('qual', 'Set3', 7);
currentColormap([2 7], :) = currentColormap([7 2], :);
currentColormap([2 6], :) = currentColormap([6 2], :);
currentColormap([2 4], :) = currentColormap([4 2], :);
currentColormap([3 4], :) = currentColormap([4 3], :);
set(pieHandle(1), 'FaceColor', currentColormap(1,:));
set(pieHandle(3), 'FaceColor', currentColormap(2,:));
set(pieHandle(5), 'FaceColor', currentColormap(3,:));
set(pieHandle(7), 'FaceColor', currentColormap(4,:));
set(pieHandle(9), 'FaceColor', currentColormap(5,:));
set(pieHandle(11), 'FaceColor', currentColormap(6,:));
set(pieHandle(13), 'FaceColor', currentColormap(7,:));
% For sleep ICs.
[~,topLabelIdx] = max(icwiseData(sleepIcIdx,9:end), [], 2);
classLabelCounts = [sum(topLabelIdx==1) ...
sum(topLabelIdx==2) ...
sum(topLabelIdx==3) ...
sum(topLabelIdx==4) ...
sum(topLabelIdx==5) ...
sum(topLabelIdx==6) ...
sum(topLabelIdx==7)];
classLabelCountsSorted = sort(classLabelCounts, 'descend');
classLabelCountsSorted([2:7]) = classLabelCountsSorted([3 4 5 6 7 2]);
subplot(1,2,2)
explode = [1 0 0 0 0 0 1];
pieHandle = pie(classLabelCountsSorted, explode);
% Obtain fancy colors from cbrewer.
addpath('/data/projects/makoto/Tools/cbrewer/cbrewer')
currentColormap = cbrewer('qual', 'Set3', 7);
currentColormap([2 7], :) = currentColormap([7 2], :);
currentColormap([2 6], :) = currentColormap([6 2], :);
currentColormap([2 4], :) = currentColormap([4 2], :);
currentColormap([3 4], :) = currentColormap([4 3], :);
set(pieHandle(1), 'FaceColor', currentColormap(1,:));
set(pieHandle(3), 'FaceColor', currentColormap(2,:));
set(pieHandle(5), 'FaceColor', currentColormap(3,:));
set(pieHandle(7), 'FaceColor', currentColormap(4,:));
set(pieHandle(9), 'FaceColor', currentColormap(5,:));
set(pieHandle(11), 'FaceColor', currentColormap(6,:));
set(pieHandle(13), 'FaceColor', currentColormap(7,:));
set(gcf, 'position', [5 533 895 398]);
%print('/data/mobi/Hiroki/p3200_forPoster/Figure3.jpg', '-r300', '-djpeg95')
print('/data/mobi/Hiroki/p4210_generatePlots/icLabelPieChart', '-dsvg')
% [~,topLabelIdx] = max(icwiseData(:,9:end), [], 2);
% classLabelCounts = [sum(topLabelIdx==1) ...
% sum(topLabelIdx==2) ...
% sum(topLabelIdx==3) ...
% sum(topLabelIdx==4) ...
% sum(topLabelIdx==5) ...
% sum(topLabelIdx==6) ...
% sum(topLabelIdx==7)];
%
% classLabelCountsSorted = sort(classLabelCounts, 'descend');
% classLabelCountsSorted([2:7]) = classLabelCountsSorted([3 4 5 6 7 2]);
%
% figure
% explode = [1 0 0 0 0 0 1];
% pieHandle = pie(classLabelCountsSorted, explode);
%
% % Obtain fancy colors from cbrewer.
% addpath('/data/projects/makoto/Tools/cbrewer/cbrewer')
% currentColormap = cbrewer('qual', 'Set3', 7);
%
% currentColormap([2 7], :) = currentColormap([7 2], :);
% currentColormap([2 6], :) = currentColormap([6 2], :);
% currentColormap([2 4], :) = currentColormap([4 2], :);
% currentColormap([3 4], :) = currentColormap([4 3], :);
%
%
% set(pieHandle(1), 'FaceColor', currentColormap(1,:));
% set(pieHandle(3), 'FaceColor', currentColormap(2,:));
% set(pieHandle(5), 'FaceColor', currentColormap(3,:));
% set(pieHandle(7), 'FaceColor', currentColormap(4,:));
% set(pieHandle(9), 'FaceColor', currentColormap(5,:));
% set(pieHandle(11), 'FaceColor', currentColormap(6,:));
% set(pieHandle(13), 'FaceColor', currentColormap(7,:));
%
% set(gcf, 'position', [5 278 818 653]);
% %print('/data/mobi/Hiroki/p3200_forPoster/Figure3.jpg', '-r300', '-djpeg95')
% print('/data/mobi/Hiroki/p4210_generatePlots/icLabelPieChart', '-dsvg')
%% 4. Diple depth for each class label for Level-1 IC cleaning.
