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efc1_main.m
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efc1_main.m
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%% Loading and initialization
clear;
close all;
clc;
% setting paths:
usr_path = userpath;
usr_path = usr_path(1:end-17);
cd(fullfile(usr_path, 'Desktop','Projects','EFC1'));
addpath(fullfile(usr_path, 'Desktop','Projects','EFC1','functions'));
addpath(genpath(fullfile(usr_path,'Desktop','matlab','dataframe-2016.1')),'-begin')
analysisDir = fullfile(usr_path,'Desktop', 'Projects', 'EFC1', 'analysis');
% analysis playground
data = dload(fullfile(analysisDir, 'efc1_all.tsv'));
% Create a figure or specify an existing figure handle:
h = figure;
% Get the default MATLAB colors:
colors = get(h, 'DefaultAxesColorOrder');
% Close the figure:
close(h);
% get subject numbers:
subjects = unique(data.sn);
% meanDev vs run
figure;
y_avg = 0;
for i = 1:length(subjects)
rows = data.trialCorr==1 & data.sn==subjects(i);
[x_coord,PLOT,ERROR] = lineplot(data.BN(rows), data.mean_dev(rows), 'linecolor', colors(i,:));
y_avg = y_avg + PLOT/length(subjects);
hold on
end
plot(x_coord,y_avg,'k','LineWidth',2)
title('MeanDev vs Run')
% movement time vs run
figure;
y_avg = 0;
for i = 1:length(subjects)
rows = data.trialCorr==1 & data.sn==subjects(i);
[x_coord,PLOT,ERROR] = lineplot(data.BN(rows), data.MT(rows), 'linecolor', colors(i,:));
y_avg = y_avg + PLOT/length(subjects);
hold on
end
plot(x_coord,y_avg,'k','LineWidth',2)
title('MT vs Run')
% RT vs run:
figure;
y_avg = 0;
for i = 1:length(subjects)
rows = data.trialCorr==1 & data.sn==subjects(i);
[x_coord,PLOT,ERROR] = lineplot(data.BN(rows), data.RT(rows), 'linecolor', colors(i,:));
y_avg = y_avg + PLOT/length(subjects);
hold on
end
plot(x_coord,y_avg,'k','LineWidth',2)
title('RT vs Run')
% mean_dev avg in each 12 runs , num finger separated:
figure;
n = get_num_active_fingers(data.chordID);
for j = 1:5
subplot(1,5,j)
for i = 1:length(subjects)
sess = [0,12,24,36,48];
sess_vec = [ones(1210,1) ; 2*ones(1210,1) ; 3*ones(1210,1) ; 4*ones(1210,1)];
sess_vec = repmat(sess_vec,length(subjects),1);
row = data.trialCorr==1 & data.sn==subjects(i) & n==j;
lineplot(sess_vec(row), data.mean_dev(row) ...
,'linecolor', colors(i,:), 'errorbars', '')
hold on;
end
lineplot(sess_vec(n==j), data.mean_dev(n==j) ...
,'linecolor', [0,0,0], 'linewidth', 4)
ylabel('avg meanDev')
xlabel('session number')
title(sprintf('n_{active} = %d',j))
ylim([0.2,3])
end
% MT avg in each 12 runs , num fingers separated:
figure;
n = get_num_active_fingers(data.chordID);
for j = 1:5
subplot(1,5,j)
for i = 1:length(subjects)
sess = [0,12,24,36,48];
sess_vec = [ones(1210,1) ; 2*ones(1210,1) ; 3*ones(1210,1) ; 4*ones(1210,1)];
sess_vec = repmat(sess_vec,length(subjects),1);
row = data.trialCorr==1 & data.sn==subjects(i) & n==j;
lineplot(sess_vec(row), data.MT(row) ...
,'linecolor', colors(i,:), 'errorbars', '')
hold on;
end
lineplot(sess_vec(n==j), data.MT(n==j) ...
,'linecolor', [0,0,0], 'linewidth', 4)
ylabel('avg MT')
xlabel('session number')
title(sprintf('n_{active} = %d',j))
ylim([500,3500])
end
% RT avg in each 12 runs , num fingers separated:
figure;
n = get_num_active_fingers(data.chordID);
for j = 1:5
subplot(1,5,j)
for i = 1:length(subjects)
sess = [0,12,24,36,48];
sess_vec = [ones(1210,1) ; 2*ones(1210,1) ; 3*ones(1210,1) ; 4*ones(1210,1)];
sess_vec = repmat(sess_vec,length(subjects),1);
row = data.trialCorr==1 & data.sn==subjects(i) & n==j;
lineplot(sess_vec(row), data.RT(row) ...
,'linecolor', colors(i,:), 'errorbars', '')
hold on;
end
lineplot(sess_vec(n==j), data.RT(n==j) ...
