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efc1_analyze.m
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function varargout=efc1_analyze(what, varargin)
addpath('functions/')
localPath = '/Users/mnlmrc/Documents/';
path = localPath;
addpath([path 'GitHub/spmj_tools/'])
addpath([path 'GitHub/dataframe/util/'])
% setting paths:
usr_path = '/Volumes/diedrichsen_data$/data/SequenceAndChord/ExtFlexChord/EFC1/analysis/';
project_path = usr_path;
% colors:
colors_red = [[255, 219, 219] ; [255, 146, 146] ; [255, 73, 73] ; [255, 0, 0] ; [182, 0, 0]]/255;
colors_gray = ['#d3d3d3' ; '#b9b9b9' ; '#868686' ; '#6d6d6d' ; '#535353'];
colors_blue = ['#dbecff' ; '#a8d1ff' ; '#429bff' ; '#0f80ff' ; '#0067db'];
colors_green = ['#9bdbb1' ; '#3aa35f' ; '#3aa35f' ; '#2d7d49' ; '#1f5833'];
colors_cyan = ['#adecee' ; '#83e2e5' ; '#2ecfd4' ; '#23a8ac' ; '#1b7e81'];
colors_random = ['#773344' ; '#E3B5A4' ; '#83A0A0' ; '#0B0014' ; '#D44D5C'];
colors_blue = hex2rgb(colors_blue);
colors_green = hex2rgb(colors_green);
colors_cyan = hex2rgb(colors_cyan);
colors_gray = hex2rgb(colors_gray);
colors_random = hex2rgb(colors_random);
colors_measures = [colors_red(3,:) ; colors_cyan(3,:) ; colors_blue(5,:)];
% figure properties:
my_font.label = 10;
my_font.title = 11;
my_font.tick_label = 8;
my_font.legend = 8;
my_font.conf_tick_label = 32;
my_font.conf_label = 36;
my_font.conf_legend = 20;
my_font.conf_title = 36;
% conference fig sizes:
conf.err_width = 3;
conf.line_width = 8;
conf.marker_size = 350;
conf.horz_line_width = 6;
conf.axis_width = 3;
conf.bar_line_width = 2.5;
conf.bar_width = 2;
% paper fig sizes:
paper.err_width = 1.5;
paper.line_width = 2;
paper.lineplot_line_width = 2;
paper.marker_size = 35;
paper.lineplot_marker_size = 3;
paper.horz_line_width = 2;
paper.axis_width = 1;
paper.bar_line_width = 1.5;
paper.bar_width = 1;
switch (what)
case 'subject_routine'
% handling input arguments:
subject_name = 'subj01';
smoothing_win_length = 30;
vararginoptions(varargin,{'subject_name','smoothing_win_length'});
% if a cell containing multiple subjects was given:
if (iscell(subject_name))
for i = 1:length(subject_name)
efc1_subj(subject_name{i},'smoothing_win_length',smoothing_win_length)
end
% if a single subject as a char was given:
else
efc1_subj(subject_name,'smoothing_win_length',smoothing_win_length);
end
case 'make_all_dataframe'
% Calculate RT, MT, Mean Deviation for each trial of each subejct
% and create a struct without the mov signals and save it as a
% single struct called efc1_all.mat
% input arguments:
percent_after_RT = 15; % percentage of movement after RT.