% % Obtain Brain ic density.
% brainIdx = find(topLabelIdx==1);
% brainXyz = icwiseData(brainIdx, 3:5);
% brainDipoleDepth = icwiseData(brainIdx, 8);
%
% % Obtain muscle rejection rate.
% muscleIdx = find(topLabelIdx==2);
% muscleProb = icwiseData(muscleIdx,10);
% muscleRejRate = sum(muscleProb>0.8)/length(muscleProb);
% muscleXyz = icwiseData(muscleIdx(muscleProb>0.8), 3:5);
% muscleDipoleDepth = icwiseData(muscleIdx(muscleProb>0.8), 8);
%
% % Obtain eye rejection rate.
% eyeIdx = find(topLabelIdx==3);
% eyeProb = icwiseData(eyeIdx,11);
% eyeRejRate = sum(eyeProb>0.8)/length(eyeProb);
% eyeXyz = icwiseData(eyeIdx(eyeProb>0.8), 3:5);
% eyeDipoleDepth = icwiseData(eyeIdx(eyeProb>0.8), 8);
%
% mean(brainDipoleDepth)
% mean(muscleDipoleDepth)
% mean(eyeDipoleDepth)
%
% trimmedBrainDipDepth = brainDipoleDepth(brainDipoleDepth>prctile(brainDipoleDepth,1) & brainDipoleDepth<prctile(brainDipoleDepth,99));
% trimmedMuscleDipDepth = muscleDipoleDepth(muscleDipoleDepth>prctile(muscleDipoleDepth,1) & muscleDipoleDepth<prctile(muscleDipoleDepth,99));
% trimmedEyeDipDepth = eyeDipoleDepth(eyeDipoleDepth>prctile(eyeDipoleDepth,1) & eyeDipoleDepth<prctile(eyeDipoleDepth,99));
%
% figure
% subplot(1,3,1)
% hist(trimmedBrainDipDepth, 50)
% subplot(1,3,2)
% hist(trimmedMuscleDipDepth, 50)
% subplot(1,3,3)
% hist(trimmedEyeDipDepth, 50)
%
%
%
%
% % Obtain heart rejection rate.
% heartIdx = find(topLabelIdx==4);
% heartProb = icwiseData(heartIdx,12);
% heartRejRate = sum(heartProb>0.8)/length(heartProb);
% %heartXyz = icwiseData(heartIdx(heartProb>0.8), 3:5);
%
% % Obtain line noise rejection rate.
% lineNoiseIdx = find(topLabelIdx==5);
% lineNoiseProb = icwiseData(lineNoiseIdx,13);
% lineNoiseRejRate = sum(lineNoiseProb>0.8)/length(lineNoiseProb);
% %lineNoiseXyz = icwiseData(lineNoiseIdx(lineNoiseProb>0.8), 3:5);
%
%
% % Obtain channel noise rejection rate.
% channelNoiseIdx = find(topLabelIdx==6);
% channelNoiseProb = icwiseData(channelNoiseIdx,14);
% channelNoiseRejRate = sum(channelNoiseProb>0.8)/length(channelNoiseProb);
% % channelNoiseXyz = icwiseData(channelNoiseIdx(channelNoiseProb>0.8), 3:5);
%
% % Obtain Cleaning Level 1 num ICs reject.
% cleaningLevel1NumRejRate = (sum(muscleProb>0.8)+sum(eyeProb>0.8)+sum(heartProb>0.8)+sum(lineNoiseProb>0.8)+sum(channelNoiseProb>0.8))/...
% (sum(topLabelIdx==2)+sum(topLabelIdx==3)+sum(topLabelIdx==4)+sum(topLabelIdx==5)+sum(topLabelIdx==6));
%% 5. Plot dipole density for Muscle, Eye, and Brain.
