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Commit 345bba58 authored by mposthuma's avatar mposthuma
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Added scripts for final experiment

parent 82999c8d
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final_experiment_analyseModel.m 0 → 100644
View file @ 345bba58
%% veranderen voor 1 subject
cfn = pb_clean;
subj = [1 3 4];
for iS = 1:length(subj)
cd(['/Users/jjheckman/Desktop/PhD/Data/Chapter 3/subj/00' num2str(subj(iS)) filesep 'converted data' filesep]);
l = dir('meta_data1.mat');
load(l(1).name);
D(iS) = S;
end
S = D;
save(['/Users/jjheckman/Desktop/PhD/Data/Chapter 3/merged_data1.mat'], 'S')
%% Initialize
cfn = pb_clean('cd','/Users/jjheckman/Desktop/PhD/Data/Chapter 3/');
l = dir('merged_data1.mat');
load(l(end).name);
% Globals
NDUR = 5;
NMODEL = 8;
NCOND = 2;
NSUBJ = 3;
NPRED = 4;
CDUR = pb_selectcolor(NDUR,1);
CCOND = pb_selectcolor(NCOND,2);
CSUBJ = pb_selectcolor(NSUBJ,2);
MIN_LAT = 0;
MAX_LAT = 400;
%% Graph reaction times
% Make probit plots of data
% Make figure
cfn = pb_newfig(cfn);
sgtitle('Probit models')
cnt = 0;
for iC = 1:NCOND
% Run for condition (head free / fixed)
for iS = 1:NSUBJ
% Run for subjects (s001/003/004)
cnt = cnt+1;
if iC <= length(S(iS).condition)
% Check if data exists
% Make subplot
subplot(NCOND,NSUBJ,cnt)
title([pb_sentenceCase(strrep(S(iS).condition(iC).condition,'_',' ')) ' (' S(iS).subj_id ')'])
hold on;
axis square;
included = zeros(2,NDUR); % preallocate
for iD = 1:NDUR
% Run for all stimulus durations
% Select data
rt = S(iS).condition(iC).model_data(iD).RT;
sel = rt>MIN_LAT & rt< MAX_LAT;
included(1,iD) = sum(sel);
included(2,iD) = length(sel);
pb_probit(rt(sel),'gcolor',CDUR(iD,:)); % plot data
end
% Write text
x = xticks;
y = yticks;
included = sum(included,2);
t = text(x(3),y(1),[num2str(included(1)/included(2)*100,3) '\% included'],'FontSize',12);
end
end
end
pb_nicegraph('def',1);
pause(.1);
%% Graph head fixed models
% graph head fixed
durs = [0.5000 1.0000 2.0000 4.0000 100.0000];
c = {'I','II','III','IV','V','VI','VII','VIII'};
% for iC = 1:NCOND
% % Run for condition (head free / fixed)
%
% for iS = 1:NSUBJ
% % Run for subjects (s001/003/004)
%
% if iC <= length(S(iS).condition)
% % Check if data exists
%
% cnt = 0;
% cnt_c = 0;
%
% % Predraw graph
% % Build figure
% cfn = pb_newfig(cfn,'ws','normal');
% sgtitle([pb_sentenceCase(strrep(S(iS).condition(iC).condition,'_',' ')) ' (' S(iS).subj_id ')']);
%
% % make subplots
% for iM = 1:NMODEL
% for iD = 1:NDUR
% cnt = cnt+1;
% h(iM,iD) = subplot(NMODEL,NDUR,cnt);
%
% if cnt < 6; title([num2str(durs(iD)) ' ms']); end
% if mod(cnt,5) == 1; cnt_c = cnt_c+1; ylabel(c{cnt_c}); end