,'linecolor', [0,0,0], 'linewidth', 4)
ylabel('avg RT')
xlabel('session number')
title(sprintf('n_{active} = %d',j))
ylim([50,550])
end
%% analysis
clc;
clearvars -except data
close all;
% global params:
corrMethod = 'pearson';
includeSubjAvgModel = 0;
% theta calc params:
onlyActiveFing = 0;
firstTrial = 2;
selectRun = -2;
durAfterActive = 200;
clim = [0,1];
% medRT params:
excludeChord = [];
% ====DATA PREP====
% efc1_analyze('all_subj'); % makes the .mat files from .dat and .mov of each subject
% ====ANALISYS====
efc1_analyze('RT_vs_run',data,'plotfcn','median');
% rho_medRT_WithinSubject = efc1_analyze('corr_within_subj_runs',data,'corrMethod',corrMethod,'excludeChord',excludeChord);
%
% rho_medRT_acrossSubj = efc1_analyze('corr_across_subj',data,'plotfcn',1,'clim',clim,'corrMethod',corrMethod,'excludeChord',excludeChord);
%
% rho_medRT_AvgModel = efc1_analyze('corr_medRT_avg_model',data,'corrMethod',corrMethod,'excludeChord',excludeChord,'includeSubj',includeSubjAvgModel);
%
% thetaCell = efc1_analyze('thetaExp_vs_thetaStd',data,'durAfterActive',durAfterActive,'plotfcn',0,...
% 'firstTrial',firstTrial,'onlyActiveFing',onlyActiveFing,'selectRun',selectRun);
%
% rho_theta_acrossSubj = efc1_analyze('corr_mean_theta_across_subj',data,'thetaCell',thetaCell,'onlyActiveFing',onlyActiveFing, ...
% 'firstTrial',firstTrial,'corrMethod',corrMethod,'plotfcn',1,'clim',clim);
%
% rho_theta_avgModel = efc1_analyze('corr_mean_theta_avg_model',data,'thetaCell',thetaCell,'onlyActiveFing',onlyActiveFing, ...
% 'firstTrial',firstTrial,'corrMethod',corrMethod,'includeSubj',includeSubjAvgModel);
%
% biasVarCell = efc1_analyze('theta_bias',data,'durAfterActive',durAfterActive,'selectRun',selectRun,...
% 'firstTrial',firstTrial,'plotfcn',0);
[meanDevCell,rho_meanDev_acrossSubj] = efc1_analyze('meanDev',data,'selectRun',selectRun,...
'corrMethod',corrMethod,'plotfcn',1,'clim',clim);
% rho_meanDev_avgModel = efc1_analyze('corr_meanDev_avg_model',data,'selectRun',selectRun,'corrMethod',corrMethod,...
% 'includeSubj',includeSubjAvgModel);
% [rho_OLS_medRT, crossValModels_medRT, singleSubjModel_medRT] = efc1_analyze('reg_OLS_medRT',data,...
% 'regSubjNum',0,'excludeChord',excludeChord,'corrMethod',corrMethod);
% [rho_OLS_meanTheta, crossValModels_meanTheta, singleSubjModel_meanTheta] = efc1_analyze('reg_OLS_meanTheta',data,...
% thetaCell,'regSubjNum',0,'corrMethod',corrMethod,'onlyActiveFing',onlyActiveFing,'firstTrial',firstTrial);
% efc1_analyze('plot_scatter_within_subj',data,'transform_type','ranked')
% efc1_analyze('plot_scatter_across_subj',data,'transform_type','no_transform')
% efc1_analyze('meanTheta_scatter_across_subj',data,thetaCell,'onlyActiveFing',onlyActiveFing,'firstTrial',firstTrial
%% ========================================================================
%% TEMPORARY CODES AND ANALYSIS FROM HERE !<UNDER CONSTRUCTION>!
%% ========================================================================
%% Cognitive modelling of difficulty
clc;
close all;
clearvars -except data
%% MD bias check
clc;
close all;
clearvars -except data
% global params:
corrMethod = 'pearson';
selectRun = -2;
clim = [0,1];
[meanDevCell,rho_meanDev_acrossSubj] = efc1_analyze('meanDev',data,'selectRun',selectRun,...