% Obtain Brain ic density.
brainIdx = find(topLabelIdx==1);
brainXyz = icwiseData(brainIdx, 3:5);
brainDipoleDepth = icwiseData(brainIdx, 8);
% Obtain muscle rejection rate.
muscleIdx = find(topLabelIdx==2);
muscleXyz = icwiseData(muscleIdx, 3:5);
% Obtain eye rejection rate.
eyeIdx = find(topLabelIdx==3);
eyeXyz = icwiseData(eyeIdx, 3:5);
% Obtain heart rejection rate.
heartIdx = find(topLabelIdx==4);
heartXyz = icwiseData(heartIdx, 3:5);
plotDipoleDensity_axialSagittalCoronalTrio(brainXyz(brainDipoleDepth<1,:), 20)
colormap('jet')
set(gcf, 'position', [5 278 818 653], 'invertHardcopy', 'off')
print('/data/mobi/Hiroki/p4210_generatePlots/dipDensityBrain.jpg', '-r300', '-djpeg98')
plotDipoleDensity_axialSagittalCoronalTrio(muscleXyz, 20)
colormap('jet')
set(gcf, 'position', [5 278 818 653], 'invertHardcopy', 'off')
print('/data/mobi/Hiroki/p4210_generatePlots/dipDensityMuscle.jpg', '-r300', '-djpeg98')
plotDipoleDensity_axialSagittalCoronalTrio(eyeXyz, 20)
colormap('jet')
set(gcf, 'position', [5 278 818 653], 'invertHardcopy', 'off')
print('/data/mobi/Hiroki/p4210_generatePlots/dipDensityEye.jpg', '-r300', '-djpeg98')
plotDipoleDensity_axialSagittalCoronalTrio(heartXyz, 20)
colormap('jet')
set(gcf, 'position', [5 278 818 653], 'invertHardcopy', 'off')
print('/data/mobi/Hiroki/p4210_generatePlots/dipDensityHeart.jpg', '-r300', '-djpeg98')
colorbar
print('/data/mobi/Hiroki/p4210_generatePlots/colorbar.jpg', '-r300', '-djpeg98')
%% Separate between Awake and Sleep.
%%%%%%%%%%%%%%%%%%%%%%%%%%%
%%% Comparison for PSD. %%%
%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Obtain sleepAwakeIdx.
awakeIdx = find(pacGroupIdx==1 | pacGroupIdx==3);
sleepIdx = find(pacGroupIdx==2 | pacGroupIdx==4);
% Load dummy data.
EEG = pop_loadset('filename','AAB35.set','filepath','/data/mobi/Hiroki/p4000_import_preprocess_n600/', 'loadmode', 'info');
freqs = EEG.Hiroki.precleanedEEG.spectraFreqs;
deltaIdx = find(freqs <= 4);
thetaIdx = find(freqs > 4 & freqs <= 8);
alphaIdx = find(freqs > 8 & freqs <= 13);
betaIdx = find(freqs > 13 & freqs <= 30);
gammaIdx = find(freqs > 30);
%%%%%%%%%%%%%
%%% Awake %%%
%%%%%%%%%%%%%
psd_orig_mean = squeeze(mean(psd_orig(awakeIdx,:,:),1));
psd_asr_mean = squeeze(mean(psd_asr(awakeIdx,:,:),1));
psd_clean1_mean = squeeze(mean(psd_clean1(awakeIdx,:,:),1));
psd_clean2_mean = squeeze(mean(psd_clean2(awakeIdx,:,:),1));
figure
iterIdx = 0;
colorRange = [-12 12];
for cleaningIdx = 1:3
switch cleaningIdx
case 1
currentData = psd_asr_mean-psd_orig_mean;
case 2
currentData = psd_clean1_mean-psd_orig_mean;
case 3
currentData = psd_clean2_mean-psd_orig_mean;
end
for freqIdx = 1:5
switch freqIdx
case 1
currentFreqIdx = deltaIdx;
case 2
currentFreqIdx = thetaIdx;
case 3
currentFreqIdx = alphaIdx;
case 4
currentFreqIdx = betaIdx;
case 5
currentFreqIdx = gammaIdx;
end
iterIdx = iterIdx+1;
subplot(3,5,iterIdx)
topoplot(mean(currentData(:,currentFreqIdx),2), EEG.chanlocs, 'maplimits', colorRange)
if iterIdx == 5
currentPosition = get(gca, 'position')
colorbar
set(gca, 'position', currentPosition)
end
end
end
set(findall(gcf, '-property', 'fontsize'), 'fontsize', 20)
set(gcf, 'position', [1 2 1858 929])
print('/data/mobi/Hiroki/p4210_generatePlots/psdDiffForFreqBandsAwake.jpg', '-r300', '-djpeg98')
%%%%%%%%%%%%%