%
% % layout
% hold on;
% axis square;
% xlim([-50 50]);
% ylim([-50 50]);
% pb_dline;
% pause(.1);
% end
%
% end
%
% % Fill in data
% for iM = 1:NMODEL
% for iD = 1:NDUR
% axes(h(iM,iD));
%
% % Select saccades
% rt = S(iS).condition(iC).model_data(iD).RT;
% sel = rt>MIN_LAT & rt< MAX_LAT;
%
%
% dGx = S(iS).condition(iC).merged_saccade_data(iM,iD).dGx(sel);
% dGy = S(iS).condition(iC).merged_saccade_data(iM,iD).dGy(sel);
%
% % Plot data
% [h_lines,b,r] = pb_regplot(dGx, dGy);
%
% % Set colors
% h_lines(1).Color = [0 0 0];
% h_lines(1).MarkerFaceColor = CDUR(iD,:);
% h_lines(1).Tag = 'Fixed';
%
% pause(.1);
%
% end
% end
% end
% pb_nicegraph;
% pause(.1);
% end
% end
%
% pause(.1);
%% Multiple Linear Regression EARLY - LATE
SEL_D = [120 400];
SEL_T = 20;
NPRED = 3;
% draw figure
cfn = pb_newfig(cfn);
cnt = 0;
c = {'$T_R$','$\Delta C_S$','$\Delta E_H$'};
sgtitle('Multiple linear regression');
cnt = 0;
for iC = 1:NCOND
% Run over number of conditions
for iP = 1:NPRED
% Run over number of predictors
cnt = cnt + 1;
% Draw axis
subplot(NCOND,NPRED,cnt)
axis square;
hold on;
if iC == 2; xlabel('Stimulus duration (ms)'); end
if iP == 1; ylabel([pb_sentenceCase(strrep(S(iS).condition(iC).condition,'_',' ')) ' (rc)']); end
% Define ticks and limits
ylim([-1.5 1.5]);
xlim([0.5 5.5]);
xticks([1 2 3 4 5]);
yticks([-1 0 1]);
xticklabels({'0.5','1','2','4','100'});
pb_hline(-1:1);
pause(.1);
% plot data
for iS = 1:NSUBJ
% Run over number of subjects
if iC <= length(S(iS).condition)
% Check if data exists
% preallocate space
rc_data = zeros(4,5);
rc_err = zeros(4,5);
for iD = 1:NDUR
% Run over number of durations
% select data
rt = S(iS).condition(iC).model_data(iD).RT;
t = abs(S(iS).condition(iC).model_data(iD).G(:,2) - S(iS).condition(iC).model_data(iD).Tr(:,2));
sel_D = rt>SEL_D(1) & rt< SEL_D(2);
sel_T = t < SEL_T;
sel = sel_D & sel_T;
% Select predictors
Tr = S(iS).condition(iC).model_data(iD).Tr(sel,1);
Cs = S(iS).condition(iC).model_data(iD).Cs(sel,1);
Eh = S(iS).condition(iC).model_data(iD).Eh(sel,1);
G = S(iS).condition(iC).model_data(iD).G(sel,1);
% Regression
tbl = table(Tr,Cs,Eh,G,'VariableNames',{'Tr','Cs','Eh','G'});
lm = fitlm(tbl,'G~Tr+Cs+Eh');
coeff = lm.Coefficients;
% Store rcs
rc_data(1:3,iD) = table2array(coeff(2:end,1));
rc_err(1:3,iD) = table2array(coeff(2:end,2));
end
end
errorbar(rc_data(iP,:),rc_err(iP,:),'Tag','Fixed','color',CSUBJ(iS,:));
pause(.1);
end
% compute avarage rcs
h_dat = pb_fobj(gca,'Type','errorbar');
%h_dat = h_dat(floor(length(h_dat)/2)+1:end);
h_sum = errorbar(mean(vertcat(h_dat.YData)), std(vertcat(h_dat.YData)),'Tag','Fixed','color',[0.4 0.4 0.4],'LineWidth',2);
end
end
pb_nicegraph;
%% Distributions