'corrMethod',corrMethod,'plotfcn',0,'clim',clim);
chordVec = generateAllChords();
chordVecSep = sepChordVec(chordVec);
% container for mean MD:
meanMD_container = [];
% going through subjects and extracting mean devs of chordIdx:
for subj = 1:size(meanDevCell,1)
% calculating the mean MD for each chord of each subject:
tmp = [meanDevCell{subj,1}(:,2)];
meanMD = cellfun(@(x) mean(x,'all'), tmp);
% adding the trial avg to the container as columns
meanMD_container = [meanMD meanMD_container];
end
% going through chord groups (numActFing) and calculating the correlation:
subjCorrs = cell(size(chordVecSep,1),1);
figure;
for i = 1:size(chordVecSep,1)
% getting the indices of the chord group:
chordIdx = chordVecSep{i,2};
% extracting the meanMD for selected chords:
tmpMD = meanMD_container(chordIdx,:);
% calculating the correlations between subjects:
corrTmp = corrcoef(tmpMD);
subjCorrs{i} = unique(corrTmp(corrTmp ~= 1));
% making an avg subject:
avgSubj = mean(tmpMD,2);
% calculating correlation of each subj with the avgSubj - cross validated:
corrAvgSubj = zeros(1,size(tmpMD,2));
for j = 1:size(tmpMD,2)
one_out = tmpMD(:,j);
others = tmpMD(:,setdiff(1:size(tmpMD,2),j));
avgSubj = mean(others,2);
corrTmp = corrcoef(avgSubj,one_out);
corrAvgSubj(j) = corrTmp(1,2);
end
% corrAvgSubj = corrcoef([avgSubj tmpMD]);
% corrAvgSubj = corrAvgSubj(2,1:end-1)
mean_corr_avg_subj = mean(corrAvgSubj);
sem_corr_avg_subj = std(corrAvgSubj)/sqrt(length(corrAvgSubj));
% scatter plot of the mean_corr across num act finger:
errorbar(i,mean_corr_avg_subj,sem_corr_avg_subj, 'k', 'LineStyle','none')
hold on
scatter(i,mean_corr_avg_subj,'k','filled')
xlim([0 6])
ylabel("mean correlation with avg subject - cross validated")
xlabel("num active finger")
title("Mean Deviation noise bias")
% color plot of correlations to compare across num act finger:
% figure;
% imagesc(corrTmp, [0,1])
% colorbar
% title(sprintf("num active finger = %d , n = %d", i, length(chordIdx)))
% xlabel("subj")
% ylabel("subj")
end
% distribution of 'subject correlations' across chord groups:
figure;
for i = 1:length(subjCorrs)
subplot(length(subjCorrs),1,i)
histogram(subjCorrs{i},5)
xline(mean(subjCorrs{i}),'--r')
title("num active fingers: "+num2str(i))
xlabel('correlation')
xlim([-1,1])
end
%% pie chart
clc;
close all;
clearvars -except data
selectRun = -2;
holdTime = 600;
baseLineForceOption = 0; % if '0', then the baseline force will be considerred [0,0,0,0,0]. If not,
% baseline force will be considerred the avg
% force during baseline duration.
durAfterActive = 200;
forceData = cell(size(data)); % extracting the force signals for each subj
for i = 1:size(data,1)
forceData{i,1} = extractDiffForce(data{i,1});
forceData{i,2} = data{i,2};
end
outCell = cell(size(data));
for subj = 1:size(data,1)
outCell{subj,2} = data{subj,2};
chordVec = generateAllChords(); % all chords
subjData = data{subj,1};
subjForceData = forceData{subj,1};
outCellSubj = cell(length(chordVec),2);
vecBN = unique(subjData.BN);
for i = 1:length(chordVec)
outCellSubj{i,2} = chordVec(i);
outCellSubj{i,1} = cell(1,3);
if (selectRun == -1) % selecting the last 12 runs
trialIdx = find(subjData.chordID == chordVec(i) & subjData.trialErrorType ~= 1 & subjData.BN > vecBN(end-12));
elseif (selectRun == -2) % selectign the last 24 runs
trialIdx = find(subjData.chordID == chordVec(i) & subjData.trialErrorType ~= 1 & subjData.BN > vecBN(end-24));
elseif (selectRun == 1) % selecting the first 12 runs
trialIdx = find(subjData.chordID == chordVec(i) & subjData.trialErrorType ~= 1 & subjData.BN < 13);
elseif (selectRun == 2)
trialIdx = find(subjData.chordID == chordVec(i) & subjData.trialErrorType ~= 1 & subjData.BN > 12 & subjData.BN < 25);
elseif (selectRun == 3)
trialIdx = find(subjData.chordID == chordVec(i) & subjData.trialErrorType ~= 1 & subjData.BN > 24 & subjData.BN < 37);
iTmp = find(subjData.BN > 24 & subjData.BN < 37,1);
if (isempty(iTmp))
error("Error with <selectRun> option , " + data{subj,2} + " does not have block number " + num2str(selectRun))
end
else
error("selectRun " + num2str(selectRun) + "does not exist. Possible choices are 1,2,3,-1 and -2.")
end
if (~isempty(trialIdx))
chordTmp = num2str(chordVec(i));
forceVec_i_holder = [];
idealVec = zeros(1,5);
for trial_i = 1:length(trialIdx)
forceTrial = subjForceData{trialIdx(trial_i)};
baselineIdx = forceTrial(:,1) == 2;
execIdx = find(forceTrial(:,1) == 3);
execIdx = execIdx(end-holdTime/2:end); % 2ms is sampling frequency hence the holdTime/2
avgBaselineForce = mean(forceTrial(baselineIdx,3:7),1);
if (baseLineForceOption == 0)
avgBaselineForce = zeros(1,5);
end
avgExecForce = mean(forceTrial(execIdx,3:7),1);
idealVec = idealVec + (avgExecForce - avgBaselineForce)/length(trialIdx);
forceTmp = [];
tVec = subjForceData{trialIdx(trial_i)}(:,2); % time vector in trial
tGoCue = subjData.planTime(trialIdx(trial_i));
for j = 1:5 % thresholded force of the fingers after "Go Cue"
if (chordTmp(j) == '1') % extension
forceTmp = [forceTmp (subjForceData{trialIdx(trial_i)}(tVec>=tGoCue,2+j) > subjData.baselineTopThresh(trialIdx(trial_i)))];
if (isempty(find(forceTmp(:,end),1)))
forceTmp(:,end) = [];
end
elseif (chordTmp(j) == '2') % flexion
forceTmp = [forceTmp (subjForceData{trialIdx(trial_i)}(tVec>=tGoCue,2+j) < -subjData.baselineTopThresh(trialIdx(trial_i)))];
if (isempty(find(forceTmp(:,end),1)))
forceTmp(:,end) = [];
end
elseif (chordTmp(j) == '9') % finger should be relaxed
forceTmp = [forceTmp (subjForceData{trialIdx(trial_i)}(tVec>=tGoCue,2+j) < -subjData.baselineTopThresh(trialIdx(trial_i)) ...