vararginoptions(varargin,{'percent_after_RT'});
% getting subject files:
files = dir(fullfile(usr_path, 'Desktop', 'Projects', 'EFC1', 'analysis', 'efc1_*_raw.tsv'));
movFiles = dir(fullfile(usr_path, 'Desktop', 'Projects', 'EFC1', 'analysis', 'efc1_*_mov.mat'));
% container to hold all subjects' data:
ANA = [];
% looping through subjects' data:
for i = 1:length({files(:).name})
% load subject data:
tmp_data = dload(fullfile(files(i).folder, files(i).name));
tmp_mov = load(fullfile(movFiles(i).folder, movFiles(i).name));
tmp_mov = tmp_mov.MOV_struct;
mean_dev_tmp = zeros(length(tmp_data.BN),1);
rt_tmp = zeros(length(tmp_data.BN),1);
mt_tmp = zeros(size(rt_tmp));
v_dev_tmp = zeros(length(tmp_data.BN),5);
first_finger_tmp = zeros(size(rt_tmp));
diff_force_f1 = zeros(length(tmp_data.BN),1);
diff_force_f2 = zeros(length(tmp_data.BN),1);
diff_force_f3 = zeros(length(tmp_data.BN),1);
diff_force_f4 = zeros(length(tmp_data.BN),1);
diff_force_f5 = zeros(length(tmp_data.BN),1);
force_f1 = zeros(length(tmp_data.BN),1);
force_f2 = zeros(length(tmp_data.BN),1);
force_f3 = zeros(length(tmp_data.BN),1);
force_f4 = zeros(length(tmp_data.BN),1);
force_f5 = zeros(length(tmp_data.BN),1);
force_e1 = zeros(length(tmp_data.BN),1);
force_e2 = zeros(length(tmp_data.BN),1);
force_e3 = zeros(length(tmp_data.BN),1);
force_e4 = zeros(length(tmp_data.BN),1);
force_e5 = zeros(length(tmp_data.BN),1);
% loop through trials:
for j = 1:length(tmp_data.BN)
% if trial was correct:
if (tmp_data.trialCorr(j) == 1)
% calculate and store mean dev:
mean_dev_tmp(j) = calculate_mean_dev(tmp_mov{j}, tmp_data.chordID(j), ...
tmp_data.baselineTopThresh(j), tmp_data.RT(j), ...
tmp_data.fGain1(j), tmp_data.fGain2(j), tmp_data.fGain3(j), ...
tmp_data.fGain4(j), tmp_data.fGain5(j));
% calculate and stor rt and mt:
[rt_tmp(j),mt_tmp(j),first_finger_tmp(j)] = calculate_rt_mt(tmp_mov{j}, tmp_data.chordID(j), ...
tmp_data.baselineTopThresh(j), tmp_data.RT(j), ...
tmp_data.fGain1(j), tmp_data.fGain2(j), tmp_data.fGain3(j), ...
tmp_data.fGain4(j), tmp_data.fGain5(j));
% calculate initial deviation vector from ideal trajectory:
v_dev_tmp(j,:) = calculate_dev_vector(tmp_mov{j}, tmp_data.chordID(j), ...
tmp_data.baselineTopThresh(j), tmp_data.RT(j), percent_after_RT, ...
tmp_data.fGain1(j), tmp_data.fGain2(j), tmp_data.fGain3(j), ...
tmp_data.fGain4(j), tmp_data.fGain5(j));
% average force:
idx_completion = find(tmp_mov{j}(:,1)==3);
idx_completion = idx_completion(end);
diff_force_f1(j) = mean(tmp_mov{j}(idx_completion-299:idx_completion,14));
diff_force_f2(j) = mean(tmp_mov{j}(idx_completion-299:idx_completion,15));
diff_force_f3(j) = mean(tmp_mov{j}(idx_completion-299:idx_completion,16));
diff_force_f4(j) = mean(tmp_mov{j}(idx_completion-299:idx_completion,17));
diff_force_f5(j) = mean(tmp_mov{j}(idx_completion-299:idx_completion,18));
force_f1(j) = mean(tmp_mov{j}(idx_completion-299:idx_completion,9));
force_f2(j) = mean(tmp_mov{j}(idx_completion-299:idx_completion,10));
force_f3(j) = mean(tmp_mov{j}(idx_completion-299:idx_completion,11));
force_f4(j) = mean(tmp_mov{j}(idx_completion-299:idx_completion,12));
force_f5(j) = mean(tmp_mov{j}(idx_completion-299:idx_completion,13));
force_e1(j) = mean(tmp_mov{j}(idx_completion-299:idx_completion,4));
force_e2(j) = mean(tmp_mov{j}(idx_completion-299:idx_completion,5));
force_e3(j) = mean(tmp_mov{j}(idx_completion-299:idx_completion,6));