% Create distribution plots of all the model's predictors
NPRED = 4;
for iS = 1:NSUBJ
% Run over number of subjects
% draw figure
cfn = pb_newfig(cfn);
cnt = 0;
c = {'$T_R$','$\Delta C_S$','$\Delta H_C$','$\Delta E_H$'};
sgtitle(['Predictor distribution (' S(iS).subj_id ')'])
for iC = 1:NCOND
% Run over number of conditions
if iC <= length(S(iS).condition)
% Check if data exists
for iP = 1:NPRED
% Run over number of predictors
cnt = cnt + 1;
% Draw axis
subplot(length(S(iS).condition),NPRED,cnt);
axis square;
hold on;
if iC == 1; title(c{iP}); end
if iC == 2; xlabel('Azimuth ($^{\circ}$)'); end
if iP == 1; ylabel('Elevation ($^{\circ}$)'); end
% Define ticks and limits
ylim([-50 50]);
xlim([-50 50]);
pause(.1);
for iD = 1:NDUR
% Run over number of durations
Di = NDUR+1-iD; % flip directions
% Select predictor
switch iP
case 1
predictor = S(iS).condition(iC).model_data(Di).Tr;
case 2
predictor = S(iS).condition(iC).model_data(Di).Cs;
case 3
predictor = S(iS).condition(iC).model_data(Di).Hc;
case 4
predictor = S(iS).condition(iC).model_data(Di).Eh;
end
x = predictor(:,1);
y = predictor(:,2);
scatter(x,y,6,'MarkerEdgeColor',CDUR(Di,:),'MarkerFaceColor',CDUR(Di,:),'MarkerFaceAlpha',0.6,'Tag','Fixed');
end
end
end
end
pb_nicegraph('def',1);
end
% %% Retinal Slip
%
%
% for iS = 1:NSUBJ
%
% % Make figure
% cfn = pb_newfig(cfn);
% sgtitle('Retinal slip')
%
% cnt = 0;
% for iC = 1:NCOND
% % Run for condition (head free / fixed)
%
% for iD = 1:NDUR
% % Run for subjects (s001/003/004)
% cnt = cnt+1;
%
% if iC <= length(S(iS).condition)
% % Check if data exists
%
% % Make subplot
% subplot(NCOND,NDUR,cnt);
%
% if iC == 1; title([num2str(durs(iD)) ' ms']); end
% if iC == length(S(iS).condition); xlabel('Position ($^{\circ}$)'); end
%
% hold on;
% axis square;
% xlim([-20 20]);
% ylim([-20 20]);
%
% x = S(iS).condition(iC).model_data(iD).Rx;
% y = S(iS).condition(iC).model_data(iD).Ry;
%
% left = S(iS).condition(iC).model_data(iD).sC < 0;
%
% h = [];
% for iR = 1:length(x)
% collie = CDUR(iD,:);
% if left(iR); collie = CDUR(iD,:)/2; end
% h(iR) = plot(x{iR},y{iR},'color',collie,'linewidth',2,'Tag','Fixed');
% end
% end
% end
% end
% pb_nicegraph;
% end
%
%%
P(1).pred(1).value = [];
P(1).pred(2).value = [];
P(1).pred(3).value = [];
P(1).pred(4).value = [];
P(2).pred(1).value = [];
P(2).pred(2).value = [];
P(2).pred(3).value = [];
P(2).pred(4).value = [];
P(3).pred(1).value = [];
P(3).pred(2).value = [];
P(3).pred(3).value = [];
P(3).pred(4).value = [];
for iS = 1:NSUBJ
for iC = 1:NCOND
for iD = 1:NDUR
if iC <= length(S(iS).condition)
% select data
rt = S(iS).condition(iC).model_data(iD).RT;
t = abs(S(iS).condition(iC).model_data(iD).G(:,2) - S(iS).condition(iC).model_data(iD).Tr(:,2));