%%% Sleep %%%
%%%%%%%%%%%%%
psd_orig_mean = squeeze(mean(psd_orig(sleepIdx,:,:),1));
psd_asr_mean = squeeze(mean(psd_asr(sleepIdx,:,:),1));
psd_clean1_mean = squeeze(mean(psd_clean1(sleepIdx,:,:),1));
psd_clean2_mean = squeeze(mean(psd_clean2(sleepIdx,:,:),1));
figure
iterIdx = 0;
colorRange = [-12 12];
for cleaningIdx = 1:3
switch cleaningIdx
case 1
currentData = psd_asr_mean-psd_orig_mean;
case 2
currentData = psd_clean1_mean-psd_orig_mean;
case 3
currentData = psd_clean2_mean-psd_orig_mean;
end
for freqIdx = 1:5
switch freqIdx
case 1
currentFreqIdx = deltaIdx;
case 2
currentFreqIdx = thetaIdx;
case 3
currentFreqIdx = alphaIdx;
case 4
currentFreqIdx = betaIdx;
case 5
currentFreqIdx = gammaIdx;
end
iterIdx = iterIdx+1;
subplot(3,5,iterIdx)
topoplot(mean(currentData(:,currentFreqIdx),2), EEG.chanlocs, 'maplimits', colorRange)
if iterIdx == 5
currentPosition = get(gca, 'position')
colorbar
set(gca, 'position', currentPosition)
end
end
end
set(findall(gcf, '-property', 'fontsize'), 'fontsize', 20)
set(gcf, 'position', [1 2 1858 929])
print('/data/mobi/Hiroki/p4210_generatePlots/psdDiffForFreqBandsSleep.jpg', '-r300', '-djpeg98')
% figure
% raincloud_plot(1-subjectwiseCleaningStats(:,1), 'box_on', 1, 'box_dodge', 1, 'box_dodge_amount', 0.175, ...
% 'color', [0.66 0.76 1], 'line_width', 1, 'lwr_bnd', 0.5)
%
% figure
% inputData = {subjectwiseCleaningStats(:,2) subjectwiseCleaningStats(:,3) subjectwiseCleaningStats(:,4)};
% rm_raincloud(inputData, [1 0 0; 0 1 0; 0 0 1], 1, 'ks')
% %%%%%%%%%%%%%%%%%%%%%%%%%%%
% %%% Comparison for PSD. %%%
% %%%%%%%%%%%%%%%%%%%%%%%%%%%
% % Load dummy data.
% EEG = pop_loadset('filename','AAB35.set','filepath','/data/mobi/Hiroki/p4000_import_preprocess_n600/', 'loadmode', 'info');
%
% freqs = EEG.Hiroki.precleanedEEG.spectraFreqs;
% deltaIdx = find(freqs <= 4);
% thetaIdx = find(freqs > 4 & freqs <= 8);
% alphaIdx = find(freqs > 8 & freqs <= 13);
% betaIdx = find(freqs > 13 & freqs <= 30);
% gammaIdx = find(freqs > 30);
%
% psd_orig_mean = squeeze(mean(psd_orig,1));
% psd_asr_mean = squeeze(mean(psd_asr,1));
% psd_clean1_mean = squeeze(mean(psd_clean1,1));
% psd_clean2_mean = squeeze(mean(psd_clean2,1));
%
% figure
% iterIdx = 0;
% colorRange = [-4 4];
% for cleaningIdx = 1:3
% switch cleaningIdx
% case 1
% currentData = psd_asr_mean-psd_orig_mean;
% case 2
% currentData = psd_clean1_mean-psd_orig_mean;
% case 3
% currentData = psd_clean2_mean-psd_orig_mean;
% end
%
% for freqIdx = 1:5
% switch freqIdx
% case 1
% currentFreqIdx = deltaIdx;
% case 2
% currentFreqIdx = thetaIdx;
% case 3
% currentFreqIdx = alphaIdx;
% case 4
% currentFreqIdx = betaIdx;
% case 5
% currentFreqIdx = gammaIdx;
% end
%
% iterIdx = iterIdx+1;
% subplot(3,5,iterIdx)
%
% topoplot(mean(currentData(:,currentFreqIdx),2), EEG.chanlocs, 'maplimits', colorRange)
%
% if iterIdx == 5
% currentPosition = get(gca, 'position')
% colorbar
% set(gca, 'position', currentPosition)
% end
% end
% end
%
% set(findall(gcf, '-property', 'fontsize'), 'fontsize', 20)
% set(gcf, 'position', [1 2 1858 929])
% print('/data/mobi/Hiroki/p4210_generatePlots/psdDiffForFreqBands.jpg', '-r300', '-djpeg98')
%
% % figure
% % raincloud_plot(1-subjectwiseCleaningStats(:,1), 'box_on', 1, 'box_dodge', 1, 'box_dodge_amount', 0.175, ...