force_e4(j) = mean(tmp_mov{j}(idx_completion-299:idx_completion,7));
force_e5(j) = mean(tmp_mov{j}(idx_completion-299:idx_completion,8));
% if trial was incorrect:
else
diff_force_f1(j) = -1;
diff_force_f2(j) = -1;
diff_force_f3(j) = -1;
diff_force_f4(j) = -1;
diff_force_f5(j) = -1;
force_f1(j) = -1;
force_f2(j) = -1;
force_f3(j) = -1;
force_f4(j) = -1;
force_f5(j) = -1;
force_e1(j) = -1;
force_e2(j) = -1;
force_e3(j) = -1;
force_e4(j) = -1;
force_e5(j) = -1;
% mean dev:
mean_dev_tmp(j) = -1;
rt_tmp(j) = -1;
mt_tmp(j) = -1;
v_dev_tmp(j,:) = -1*ones(1,size(v_dev_tmp,2));
first_finger_tmp(j) = -1;
end
end
% removing unnecessary fields:
tmp_data = rmfield(tmp_data,'RT');
tmp_data = rmfield(tmp_data,'trialPoint');
% adding the calculated parameters to the subject struct:
tmp_data.RT = rt_tmp;
tmp_data.MT = mt_tmp;
tmp_data.first_finger = first_finger_tmp;
tmp_data.MD = mean_dev_tmp;
tmp_data.v_dev1 = v_dev_tmp(:,1);
tmp_data.v_dev2 = v_dev_tmp(:,2);
tmp_data.v_dev3 = v_dev_tmp(:,3);
tmp_data.v_dev4 = v_dev_tmp(:,4);
tmp_data.v_dev5 = v_dev_tmp(:,5);
sess = (tmp_data.BN<=12) + 2*(tmp_data.BN>=13 & tmp_data.BN<=24) + 3*(tmp_data.BN>=25 & tmp_data.BN<=36) + 4*(tmp_data.BN>=37 & tmp_data.BN<=48);
tmp_data.sess = sess;
tmp_data.diff_force_f1 = diff_force_f1;
tmp_data.diff_force_f2 = diff_force_f2;
tmp_data.diff_force_f3 = diff_force_f3;
tmp_data.diff_force_f4 = diff_force_f4;
tmp_data.diff_force_f5 = diff_force_f5;
tmp_data.force_f1 = force_f1;
tmp_data.force_f2 = force_f2;
tmp_data.force_f3 = force_f3;
tmp_data.force_f4 = force_f4;
tmp_data.force_f5 = force_f5;
tmp_data.force_e1 = force_e1;
tmp_data.force_e2 = force_e2;
tmp_data.force_e3 = force_e3;
tmp_data.force_e4 = force_e4;
tmp_data.force_e5 = force_e5;
% adding subject data to ANA:
ANA=addstruct(ANA,tmp_data,'row','force');
end
% adding number of active fingers:
ANA.num_fingers = get_num_active_fingers(ANA.chordID);
dsave(fullfile(usr_path,'Desktop','Projects','EFC1','analysis','efc1_all.tsv'),ANA);
case 'make_chord_dataframe'
% fields:
% sn, sess, chordID, num_trials, num_fingers, MD, MT, RT, MD_std, MT_std, RT_std
% IMPORTANT: If new subjects are added, you should run
% subject_routine and make_all_dataframe before running this
% function.
% handling input args:
exclude_1st_rep = 0;
out_file_name = 'efc1_chord.tsv';
vararginoptions(varargin,{'exclude_1st_rep','out_file_name'})
% load trial dataframe:
data = dload(fullfile(project_path, 'analysis', 'efc1_all.tsv'));
if exclude_1st_rep
tmp_idx = mod(data.TN,5);
% removing 1st rep from data:
data = getrow(data,tmp_idx~=1);
end
subjects = unique(data.sn);
chords = generateAllChords;
n = get_num_active_fingers(chords);
sess = unique(data.sess);
% container to hold the dataframe:
ANA = [];
% loop on subjects:
for i = 1:length(subjects)
tmp = [];
% loop on sess:
cnt = 1;
for j = 1:length(sess)
% loop on chords:
for k = 1:length(chords)
tmp.sn(cnt,1) = subjects(i);
tmp.sess(cnt,1) = sess(j);
tmp.chordID(cnt,1) = chords(k);
row = data.sn==subjects(i) & data.sess==sess(j) & data.chordID==chords(k) & data.trialCorr==1;
tmp.num_trials(cnt,1) = sum(row);
tmp.accuracy(cnt,1) = sum(row)/5;
tmp.num_fingers(cnt,1) = n(k);
tmp.MD(cnt,1) = mean(data.MD(row));
tmp.MT(cnt,1) = mean(data.MT(row));
tmp.RT(cnt,1) = mean(data.RT(row));
tmp.MD_std(cnt,1) = std(data.MD(row));
tmp.MT_std(cnt,1) = std(data.MT(row));
tmp.RT_std(cnt,1) = std(data.RT(row));