| subjForceData{trialIdx(trial_i)}(tVec>=tGoCue,2+j) > subjData.baselineTopThresh(trialIdx(trial_i)))];
if (isempty(find(forceTmp(:,end),1)))
forceTmp(:,end) = [];
end
end
end
if (isempty(find(forceTmp,1))) % if no fingers moved out of threshold, go to next trial
disp("empty trial")
continue
end
tmpIdx = [];
for k = 1:size(forceTmp,2)
tmpIdx(k) = find(forceTmp(:,k),1);
end
[sortIdx,~] = sort(tmpIdx); % sortIdx(1) is the first index after "Go Cue" that the first finger crossed the baseline thresh
idxStart = find(tVec==tGoCue)+sortIdx(1)-1; % index that the first finger passes the baseline threhold after "Go Cue"
%idxStart = idxStart - idxStartShift;
forceSelceted = [];
for j = 1:5 % getting the force from idxStart to idxStart+durAfterActive
forceSelceted = [forceSelceted subjForceData{trialIdx(trial_i)}(idxStart:idxStart+round(durAfterActive/2),2+j)];
end
forceVec_i = mean(forceSelceted,1); % average of finger forces in the first {durAfterActive} ms
forceVec_i_holder = [forceVec_i_holder ; forceVec_i/norm(forceVec_i)];
end
outCellSubj{i,1}{1} = forceVec_i_holder;
outCellSubj{i,1}{2} = repmat(idealVec/norm(idealVec),size(forceVec_i_holder,1),1);
outCellSubj{i,1}{3} = [ones(size(forceVec_i_holder,1),1)*i (1:size(forceVec_i_holder,1))'];
else
outCellSubj{i,1} = [];
end
end
outCell{subj,1} = outCellSubj;
end
% Making regressors:
y = []; % dependent variable -> N by 5 matrix
X1 = []; % chord -> N by 242 matrix
X2 = []; % chord and subj -> N by 242*6 matrix
labels = [];
chordIDVec = [];
for subj = 1:size(outCell,1)
tmp = outCell{subj,1};
forceVec = [tmp{:,1}]';
idealVec = forceVec(2:3:end);
tmpChord = forceVec(3:3:end);
forceVec = forceVec(1:3:end);
idealVec = vertcat(idealVec{:});
forceVec = vertcat(forceVec{:});
tmpChord = vertcat(tmpChord{:});
labels = [labels ; [subj*ones(size(tmpChord,1),1),tmpChord]];
tmpChord = tmpChord(:,1);
X1_tmp = zeros(size(tmpChord,1),242);
X2_tmp = zeros(size(tmpChord,1),242*6);
val = unique(tmpChord);
for i = 1:length(val)
X1_tmp(tmpChord==val(i),val(i)) = 1;
X2_tmp(tmpChord==val(i),(subj-1)*242+val(i)) = 1;
end
chordIDVec = [chordIDVec ; tmpChord];
X1 = [X1 ; X1_tmp];
X2 = [X2 ; X2_tmp];
y = [y;idealVec-forceVec];
end
% mean cetnering the dependent variable (for simpler matrix calculations):
y = y - repmat(mean(y,1),size(y,1),1);
% ====== Regresison:
[beta,SSR,SST] = myOLS(y,[X1,X2],labels,'shuffle_trial_crossVal');
% var explained:
chordVar = mean(SSR(:,1)./SST) * 100;
subjVar = mean((SSR(:,2) - SSR(:,1))./SST) * 100;
trialVar = 100 - (chordVar + subjVar);
fprintf("var partitioning:\nChord = %.4f , Chord-Subj = %.4f , Trial = %.4f\n\n\n",chordVar,subjVar,trialVar);
% pie chart:
figure;
pie([chordVar,subjVar,trialVar],{'chord','chord-subj','trial-noise'});
title(sprintf('Variance Partitioning'))
% Simulations ===============================================
% random noise simulation
y = makeSimData(size(y,1),5,'random',[0,1]);
% ====== Regresison:
[beta,SSR,SST] = myOLS(y,[X1,X2],labels,'shuffle_trial_crossVal');
% var explained:
chordVar = mean(SSR(:,1)./SST) * 100;
subjVar = mean((SSR(:,2) - SSR(:,1))./SST) * 100;
trialVar = 100 - (chordVar + subjVar);
fprintf("Sim Noisy data:\nChord = %.4f , Chord-Subj = %.4f , Trial = %.4f\n\n\n",chordVar,subjVar,trialVar);
% pie chart:
figure;
pie([chordVar,subjVar,trialVar],{'chord','chord-subj','trial-noise'});
title(sprintf('Simulation , Random noise'))
% Model simulation
varChord = 5;
varSubj = 3;
varEps = 1;
total = varChord + varSubj + varEps;
y = makeSimData(size(y,1),5,'model',{{X1,X2},[varChord,varSubj,varEps]});