% % 'color', [0.66 0.76 1], 'line_width', 1, 'lwr_bnd', 0.5)
% %
% % figure
% % inputData = {subjectwiseCleaningStats(:,2) subjectwiseCleaningStats(:,3) subjectwiseCleaningStats(:,4)};
% % rm_raincloud(inputData, [1 0 0; 0 1 0; 0 0 1], 1, 'ks')
%% Generate MI plots with bar graphs (original)
% Load dummy data.
EEG = pop_loadset('filename','AAB35.set','filepath','/data/mobi/Hiroki/p4000_import_preprocess_n600/', 'loadmode', 'info');
chanLabels = {EEG.chanlocs.labels}';
% Determine trimming threshold.
trimmingProb = 0.05;
% Loop for awake and sleep.
for sleepAwakeIdx = 1:2
switch sleepAwakeIdx
case 1
currentSubjIdx = find(pacGroupIdx==1 | pacGroupIdx==3);
currentSubjData = pacTensor(currentSubjIdx,:,:);
case 2
currentSubjIdx = find(pacGroupIdx==2 | pacGroupIdx==4);
currentSubjData = pacTensor(currentSubjIdx,:,:);
end
sortedData = sort(currentSubjData,1,'descend');
trimmedData = sortedData(round(size(sortedData,1)*trimmingProb)+1:end,:,:);
figure
subplot(2,3,1)
topoplot(squeeze(mean(zscore(trimmedData(:,:,1),0,2))), EEG.chanlocs, 'conv', 'off', 'maplimits', [-3 3])
subplot(2,3,2)
topoplot(squeeze(mean(zscore(trimmedData(:,:,2),0,2))), EEG.chanlocs, 'conv', 'off', 'maplimits', [-3 3])
subplot(2,3,3)
topoplot(squeeze(mean(zscore(trimmedData(:,:,3),0,2))), EEG.chanlocs, 'conv', 'off', 'maplimits', [-3 3])
subplot(2,3,4)
bar(mean(trimmedData(:,:,1),1), 'FaceColor', [0.66 0.76 1]);
set(gca, 'xtick', [1:21], 'xticklabel', chanLabels)
hold on
errorbar(1:21, mean(trimmedData(:,:,1),1), zeros(size(trimmedData(:,:,1),2),1), std(trimmedData(:,:,1),0,1), 'color', [0 0 0], 'linestyle', 'none')
ylabel('Canolty''s MI')
title('Without cleaning, Mean+/-SD')
switch sleepAwakeIdx
case 1
ylim([0 10])
case 2
ylim([0 4.5])
end
subplot(2,3,5)
bar(mean(trimmedData(:,:,2),1), 'FaceColor', [0.66 0.76 1]);
set(gca, 'xtick', [1:21], 'xticklabel', chanLabels)
hold on
errorbar(1:21, mean(trimmedData(:,:,2),1), zeros(size(trimmedData(:,:,2),2),1), std(trimmedData(:,:,2),0,1), 'color', [0 0 0], 'linestyle', 'none')
ylabel('Canolty''s MI')
title('ICrej 1, Mean+/-SD')
switch sleepAwakeIdx
case 1
ylim([0 10])
case 2
ylim([0 4.5])
end
subplot(2,3,6)
bar(mean(trimmedData(:,:,3),1), 'FaceColor', [0.66 0.76 1]);
set(gca, 'xtick', [1:21], 'xticklabel', chanLabels)
hold on
errorbar(1:21, mean(trimmedData(:,:,3),1), zeros(size(trimmedData(:,:,3),2),1), std(trimmedData(:,:,3),0,1), 'color', [0 0 0], 'linestyle', 'none')
ylabel('Canolty''s MI')
title('ICrej 2, Mean+/-SD')
switch sleepAwakeIdx
case 1
ylim([0 10])
case 2
ylim([0 4.5])
end
% Print.
set(gcf, 'position', [1 2 1858 929])
print(sprintf('/data/mobi/Hiroki/p4210_generatePlots/miTopo_%d', sleepAwakeIdx), '-dsvg')
end
%% Generate MI plots with box plots (median+quartiles, 02/16/2021; re-visited 03/15/2021)
clear
% Load summary data.
load /data/mobi/Hiroki/p4200_select50subjects/summaryData
% Load dummy data.