tmp.diff_force_f1(cnt,1) = mean(data.diff_force_f1(row));
tmp.diff_force_f2(cnt,1) = mean(data.diff_force_f2(row));
tmp.diff_force_f3(cnt,1) = mean(data.diff_force_f3(row));
tmp.diff_force_f4(cnt,1) = mean(data.diff_force_f4(row));
tmp.diff_force_f5(cnt,1) = mean(data.diff_force_f5(row));
tmp.force_f1(cnt,1) = mean(data.force_f1(row));
tmp.force_f2(cnt,1) = mean(data.force_f2(row));
tmp.force_f3(cnt,1) = mean(data.force_f3(row));
tmp.force_f4(cnt,1) = mean(data.force_f4(row));
tmp.force_f5(cnt,1) = mean(data.force_f5(row));
tmp.force_e1(cnt,1) = mean(data.force_e1(row));
tmp.force_e2(cnt,1) = mean(data.force_e2(row));
tmp.force_e3(cnt,1) = mean(data.force_e3(row));
tmp.force_e4(cnt,1) = mean(data.force_e4(row));
tmp.force_e5(cnt,1) = mean(data.force_e5(row));
cnt = cnt+1;
end
end
ANA = addstruct(ANA,tmp,'row','force');
end
dsave(fullfile(project_path,'analysis',out_file_name),ANA);
case 'success_rate'
chords = generateAllChords;
data = dload(fullfile(project_path, 'analysis', 'efc1_chord.tsv'));
subjects = unique(data.sn);
C = [];
for i = 1:length(subjects)
% loop on chords:
acc_tmp = zeros(size(chords));
for j = 1:length(chords)
% accuracy of chords:
acc_tmp(j) = mean(data.accuracy(data.sn==subjects(i) & data.chordID==chords(j)));
end
% The most undoable chords of subject in the first
% two sessions:
[acc,idx] = sort(acc_tmp);
% the most undoable chord:
hard_chord = chords(idx(1));
C.sn(i,1) = subjects(i);
C.chordID(i,1) = hard_chord;
% Accuracies:
C.acc_diff_s01(i,1) = data.accuracy(data.sn==subjects(i) & data.sess==1 & data.chordID==hard_chord);
C.acc_diff_s02(i,1) = data.accuracy(data.sn==subjects(i) & data.sess==2 & data.chordID==hard_chord);
C.acc_diff_s03(i,1) = data.accuracy(data.sn==subjects(i) & data.sess==3 & data.chordID==hard_chord);
C.acc_diff_s04(i,1) = data.accuracy(data.sn==subjects(i) & data.sess==4 & data.chordID==hard_chord);
C.acc_avg_s01(i,1) = mean(data.accuracy(data.sn==subjects(i) & data.sess==1));
C.acc_avg_s02(i,1) = mean(data.accuracy(data.sn==subjects(i) & data.sess==2));
C.acc_avg_s03(i,1) = mean(data.accuracy(data.sn==subjects(i) & data.sess==3));
C.acc_avg_s04(i,1) = mean(data.accuracy(data.sn==subjects(i) & data.sess==4));
end
% plot:
avg_diff = [mean(C.acc_diff_s01) ; mean(C.acc_diff_s02) ; mean(C.acc_diff_s03) ; mean(C.acc_diff_s04)];
sem_diff = [std(C.acc_diff_s01) ; std(C.acc_diff_s02) ; std(C.acc_diff_s03) ; std(C.acc_diff_s04)]/sqrt(length(C.sn));
avg_all = [mean(C.acc_avg_s01) ; mean(C.acc_avg_s02) ; mean(C.acc_avg_s03) ; mean(C.acc_avg_s04)];
sem_all = [std(C.acc_avg_s01) ; std(C.acc_avg_s02) ; std(C.acc_avg_s03) ; std(C.acc_avg_s04)]/sqrt(length(C.sn));
fig_dataframe = [];
fig_dataframe.sess = (1:4)';
fig_dataframe.difficult = avg_diff;
fig_dataframe.difficult_sem = sem_diff;
fig_dataframe.all_chords = avg_all;
fig_dataframe.all_chords_sem = sem_all;
dsave(fullfile(project_path,'analysis','success_rate.tsv'),fig_dataframe);
varargout{1} = C;
case 'corr_reliability_nfingers'
chords = generateAllChords;
measure = 'MD';
vararginoptions(varargin,{'chords','measure'})
data = dload(fullfile(project_path, 'analysis', 'efc1_chord.tsv'));
% getting the values of measure:
values = eval(['data.' measure]);
% remove nan values - subjects may have missed all 5 reps:
nan_idx = isnan(values);
values(nan_idx) = 0;
% reliability estimation:
[c_g, c_gs] = reliability_corr(values(data.sess>=3), data.sn(data.sess>=3), data.sess(data.sess>=3), ...