% ====== Regresison:
[beta,SSR,SST] = myOLS(y,[X1,X2],labels,'shuffle_trial_crossVal');
% var explained:
chordVar = mean(SSR(:,1)./SST) * 100;
subjVar = mean((SSR(:,2) - SSR(:,1))./SST) * 100;
trialVar = 100 - (chordVar + subjVar);
fprintf("Sim Model data:\nChord = %.4f , Chord-Subj = %.4f , Trial = %.4f\n",chordVar,subjVar,trialVar);
fprintf("Theoretical Partiotions:\nChord = %.4f , Chord-Subj = %.4f , Trial = %.4f\n\n\n",varChord/total*100,varSubj/total*100,varEps/total*100);
% pie chart:
figure;
pie([chordVar,subjVar,trialVar],{'chord','chord-subj','trial-noise'});
title(sprintf('Simulation , chord=%.2f , chord-subj=%.2f , noise=%.2f',varChord/total*100,varSubj/total*100,varEps/total*100))
%% Model Testing
clc;
close all;
clearvars -except data
% global params:
dataName = "meanDev";
corrMethod = 'pearson';
% theta calc params:
onlyActiveFing = 0;
firstTrial = 2;
selectRun = -2;
durAfterActive = 200;
% medRT params:
excludeChord = [];
featureCell = {"singleFingExt","singleFinger",...
"neighbourFingers+singleFinger"};
efc1_analyze('modelTesting',data,'dataName',dataName,'featureCell',featureCell,'corrMethod',corrMethod,'onlyActiveFing',onlyActiveFing,...
'firstTrial',firstTrial,'selectRun',selectRun,'durAfterActive',durAfterActive,'excludeChord',excludeChord);
%% variability of finger forces
% meand and var of finger forces during baseline interval, inactive and
% active for each subject.
clc;
clearvars -except data
close all;
holdTime = 600; % chord hold time = 600ms
baselineForceCell = cell(size(data,1),3);
execForceCell = cell(size(data,1),3);
for subj = 1:size(data,1)
dataTmp = data{subj,1};
forces = extractDiffForce(dataTmp); % force signals
correctTrialIdx = find(dataTmp.trialErrorType == 0); % correct trials
subj_baselineForceMat = zeros(length(correctTrialIdx),5);
subj_execForceMat = zeros(length(correctTrialIdx),5);
for j = 1:length(correctTrialIdx)
forceTmp = forces{correctTrialIdx(j)};
baselineIdx = find(forceTmp(:,1) == 2);
execIdx = find(forceTmp(:,1) == 3);
execIdx = execIdx(end-holdTime/2:end);
baselineForce = forceTmp(baselineIdx,3:end);
execForce = forceTmp(execIdx,3:end);
subj_baselineForceMat(j,:) = mean(baselineForce,1);
subj_execForceMat(j,:) = mean(execForce,1);
end
baselineForceCell{subj,1} = subj_baselineForceMat;
execForceCell{subj,1} = subj_execForceMat;
baselineForceCell{subj,2} = data{subj,2};
execForceCell{subj,2} = data{subj,2};
baselineForceCell{subj,3} = correctTrialIdx;
execForceCell{subj,3} = correctTrialIdx;
end
% baseline force plot
figure;
for subj = 1:size(baselineForceCell,1)
tmpForceMat = baselineForceCell{subj,1};
avgFingForce = mean(tmpForceMat,1);
stdFingForce = std(tmpForceMat,[],1);
baselineTopThreshold = data{subj,1}.baselineTopThresh(1);
subplot(2,3,subj);
scatter([1,2,3,4,5],avgFingForce,60,'k','filled')
hold on
errorbar([1,2,3,4,5],avgFingForce,stdFingForce,'LineStyle','none','Color','k')
hold on
line([0,6],[-baselineTopThreshold -baselineTopThreshold],'Color','r','LineStyle','--')
hold on
line([0,6],[baselineTopThreshold baselineTopThreshold],'Color','r','LineStyle','--')
ylim([-6,6])
xlim([0,6])
xticks(1:5)
xticklabels({'finger 1', 'finger 2', 'finger 3', 'finger 4', 'finger 5'})
ylabel('avg force (N)')
title(sprintf("baseline , %s",baselineForceCell{subj,2}))
end
% exec inactive force plot
figure;
for subj = 1:size(baselineForceCell,1)
baselineTopThreshold = data{subj,1}.baselineTopThresh(1);
tmpForceMat = execForceCell{subj,1};
tmpForceMat(tmpForceMat > baselineTopThreshold | tmpForceMat < -baselineTopThreshold) = 0;