EEG = pop_loadset('filename','AAB35.set','filepath','/data/mobi/Hiroki/p4000_import_preprocess_n600/', 'loadmode', 'info');
chanLabels = {EEG.chanlocs.labels}';
% Determine trimming threshold. pacTensor(:,:,1:3) are MI, pacTensor(:,:,4:6) are RVL.
% trimmingProb = 0.05;
% Loop for awake and sleep.
for sleepAwakeIdx = 1:2
if sleepAwakeIdx == 1
maplimits = [-3 3];
yLim = [0 16.5];
dataLim = [0 16];
else
maplimits = [-1.5 1.5]
yLim = [0 8.25];
dataLim = [0 8];
end
% Average Time 1 and Time 2.
switch sleepAwakeIdx
case 1
currentSubjIdx1 = find(pacGroupIdx==1);
currentSubjIdx3 = find(pacGroupIdx==3);
currentSubjData = (pacTensor(currentSubjIdx1,:,:) + pacTensor(currentSubjIdx3,:,:))/2;
case 2
currentSubjIdx2 = find(pacGroupIdx==2);
currentSubjIdx4 = find(pacGroupIdx==4);
currentSubjData = (pacTensor(currentSubjIdx2,:,:) + pacTensor(currentSubjIdx4,:,:))/2;
end
% sortedData = sort(currentSubjData,1,'descend');
% trimmedData = sortedData(round(size(sortedData,1)*trimmingProb)+1:end,:,:);
figure
subplot(2,3,1)
topoplot(squeeze(median(currentSubjData(:,1:19,1))), EEG.chanlocs(1:19), 'conv', 'off', 'maplimits', maplimits)
subplot(2,3,2)
topoplot(squeeze(median(currentSubjData(:,1:19,2))), EEG.chanlocs(1:19), 'conv', 'off', 'maplimits', maplimits)
subplot(2,3,3)
topoplot(squeeze(median(currentSubjData(:,1:19,3))), EEG.chanlocs(1:19), 'conv', 'off', 'maplimits', maplimits)
subplot(2,3,4)
boxplot(currentSubjData(:,1:19,1), 'datalim', dataLim, 'width', 1, 'jitter', 0.25, 'plotstyle', 'compact')
outlierTags = findobj(gca, 'tag', 'Outliers');
set(outlierTags, 'Marker', '.', 'markerEdgeColor', [0.5 0.5 1], 'markersize', 8)
ylabel('Canolty''s MI')
title('Clean 0, Median with quartiles')
set(gca, 'xtick', 1:19, 'xticklabel', chanLabels)
ylim(yLim)
subplot(2,3,5)
boxplot(currentSubjData(:,1:19,2), 'datalim', dataLim, 'width', 1, 'jitter', 0.25, 'plotstyle', 'compact')
outlierTags = findobj(gca, 'tag', 'Outliers');
set(outlierTags, 'Marker', '.', 'markerEdgeColor', [0.5 0.5 1], 'markersize', 8)
ylabel('Canolty''s MI')
title('Clean 1, with quartiles')
set(gca, 'xtick', 1:19, 'xticklabel', chanLabels)
ylim(yLim)
subplot(2,3,6)
boxplot(currentSubjData(:,1:19,3), 'datalim', dataLim, 'width', 1, 'jitter', 0.25, 'plotstyle', 'compact')
outlierTags = findobj(gca, 'tag', 'Outliers');
set(outlierTags, 'Marker', '.', 'markerEdgeColor', [0.5 0.5 1], 'markersize', 8)
ylabel('Canolty''s MI')
title('Clean 2, with quartiles')
set(gca, 'xtick', 1:19, 'xticklabel', chanLabels)
ylim(yLim)
% Print.
set(gcf, 'position', [1 2 1858 929])
print(sprintf('/data/mobi/Hiroki/p4210_generatePlots/miTopoBoxPlot_%d', sleepAwakeIdx), '-dsvg')
end
% Calculate stats.