'cond_vec', data.num_fingers(data.sess>=3));
% % plot:
% y = [];
% figure;
% ax1 = axes('Units','centimeters', 'Position', [2 2 4.8 5],'Box','off');
% for i = 1:length(unique(data.num_fingers))
% y(i,:) = [c_g{i}/c_gse{i} (c_gs{i}-c_g{i})/c_gse{i} (c_gse{i}-c_gs{i})/c_gse{i}];
% b = bar(i,y(i,:),'stacked','FaceColor','flat');
% b(1).CData = [238, 146, 106]/255; % global var
% b(2).CData = [36, 168, 255]/255; % subj var
% b(3).CData = [0.8 0.8 0.8]; % noise var
% b(1).EdgeColor = [1 1 1];
% b(2).EdgeColor = [1 1 1];
% b(3).EdgeColor = [1 1 1];
% b(1).LineWidth = 1;
% b(2).LineWidth = 1;
% b(3).LineWidth = 1;
% hold on
% end
% drawline(1,'dir','horz','color',[0.7 0.7 0.7])
% drawline(0,'dir','horz','color',[0.7 0.7 0.7])
% set(gca, 'XTick', 1:5);
% h = gca;
% h.YTick = 0:0.2:1;
% box off;
% title([measure ' Reliability'],'FontSize',my_font.title)
% xlabel('num fingers','FontSize',my_font.xlabel)
% ylabel('correlation','FontSize',my_font.ylabel)
% legend('global','subject','noise')
% legend boxoff
% ylim([-0.1,1.2])
%
% varargout{1} = [c_g, c_gs, c_gse];
case 'training_performance'
% loading data:
data = dload(fullfile(project_path,'analysis','efc1_chord.tsv'));
subj = unique(data.sn);
% calaculating avg improvement from sess 1 to 4:
C = [];
for i = 1:length(subj)
C.sn(i,1) = subj(i);
% sess 1 and 4 data:
val1 = mean(data.MD(data.sn==subj(i) & data.sess==1),'omitmissing');
val4 = mean(data.MD(data.sn==subj(i) & data.sess==4),'omitmissing');
C.perc_improvement(i,1) = (val1-val4)/val1;
end
fig_dataframe = [];
% sem for all finger counts separately:
[sem_subj, X, Y, cond, SN] = get_sem(data.MD, data.sn, data.sess, data.num_fingers);
fig_dataframe.sn = SN;
fig_dataframe.sess = X;
fig_dataframe.finger_count = cond;
fig_dataframe.MD = Y;
[sem_subj, X, Y, cond, SN] = get_sem(data.RT, data.sn, data.sess, data.num_fingers);
fig_dataframe.RT = Y;
dsave(fullfile(project_path,'analysis','training_performance.tsv'),fig_dataframe);
% doing stats:
% idx_exlude_nans = ~isnan(values);
% stats = rm_anova2(values(idx_exlude_nans),data.sn(idx_exlude_nans),data.sess(idx_exlude_nans),data.num_fingers(idx_exlude_nans),{'sess','num_fingers'});
% % T = MANOVA2rp(data.sn(idx_exlude_nans),[data.sess(idx_exlude_nans),data.num_fingers(idx_exlude_nans)],values(idx_exlude_nans));
%
% percent improvement:
% fprintf('%s improvement from sess 1 to 4:\n %.4f%% +- %.4f SEM\n',measure, mean(C.perc_improvement)*100, 100*std(C.perc_improvement)/sqrt(length(C.perc_improvement)))
% varargout{1} = stats;
% varargout{2} = C;
case 'similarity_of_measures'
% loading data:
data = dload(fullfile(project_path,'analysis','efc1_chord.tsv'));
subj = unique(data.sn);
sess = unique(data.sess);
C = [];
C_total = [];
cnt = 1;
for i = 1:length(subj)
for j = 1:length(sess)
% correlation without separating num fingers:
row = data.sn==subj(i) & data.sess==sess(j);
C_tmp.sn = subj(j);
C_tmp.sess = sess(j);
C_tmp.rt_mt = corr(data.RT(row),data.MT(row),'rows','complete');
C_tmp.rt_md = corr(data.RT(row),data.MD(row),'rows','complete');
C_tmp.mt_md = corr(data.MT(row),data.MD(row),'rows','complete');