avgFingForce = zeros(1,5);
stdFingForce = zeros(1,5);
for j = 1:5 % loop over fingers
tmp = tmpForceMat(:,j);
tmp(tmp==0) = [];
avgFingForce(j) = mean(tmp);
stdFingForce(j) = std(tmp);
end
subplot(2,3,subj);
hold all
scatter([1,2,3,4,5],avgFingForce,60,'k','filled')
errorbar([1,2,3,4,5],avgFingForce,stdFingForce,'LineStyle','none','Color','k')
line([0,6],[-baselineTopThreshold -baselineTopThreshold],'Color','r','LineStyle','--')
line([0,6],[baselineTopThreshold baselineTopThreshold],'Color','r','LineStyle','--')
ylim([-6,6])
xlim([0,6])
xticks(1:5)
xticklabels({'finger 1', 'finger 2', 'finger 3', 'finger 4', 'finger 5'})
ylabel('avg force (N)')
title(sprintf("execution inactive, %s",baselineForceCell{subj,2}))
end
% exec extension force plot
figure;
for subj = 1:size(baselineForceCell,1)
baselineTopThreshold = data{subj,1}.baselineTopThresh(1);
extBotThresh = data{subj,1}.extBotThresh(1);
extTopThresh = data{subj,1}.extTopThresh(1);
tmpForceMat = execForceCell{subj,1};
tmpForceMat(tmpForceMat < baselineTopThreshold) = 0;
avgFingForce = zeros(1,5);
stdFingForce = zeros(1,5);
for j = 1:5 % loop over fingers
tmp = tmpForceMat(:,j);
tmp(tmp==0) = [];
avgFingForce(j) = mean(tmp);
stdFingForce(j) = std(tmp);
end
subplot(2,3,subj);
hold all
scatter([1,2,3,4,5],avgFingForce,60,'k','filled')
errorbar([1,2,3,4,5],avgFingForce,stdFingForce,'LineStyle','none','Color','k')
line([0,6],[-baselineTopThreshold -baselineTopThreshold],'Color','r','LineStyle','--')
line([0,6],[baselineTopThreshold baselineTopThreshold],'Color','r','LineStyle','--')
line([0,6],[extBotThresh extBotThresh],'Color','k')
line([0,6],[extTopThresh extTopThresh],'Color','k')
line([0,6],[-extTopThresh -extTopThresh],'Color','k')
line([0,6],[-extBotThresh -extBotThresh],'Color','k')
ylim([-6,6])
xlim([0,6])
xticks(1:5)
xticklabels({'finger 1', 'finger 2', 'finger 3', 'finger 4', 'finger 5'})
ylabel('avg force (N)')
title(sprintf("extension, %s",baselineForceCell{subj,2}))
end
% exec flexion force plot
figure;
for subj = 1:size(baselineForceCell,1)
baselineTopThreshold = data{subj,1}.baselineTopThresh(1);
extBotThresh = data{subj,1}.extBotThresh(1);
extTopThresh = data{subj,1}.extTopThresh(1);
tmpForceMat = execForceCell{subj,1};
tmpForceMat(tmpForceMat > -baselineTopThreshold) = 0;
avgFingForce = zeros(1,5);
stdFingForce = zeros(1,5);
for j = 1:5 % loop over fingers
tmp = tmpForceMat(:,j);
tmp(tmp==0) = [];
avgFingForce(j) = mean(tmp);
stdFingForce(j) = std(tmp);
end
subplot(2,3,subj);
hold all
scatter([1,2,3,4,5],avgFingForce,60,'k','filled')
errorbar([1,2,3,4,5],avgFingForce,stdFingForce,'LineStyle','none','Color','k')
line([0,6],[-baselineTopThreshold -baselineTopThreshold],'Color','r','LineStyle','--')
line([0,6],[baselineTopThreshold baselineTopThreshold],'Color','r','LineStyle','--')
line([0,6],[extBotThresh extBotThresh],'Color','k')
line([0,6],[extTopThresh extTopThresh],'Color','k')
line([0,6],[-extTopThresh -extTopThresh],'Color','k')
line([0,6],[-extBotThresh -extBotThresh],'Color','k')
ylim([-6,6])
xlim([0,6])
xticks(1:5)
xticklabels({'finger 1', 'finger 2', 'finger 3', 'finger 4', 'finger 5'})
ylabel('avg force (N)')
title(sprintf("flexion, %s",baselineForceCell{subj,2}))
end
%% thetaMean avg vs numFingerActive
runVec = [1,2,3,-1];
colors = [[0 0.4470 0.7410];[0.8500 0.3250 0.0980];[0.9290 0.6940 0.1250];[0.4940 0.1840 0.5560];...
[0.4660 0.6740 0.1880];[0.3010 0.7450 0.9330];[0.6350 0.0780 0.1840]];
figure;
for j = 1:4
thetaCell = efc1_analyze('thetaExp_vs_thetaStd',data,'durAfterActive',durAfterActive,'plotfcn',0,...