currentSubjIdx1 = find(pacGroupIdx==1);
currentSubjIdx3 = find(pacGroupIdx==3);
awakeData = (pacTensor(currentSubjIdx1,:,:) + pacTensor(currentSubjIdx3,:,:))/2;
currentSubjIdx2 = find(pacGroupIdx==2);
currentSubjIdx4 = find(pacGroupIdx==4);
sleepData = (pacTensor(currentSubjIdx2,:,:) + pacTensor(currentSubjIdx4,:,:))/2;
sortedDataAwake = sort(awakeData,1,'descend');
trimmedDataAwake = sortedDataAwake(round(size(sortedDataAwake,1)*trimmingProb)+1:end,:,:);
sortedDataSleep = sort(sleepData,1,'descend');
trimmedDataSleep = sortedDataSleep(round(size(sortedDataSleep,1)*trimmingProb)+1:end,:,:);
[H1,P1,CI1,STATS1] = ttest2(mean(trimmedDataAwake(:,:,2),2), mean(trimmedDataAwake(:,:,1),2));
[H2,P2,CI2,STATS2] = ttest2(mean(trimmedDataAwake(:,:,3),2), mean(trimmedDataAwake(:,:,1),2));
[H3,P3,CI3,STATS3] = ttest2(mean(trimmedDataSleep(:,:,2),2), mean(trimmedDataSleep(:,:,1),2));
[H4,P4,CI4,STATS4] = ttest2(mean(trimmedDataSleep(:,:,3),2), mean(trimmedDataSleep(:,:,1),2));
% clear
%
% % Load summary data.
% load /data/mobi/Hiroki/p4200_select50subjects/summaryData
%
% % Load dummy data.
% EEG = pop_loadset('filename','AAB35.set','filepath','/data/mobi/Hiroki/p4000_import_preprocess_n600/', 'loadmode', 'info');
% chanLabels = {EEG.chanlocs.labels}';
%
% % Determine trimming threshold. pacTensor(:,:,1:3) are MI, pacTensor(:,:,4:6) are RVL.
% trimmingProb = 0.05;
%
% % Loop for awake and sleep.
% for sleepAwakeIdx = 1:2
%
% % Average Time 1 and Time 2.
% switch sleepAwakeIdx
% case 1
% currentSubjIdx1 = find(pacGroupIdx==1);
% currentSubjIdx3 = find(pacGroupIdx==3);
% currentSubjData = (pacTensor(currentSubjIdx1,:,:) + pacTensor(currentSubjIdx3,:,:))/2;
% case 2
% currentSubjIdx2 = find(pacGroupIdx==2);
% currentSubjIdx4 = find(pacGroupIdx==4);
% currentSubjData = (pacTensor(currentSubjIdx2,:,:) + pacTensor(currentSubjIdx4,:,:))/2;
% end
%
% sortedData = sort(currentSubjData,1,'descend');
% trimmedData = sortedData(round(size(sortedData,1)*trimmingProb)+1:end,:,:);
%
% figure
% subplot(2,3,1)
% topoplot(squeeze(mean(zscore(trimmedData(:,:,1),0,2))), EEG.chanlocs, 'conv', 'off', 'maplimits', [-3 3])
% subplot(2,3,2)
% topoplot(squeeze(mean(zscore(trimmedData(:,:,2),0,2))), EEG.chanlocs, 'conv', 'off', 'maplimits', [-3 3])
% subplot(2,3,3)
% topoplot(squeeze(mean(zscore(trimmedData(:,:,3),0,2))), EEG.chanlocs, 'conv', 'off', 'maplimits', [-3 3])
%
% subplot(2,3,4)
% boxplot(currentSubjData(:,:,1), 'datalim', [0 16], 'width', 1, 'jitter', 0.25, 'plotstyle', 'compact')
% outlierTags = findobj(gca, 'tag', 'Outliers');
% set(outlierTags, 'Marker', '.', 'markerEdgeColor', [0.5 0.5 1], 'markersize', 8)
% ylabel('Canolty''s MI')
% title('Cleaning Level 0, Mean+/-SD')
% set(gca, 'xtick', 1:21, 'xticklabel', chanLabels)
% ylim([0 16.5])
%
% subplot(2,3,5)
% boxplot(currentSubjData(:,:,2), 'datalim', [0 16], 'width', 1, 'jitter', 0.25, 'plotstyle', 'compact')
% outlierTags = findobj(gca, 'tag', 'Outliers');
% set(outlierTags, 'Marker', '.', 'markerEdgeColor', [0.5 0.5 1], 'markersize', 8)
% ylabel('Canolty''s MI')
% title('Cleaning Level 1, Mean+/-SD')
% set(gca, 'xtick', 1:21, 'xticklabel', chanLabels)
% ylim([0 16.5])
%
% subplot(2,3,6)
% boxplot(currentSubjData(:,:,3), 'datalim', [0 16], 'width', 1, 'jitter', 0.25, 'plotstyle', 'compact')
% outlierTags = findobj(gca, 'tag', 'Outliers');
% set(outlierTags, 'Marker', '.', 'markerEdgeColor', [0.5 0.5 1], 'markersize', 8)
% ylabel('Canolty''s MI')
% title('Cleaning Level 2, Mean+/-SD')
% set(gca, 'xtick', 1:21, 'xticklabel', chanLabels)
% ylim([0 16.5])
%
% % Print.