C_total = addstruct(C_total,C_tmp,'row',1);
% loop on number of fingers:
for n = 1:5
row = data.sn==subj(i) & data.sess==sess(j) & data.num_fingers==n;
% getting the data for each session:
rt = data.RT(row);
mt = data.MT(row);
md = data.MD(row);
% correlation of measures within subj:
C.sn(cnt,1) = subj(i);
C.sess(cnt,1) = sess(j);
C.num_fingers(cnt,1) = n;
C.rt_mt(cnt,1) = corr(rt,mt,'rows','complete');
C.rt_md(cnt,1) = corr(rt,md,'rows','complete');
C.mt_md(cnt,1) = corr(mt,md,'rows','complete');
cnt = cnt+1;
end
end
end
% printing values of total corr:
C_rt_mt = get_sem(C_total.rt_mt, C_total.sn, ones(size(C_total.sn)), ones(size(C_total.sn)));
C_rt_md = get_sem(C_total.rt_md, C_total.sn, ones(size(C_total.sn)), ones(size(C_total.sn)));
C_mt_md = get_sem(C_total.mt_md, C_total.sn, ones(size(C_total.sn)), ones(size(C_total.sn)));
fprintf('total corr(rt,mt) = %.4f\n',C_rt_mt.y);
fprintf('total corr(rt,md) = %.4f\n',C_rt_md.y);
fprintf('total corr(mt,md) = %.4f\n',C_mt_md.y);
% getting the summary of data:
C_rt_mt = get_sem(C.rt_mt, C.sn, ones(size(C.sn)), C.num_fingers);
C_rt_md = get_sem(C.rt_md, C.sn, ones(size(C.sn)), C.num_fingers);
C_mt_md = get_sem(C.mt_md, C.sn, ones(size(C.sn)), C.num_fingers);
C_rt_mt.y
mean(C_rt_mt.y)
C_rt_md.y
mean(C_rt_md.y)
C_mt_md.y
mean(C_mt_md.y)
% PLOT - corr vs num_fingers:
figure;
ax1 = axes('Units', 'centimeters', 'Position', [2 2 4.8 5],'Box','off');
drawline(0,'dir','horz','color',[0.7 0.7 0.7],'lim',[0,6]); hold on;
% plot(1:5, C_rt_mt.y, 'Color', (colors_measures(1,:)+colors_measures(2,:))/2, 'LineWidth', 2);
% errorbar(1:5, C_rt_mt.y, C_rt_mt.sem, 'LineStyle', 'none', 'CapSize', 0, 'Color', (colors_measures(1,:)+colors_measures(2,:))/2);
% scatter(1:5, C_rt_mt.y, 15, 'MarkerFaceColor', (colors_measures(1,:)+colors_measures(2,:))/2, 'MarkerEdgeColor', (colors_measures(1,:)+colors_measures(2,:))/2); hold on;
plot(1:5, C_rt_md.y, 'Color', (colors_measures(1,:)+colors_measures(3,:))/2, 'LineWidth', 2);
errorbar(1:5, C_rt_md.y, C_rt_md.sem, 'LineStyle', 'none', 'CapSize', 0, 'Color', (colors_measures(1,:)+colors_measures(3,:))/2);
scatter(1:5, C_rt_md.y, 15, 'MarkerFaceColor', (colors_measures(1,:)+colors_measures(3,:))/2, 'MarkerEdgeColor', (colors_measures(1,:)+colors_measures(3,:))/2);
% plot(1:5, C_mt_md.y, 'Color', (colors_measures(2,:)+colors_measures(3,:))/2, 'LineWidth', 2);
% errorbar(1:5, C_mt_md.y, C_mt_md.sem, 'LineStyle', 'none', 'CapSize', 0, 'Color', (colors_measures(2,:)+colors_measures(3,:))/2);
% scatter(1:5, C_mt_md.y, 15, 'MarkerFaceColor', (colors_measures(2,:)+colors_measures(3,:))/2, 'MarkerEdgeColor', (colors_measures(2,:)+colors_measures(3,:))/2);
% lgd = legend({'','r_{RT,MT}','','','r_{RT,MD}','','','r_{MT,MD}','',''});
% legend boxoff
% fontsize(lgd,6,'points')
ylim([-0.1,1.05])
xlim([0 6])
xlabel('finger count','FontSize',my_font.xlabel)
ylabel('correlation','FontSize',my_font.tick_label)
h = gca;
h.YTick = 0:0.25:1;
h.XTick = 1:5;
h.XAxis.FontSize = my_font.tick_label;
h.YAxis.FontSize = my_font.tick_label;
fontname("Arial")
varargout{1} = C;
case 'selected_chords_reliability'
% handling input args:
blocks = [25, 48];
% check reliability for these chords:
chords = [11912,22921,21911,12922,12191,21292,19121,29212,12112,21221,21121,21121,12212,11212,22121,21211,21122]';
plot_option = 1;
vararginoptions(varargin,{'blocks','chords','plot_option'})
% loading data:
data = dload(fullfile(project_path,'analysis','efc1_all.tsv'));
subjects = unique(data.sn);
% extracting avg mean dev of all subjects:
C = [];
% loop on subjs:
for sn = 1:length(subjects)
% container for subjects:
tmp = [];
% loop on chords:
for i = 1:length(chords)
tmp.sn(i,1) = subjects(sn);
tmp.chordID(i,1) = chords(i);
tmp.num_fingers(i,1) = get_num_active_fingers(chords(i));
row = data.sn==subjects(sn) & data.BN>=blocks(1) & data.BN<=blocks(2) & data.chordID==chords(i) & data.trialCorr==1;
tmp.MD(i,1) = mean(data.MD(row));
tmp.RT(i,1) = median(data.RT(row));
tmp.MT(i,1) = median(data.MT(row));
end
% concatenating subjects:
C = addstruct(C,tmp,'row','force');
end
% getting the values in matrix format
MD = zeros(length(chords),length(sn));
RT = zeros(length(chords),length(sn));
MT = zeros(length(chords),length(sn));
for sn = 1:length(subjects)
MD(:,sn) = C.MD(C.sn==subjects(sn));
RT(:,sn) = C.RT(C.sn==subjects(sn));
MT(:,sn) = C.MT(C.sn==subjects(sn));
end
% calculating correlations leave_one_out:
corr_struct = [];
for sn = 1:length(subjects)
[r,p] = corrcoef(MD(:,sn),mean(MD(:,subjects~=subjects(sn)),2));
corr_struct.MD(sn,1) = r(2);
corr_struct.MD_p(sn,1) = p(2);
[r,p] = corrcoef(MT(:,sn),mean(MT(:,subjects~=subjects(sn)),2));
corr_struct.MT(sn,1) = r(2);
corr_struct.MT_p(sn,1) = p(2);
[r,p] = corrcoef(RT(:,sn),mean(RT(:,subjects~=subjects(sn)),2));
corr_struct.RT(sn,1) = r(2);
corr_struct.RT_p(sn,1) = p(2);
end
% calculating correlations subejct to subject:
[r_MD,p_MD] = corrcoef(MD);
[r_MT,p_MT] = corrcoef(MT);
[r_RT,p_RT] = corrcoef(RT);
if plot_option
% figures:
figure;
subplot(1,3,1)
bar(1,mean(corr_struct.MD))
hold on
scatter(ones(length(subjects),1),corr_struct.MD,20,'k','filled');
title(sprintf('MD reliability , block %d to %d',blocks(1),blocks(2)))
ylabel('corr leave one out subj')
ylim([-1,1])
subplot(1,3,2)
bar(1,mean(corr_struct.MT))
hold on
scatter(ones(length(subjects),1),corr_struct.MT,20,'k','filled');
title(sprintf('MT reliability , block %d to %d',blocks(1),blocks(2)))
ylabel('corr leave one out subj')
ylim([-1,1])
subplot(1,3,3)
bar(1,mean(corr_struct.RT))
hold on
scatter(ones(length(subjects),1),corr_struct.RT,20,'k','filled');
title(sprintf('RT reliability , block %d to %d',blocks(1),blocks(2)))
ylabel('corr leave one out subj')
ylim([-1,1])
% subject-subject figures;
figure; imagesc(r_MD); title('MD , subject-subject correlation')
colorbar
caxis([-1 1])
figure; imagesc(r_MT); title('MT , subject-subject correlation')
colorbar
caxis([-1 1])
figure; imagesc(r_RT); title('RT , subject-subject correlation')
colorbar
caxis([-1 1])
end
varargout{1} = C;
varargout{2} = corr_struct;
case 'selected_chord_trends'
% handling input args:
% check reliability for these chords:
% EFC EMG Pilot 1:
chords = [19999,91999,99199,99919,99991,29999,92999,99299,99929,99992,...