'firstTrial',firstTrial,'onlyActiveFing',onlyActiveFing,'selectRun',runVec(j));
[thetaMean,~] = meanTheta(thetaCell,firstTrial);
chordVec = generateAllChords();
chordVecSep = sepChordVec(chordVec);
xVec = [];
yVec = [];
for i = 1:size(chordVecSep,1)
xTmp = repmat(i,size(thetaMean,2)*length(chordVecSep{i,2}),1);
yTmp = thetaMean(chordVecSep{i,2},:);
yTmp = yTmp(:);
xVec = [xVec;xTmp];
yVec = [yVec;yTmp];
end
xVec(isnan(yVec)) = [];
yVec(isnan(yVec)) = [];
lineplot(xVec,yVec,'linecolor',colors(j,:));
title("meanTheta across subjects")
xlabel("num Finger Active")
ylabel("meanTheta (degree)")
hold on
end
%% bias vs numFingerActive
runVec = [1,2,3,-1];
colors = [[0 0.4470 0.7410];[0.8500 0.3250 0.0980];[0.9290 0.6940 0.1250];[0.4940 0.1840 0.5560];...
[0.4660 0.6740 0.1880];[0.3010 0.7450 0.9330];[0.6350 0.0780 0.1840]];
figure;
for j = 1:4
biasVarCell = efc1_analyze('theta_bias',data,'durAfterActive',durAfterActive,'selectRun',runVec(j),...
'firstTrial',firstTrial);
biasMat = [biasVarCell{:,1}];
biasMat(:,1:2:end)=[];
% in some runs, for some of the chords we get [] for biasVar.
% Fixing for that here:
emptyCells = cellfun(@isempty,biasMat);
[row,col] = find(emptyCells);
biasMat(row,:) = [];
biasMat = cell2mat(biasMat);
biasMat(:,2:2:end)=[];
chordVec = generateAllChords();
chordVec(row,:) = [];
chordVecSep = sepChordVec(chordVec);
xVec = [];
yVec = [];
for i = 1:size(chordVecSep,1)
xTmp = repmat(i,size(biasMat,2)*length(chordVecSep{i,2}),1);
yTmp = biasMat(chordVecSep{i,2},:);
yTmp = yTmp(:);
xVec = [xVec;xTmp];
yVec = [yVec;yTmp];
end
xVec(isnan(yVec)) = [];
yVec(isnan(yVec)) = [];
lineplot(xVec,yVec,'linecolor',colors(j,:));
title("biasTheta")
xlabel("num Finger Active")
ylabel("biasTheta (degree)")
hold on
end
%% MeanDev vs numFingerActive
runVec = [1,2,3,-1];
colors = [[0 0.4470 0.7410];[0.8500 0.3250 0.0980];[0.9290 0.6940 0.1250];[0.4940 0.1840 0.5560];...
[0.4660 0.6740 0.1880];[0.3010 0.7450 0.9330];[0.6350 0.0780 0.1840]];
figure;
for j = 1:4
meanDev = regressionDataset(data,'meanDev','selectRun',runVec(j),'plotfcn',0);
chordVec = generateAllChords();
chordVecSep = sepChordVec(chordVec);
xVec = [];
yVec = [];
for i = 1:size(chordVecSep,1)
xTmp = repmat(i,size(meanDev,2)*length(chordVecSep{i,2}),1);
yTmp = meanDev(chordVecSep{i,2},:);
yTmp = yTmp(:);
xVec = [xVec;xTmp];
yVec = [yVec;yTmp];
end
xVec(isnan(yVec)) = [];
yVec(isnan(yVec)) = [];
lineplot(xVec,yVec,'linecolor',colors(j,:));
title("MeanDev")
xlabel("num Finger Active")
ylabel("Avg MeanDev")
hold on
end
%% median RT over numActiveFinger + mean theta over numActiveFinger
close all;
clc;
chordVec = generateAllChords();
chordVecSep = sepChordVec(chordVec);
colors = [[0 0.4470 0.7410];[0.8500 0.3250 0.0980];[0.9290 0.6940 0.1250];[0.4940 0.1840 0.5560];...
[0.4660 0.6740 0.1880];[0.3010 0.7450 0.9330];[0.6350 0.0780 0.1840]];
activeVec = zeros(length(chordVec),1);
for i = 1:size(chordVecSep,1)
activeVec(chordVecSep{i,2}) = i;
end
for i = 1:size(data,1)
if (length(data{i,1}.BN) >= 2420)
medRT = cell2mat(calcMedRT(data{i,1},[]));
lineplot(activeVec,medRT(:,end),'linecolor',colors(i,:),'errorbars',{''});
legNames{i} = data{i,2};
hold on
end
end
legend(legNames,'Location','northwest')
xlabel("num active fingers")
ylabel("average medRT")
% mean theta over numActiveFinger:
chordVec = generateAllChords();
chordVecSep = sepChordVec(chordVec);
colors = [[0 0.4470 0.7410];[0.8500 0.3250 0.0980];[0.9290 0.6940 0.1250];[0.4940 0.1840 0.5560];...