sel_D = rt>SEL_D(1) & rt< SEL_D(2);
sel_T = t < SEL_T;
sel = sel_D & sel_T;
P(iS).pred(1).value = [P(iS).pred(1).value; S(iS).condition(iC).model_data(iD).G(sel,1)];
P(iS).pred(2).value = [P(iS).pred(2).value; S(iS).condition(iC).model_data(iD).Tr(sel,1)];
P(iS).pred(3).value = [P(iS).pred(3).value; S(iS).condition(iC).model_data(iD).Cs(sel,1)];
P(iS).pred(4).value = [P(iS).pred(4).value; S(iS).condition(iC).model_data(iD).Eh(sel,1)];
end
end
end
end
%%
NPRED = 4;
CSUB = pb_selectcolor(3,2);
cfn = pb_newfig(cfn);
sgtitle('Interactions');
c_pred = {'G','Tr','Cs','Eh'};
cnt = 0;
for iAx = 1:NPRED
% for all predictors
for iAy = 1:NPRED
% for all predictors
cnt = cnt+1;
subplot(NPRED,NPRED,cnt)
if iAx == 1; title(c_pred{iAy}); end
if iAy == 1; ylabel(c_pred{iAx}); end
axis square;
hold on;
if ~(mod(cnt,NPRED+1) == 1)
% target response
for iS = 1:NSUBJ
h = pb_regplot(P(iS).pred(iAy).value, P(iS).pred(iAx).value,'color',CSUB(iS,:),'marker','.','alpha',0.2,'size',5);
end
xlim([-50 50]);
ylim([-50 50]);
else
% histogram
if iC == 1; title(c{iP}); end
for iS = 1:NSUBJ
histogram(P(iS).pred(iAx).value,'BinWidth',5);
end
xlim([-100 100]);
end
end
end
pb_nicegraph;
final_experiment_epoch_blocks.m 0 → 100644
View file @ 345bba58
%% Epoch data and sacdet
% Epoching to only take relevant parts of data
l = dir('prep*.mat');
fn = l(1).name;
fs = 120;
duration = 3;
samples = fs*duration - 1;
for iB = 1:length(Data.timestamps)
% Empty traces
E.AzChairEpoched = [];
E.ElChairEpoched = [];
E.AzGazeEpoched = [];
E.ElGazeEpoched = [];
E.AzEyeEpoched = [];
E.ElEyeEpoched = [];
E.AzHeadEpoched = [];
E.ElHeadEpoched = [];
% Interpolate Gaze
lsl_opti = Data.timestamps(iB).optitrack;
Data.timestamps(iB).epoch_interp = 0:1/120:lsl_opti(end)-lsl_opti(1);
Data.position(iB).gaze_interp(:,1) = interp1(lsl_opti-lsl_opti(iB), Data.position(iB).gaze(:,1), Data.timestamps(iB).epoch_interp,'pchip')';
Data.position(iB).gaze_interp(:,2) = interp1(lsl_opti-lsl_opti(iB), Data.position(iB).gaze(:,2), Data.timestamps(iB).epoch_interp,'pchip')';
% Interpolate Chair
CUT_OFF = 203;
Data.position(iB).chair_interp(:,1) = interp1(Data.timestamps(iB).chair, Data.position(iB).chair, Data.timestamps(iB).epoch_interp,'pchip')';
Data.position(iB).chair_interp(Data.timestamps(iB).epoch_interp>CUT_OFF,1) = zeros(1,sum(Data.timestamps(iB).epoch_interp>CUT_OFF)); % Correct for extrapolation == 0;
Data.position(iB).chair_interp(:,2) = zeros(size(Data.position(iB).chair_interp(:,1)));
% Interpolate Eye
Data.position(iB).eye_interp(:,1) = interp1(lsl_opti-lsl_opti(iB), Data.position(iB).pupillabs(:,1), Data.timestamps(iB).epoch_interp,'pchip')';