% set(gcf, 'position', [1 2 1858 929])
% print(sprintf('/data/mobi/Hiroki/p4210_generatePlots/miTopoBoxPlot_%d', sleepAwakeIdx), '-dsvg')
% end
%
%
% % Calculate stats.
% currentSubjIdx1 = find(pacGroupIdx==1);
% currentSubjIdx3 = find(pacGroupIdx==3);
% awakeData = (pacTensor(currentSubjIdx1,:,:) + pacTensor(currentSubjIdx3,:,:))/2;
%
% currentSubjIdx2 = find(pacGroupIdx==2);
% currentSubjIdx4 = find(pacGroupIdx==4);
% sleepData = (pacTensor(currentSubjIdx2,:,:) + pacTensor(currentSubjIdx4,:,:))/2;
%
% sortedDataAwake = sort(awakeData,1,'descend');
% trimmedDataAwake = sortedDataAwake(round(size(sortedDataAwake,1)*trimmingProb)+1:end,:,:);
% sortedDataSleep = sort(sleepData,1,'descend');
% trimmedDataSleep = sortedDataSleep(round(size(sortedDataSleep,1)*trimmingProb)+1:end,:,:);
%
% [H1,P1,CI1,STATS1] = ttest2(mean(trimmedDataAwake(:,:,2),2), mean(trimmedDataAwake(:,:,1),2));
% [H2,P2,CI2,STATS2] = ttest2(mean(trimmedDataAwake(:,:,3),2), mean(trimmedDataAwake(:,:,1),2));
%
% [H3,P3,CI3,STATS3] = ttest2(mean(trimmedDataSleep(:,:,2),2), mean(trimmedDataSleep(:,:,1),2));
% [H4,P4,CI4,STATS4] = ttest2(mean(trimmedDataSleep(:,:,3),2), mean(trimmedDataSleep(:,:,1),2));
%%
% MI.
% inputData = {squeeze(miRvlTensor(1,1,:)) ...
% squeeze(miRvlTensor(3,1,:))};
% inputData = {squeeze(miRvlTensor(1,3,:)) ...
% squeeze(miRvlTensor(3,3,:))};
% inputData = {squeeze(miRvlTensor(2,1,:)) ...
% squeeze(miRvlTensor(4,1,:))};
inputData = {squeeze(miRvlTensor(2,3,:)) ...
squeeze(miRvlTensor(4,3,:))};
% Apply trimming.
for loopIdx = 1:length(inputData)
currentData = inputData{loopIdx};
trimmedData = currentData(currentData<prctile(currentData,90));
inputData{loopIdx} = trimmedData;
end
% Obtain fancy colors from cbrewer.
addpath('/data/projects/makoto/Tools/cbrewer/cbrewer')
currentColorMap = cbrewer('qual', 'Set3', 10);
% tmp = 1:10;
% figure; imagesc(tmp)
% colormap(currentColormap)
currentColorMap = currentColorMap(4:5,:);
figure
rm_raincloud(inputData, currentColorMap, 1, 'ks')
figure
inputData = {squeeze(miRvlTensor(1,1,:)) ...
squeeze(miRvlTensor(2,1,:)) ...
squeeze(miRvlTensor(3,1,:)) ...
squeeze(miRvlTensor(4,1,:)) ...
squeeze(miRvlTensor(1,2,:)) ...
squeeze(miRvlTensor(2,2,:)) ...
squeeze(miRvlTensor(3,2,:)) ...
squeeze(miRvlTensor(4,2,:)) ...
squeeze(miRvlTensor(1,3,:)) ...
squeeze(miRvlTensor(2,3,:)) ...
squeeze(miRvlTensor(3,3,:)) ...
squeeze(miRvlTensor(4,3,:))};
% Obtain fancy colors from cbrewer.
addpath('/data/projects/makoto/Tools/cbrewer/cbrewer')
currentColormap = cbrewer('qual', 'Set3', 12);
%% Calculate state-separate asrVarRej and numIcRej