[0.4660 0.6740 0.1880];[0.3010 0.7450 0.9330];[0.6350 0.0780 0.1840]];
firstTrial = 2;
activeVec = zeros(length(chordVec),1);
for i = 1:size(chordVecSep,1)
activeVec(chordVecSep{i,2}) = i;
end
thetaMean = zeros(242,size(thetaCell,1));
thetaStd = zeros(242,size(thetaCell,1));
for subj = 1:size(thetaCell,1)
for j = 11:size(thetaMean,1)
thetaMean(j,subj) = mean(thetaCell{subj,1}{j,2}(firstTrial:end));
thetaStd(j,subj) = std(thetaCell{subj,1}{j,2}(firstTrial:end));
end
end
figure;
for i = 1:size(thetaMean,2)
lineplot(activeVec,thetaMean(:,i),'linecolor',colors(i,:),'errorbars',{''});
legNames{i} = data{i,2};
hold on
end
legend(legNames,'Location','northwest')
xlabel("num active fingers")
ylabel("mean theta")
%% Linear Regression (OLS) - Mean Theta
clc;
close all;
chordVec = generateAllChords();
chordVecSep = sepChordVec(chordVec);
% features
% num active fingers - continuous:
f1 = zeros(size(chordVec));
for i = 1:size(chordVecSep,1)
f1(chordVecSep{i,2}) = i;
end
% num active fingers - one hot:
% f1 = zeros(size(chordVec,1),5);
% for i = 1:size(chordVecSep,1)
% f1(chordVecSep{i,2},i) = 1;
% end
% each finger flexed or not:
f2 = zeros(size(chordVec,1),5);
for i = 1:size(chordVec,1)
chord = num2str(chordVec(i));
f2(i,:) = (chord == '2');
end
% each finger extended or not:
f3 = zeros(size(chordVec,1),5);
for i = 1:size(chordVec,1)
chord = num2str(chordVec(i));
f3(i,:) = (chord == '1');
end
% second level interactions of finger combinations:
f4Base = [f2,f3];
f4 = [];
for i = 1:size(f4Base,2)-1
for j = i+1:size(f4Base,2)
f4 = [f4, f4Base(:,i) .* f4Base(:,j)];
end
end
firstTrial = 2;
activeVec = zeros(length(chordVec),1);
for i = 1:size(chordVecSep,1)
activeVec(chordVecSep{i,2}) = i;
end
thetaMean = zeros(242,size(thetaCell,1));
thetaStd = zeros(242,size(thetaCell,1));
for subj = 1:size(thetaCell,1)
for j = 11:size(thetaMean,1)
thetaMean(j,subj) = mean(thetaCell{subj,1}{j,2}(firstTrial:end));
thetaStd(j,subj) = std(thetaCell{subj,1}{j,2}(firstTrial:end));
end
end
% linear regression for one subj:
features = [f1,f2,f3,f4];
if (onlyActiveFing)
thetaMean(1:10,:) = [];
features(1:10,:) = [];
end
[i,~] = find(isnan(thetaMean));
thetaMean(i,:) = [];
features(i,:) = [];
subj = 1;
estimated = thetaMean(:,subj);
mdl = fitlm(features,estimated)
% cross validated linear regression:
fullFeatures = repmat(features,size(data,1)-1,1);
rho_OLS_meanTheta = cell(1,2);
for i = 1:size(data,1)
fprintf("\n")
idx = setdiff(1:size(data,1),i);
estimated = [];
for j = idx
estimated = [estimated ; thetaMean(:,j)];
end
fprintf('%s out:\n',data{i,2})
mdl = fitlm(fullFeatures,estimated)
% testing model:
pred = predict(mdl,features);
meanThetaOut = thetaMean(:,i);
corrTmp = corr(meanThetaOut,pred,'type',corrMethod);
rho_OLS_meanTheta{2}(1,i) = convertCharsToStrings(data{i,2});
rho_OLS_meanTheta{1}(1,i) = corrTmp;
end
%% analysis tmp
clc;
clearvars -except data
close all;
forceData = cell(size(data));
for i = 1:size(data,1)
forceData{i,1} = extractDiffForce(data{i,1});
forceData{i,2} = data{i,2};
end
%% visualize force data - examples
clc;
close all;
clearvars -except data forceData
subj = 1;
trial = 3977;
sigTmp = forceData{subj,1}{trial};
fGain4 = data{subj,1}.fGain4(trial);
fGain5 = data{subj,1}.fGain5(trial);
plot(sigTmp(:,2),[sigTmp(:,3:5),fGain4*sigTmp(:,6),fGain5*sigTmp(:,7)])
xline(500,'r','LineWidth',1.5)
hold on
plot([sigTmp(1,2) sigTmp(end,2)],[data{subj,1}.baselineTopThresh data{subj,1}.baselineTopThresh],'k')
hold on
plot([sigTmp(1,2) sigTmp(end,2)],-[data{subj,1}.baselineTopThresh data{subj,1}.baselineTopThresh],'k')
legend({"1","2","3","4","5"})