Data.position(iB).eye_interp(:,2) = interp1(lsl_opti-lsl_opti(iB), Data.position(iB).pupillabs(:,2), Data.timestamps(iB).epoch_interp,'pchip')';
% Interpolate Head
Data.position(iB).head_interp(:,1) = interp1(lsl_opti-lsl_opti(iB), Data.position(iB).optitrack(:,1), Data.timestamps(iB).epoch_interp,'pchip')';
Data.position(iB).head_interp(:,2) = interp1(lsl_opti-lsl_opti(iB), Data.position(iB).optitrack(:,2), Data.timestamps(iB).epoch_interp,'pchip')';
% Select stimuli indices
nstim = length(Data.stimuli(iB).azimuth);
ntriggers = length(Data.timestamps(iB).stimuli);
ind = 1;
ext = 2;
for iS = 1:nstim
% epoch for stimuli
start = Data.timestamps(iB).stimuli(ind) - lsl_opti(1);
[~,idx] = min(abs(Data.timestamps(iB).epoch_interp-start));
% Gaze
E.AzGazeEpoched = [E.AzGazeEpoched, Data.position(iB).gaze_interp(idx:idx+samples,1)'];
E.ElGazeEpoched = [E.ElGazeEpoched, Data.position(iB).gaze_interp(idx:idx+samples,2)'];
% Chair
E.AzChairEpoched = [E.AzChairEpoched, Data.position(iB).chair_interp(idx:idx+samples,1)'];
E.ElChairEpoched = [E.ElChairEpoched, Data.position(iB).chair_interp(idx:idx+samples,2)'];
% Eye
E.AzEyeEpoched = [E.AzEyeEpoched, Data.position(iB).eye_interp(idx:idx+samples,1)'];
E.ElEyeEpoched = [E.ElEyeEpoched, Data.position(iB).eye_interp(idx:idx+samples,2)'];
% Head
E.AzHeadEpoched = [E.AzHeadEpoched, Data.position(iB).head_interp(idx:idx+samples,1)'];
E.ElHeadEpoched = [E.ElHeadEpoched, Data.position(iB).head_interp(idx:idx+samples,2)'];
ind = ind + ext;
end
Data.epoch(iB) = E;
end
fn = strrep(fn,'preprocessed','epoched');
save(fn,'Data');
final_experiment_extractpar.m 0 → 100644
View file @ 345bba58
cfn = pb_clean('cd','C:\Users\marc_\OneDrive\Documents\Studie\STAGE\MATLAB Code\Data\Recordings\final experiment\Subject 0022\dynamic');
cd(pb_getdir('cdir',cd));
pause(.1); cdir = [cd '\']; pause(.1);
fseps = strfind(cdir,filesep);
% Global
NDUR = 5;
NMODEL = 4;
FSAMPLE = 120;
CONDITION = pb_sentenceCase(cdir(max(fseps)+1:end));
MODELPAR = [1 -1 -1;
1 0 -1;
1 -1 0;
1 0 0];
% load data
l = dir('preprocessed_data_*');
load(l(1).name);
durs = [0.5000 1.0000 2.0000 4.0000 16.0000];
c = {'I','II','III','IV'};
cnt = 0;
cnt_c = 0;
% Build figure
cfn = pb_newfig(cfn);
sgtitle([CONDITION ' (s00' num2str(cdir(fseps(end-1)-1)) ')']);
for iM = 1:NMODEL
for iSP = 1:NDUR
cnt = cnt+1;
h(iM,iSP) = subplot(NMODEL,NDUR,cnt);
if cnt < 6; title([num2str(durs(iSP)) ' ms']); end
if mod(cnt,5) == 1; cnt_c = cnt_c+1; ylabel(c{cnt_c}); end
hold on;
axis square;
xlim([-50 50]);
ylim([-50 50]);
pb_dline;
% Get parameters for analysis
M(iM,iSP).dGy = [];
M(iM,iSP).dGx = [];
L(iSP).RT = [];
L(iSP).Cs = [];
L(iSP).Hc = [];
L(iSP).Eh = [];
L(iSP).Tr = [];
L(iSP).G = [];