11912,22921,21911,12922,12191,21292,19121,29212,12112,21221,21121,21121,12212,11212,22121,21211,21122]';
% Sheena:
% chords = [99922,99292,92992,92929,92922,92292,29992,29929,29922,29299,29292,29229,22929,22299]';
measure = 'MD';
subjects = [];
conference_fig = 0;
vararginoptions(varargin,{'measure','chords','subjects','conference_fig'})
% loading data:
data = dload(fullfile(project_path,'analysis','efc1_chord.tsv'));
% rows for selected chords:
row = arrayfun(@(x) ~isempty(intersect(x,chords)), data.chordID);
data = getrow(data,row);
if isempty(subjects)
subjects = unique(data.sn);
else
row = arrayfun(@(x) ~isempty(intersect(x,subjects)), data.sn);
data = getrow(data,row);
end
% getting the values of measure:
values = eval(['data.' measure]);
cond_vec = data.num_fingers;
% cond_vec(cond_vec>1) = 2;
[sem_subj, X_subj, Y_subj, COND] = get_sem(values, data.sn, data.sess, cond_vec);
x = [];
y = [];
n = [];
conditions = unique(cond_vec);
cnt = 1;
for i = 1:length(subjects)
for j = 1:length(unique(data.sess))
for k = 1:length(conditions)
x(cnt,1) = j;
y(cnt,1) = mean(values(data.sess==j & data.sn==subjects(i) & cond_vec==conditions(k)),'omitmissing');
n(cnt,1) = conditions(k);
cnt = cnt+1;
end
end
end
if ~conference_fig
% avg trend acorss sessions:
fig = figure('Position', [500 500 190 200]);
fontsize(fig, my_font.tick_label, 'points')
errorbar(sem_subj.partitions(sem_subj.cond==1),sem_subj.y(sem_subj.cond==1),sem_subj.sem(sem_subj.cond==1),'LineStyle','none','Color',colors_blue(2,:)); hold on;
lineplot(x(n==conditions(1)),y(n==conditions(1)),'markertype','o','markersize',5,'markerfill',colors_blue(2,:),'markercolor',colors_blue(2,:),'linecolor',colors_blue(2,:),'linewidth',2,'errorbars','');
hold on
errorbar(sem_subj.partitions(sem_subj.cond==3),sem_subj.y(sem_subj.cond==3),sem_subj.sem(sem_subj.cond==3),'LineStyle','none','Color',colors_blue(3,:))
lineplot(x(n==conditions(2)),y(n==conditions(2)),'markertype','o','markersize',5,'markerfill',colors_blue(3,:),'markercolor',colors_blue(3,:),'linecolor',colors_blue(3,:),'linewidth',2,'errorbars','');
errorbar(sem_subj.partitions(sem_subj.cond==5),sem_subj.y(sem_subj.cond==5),sem_subj.sem(sem_subj.cond==5),'LineStyle','none','Color',colors_blue(5,:))
lineplot(x(n==conditions(3)),y(n==conditions(3)),'markertype','o','markersize',5,'markerfill',colors_blue(5,:),'markercolor',colors_blue(5,:),'linecolor',colors_blue(5,:),'linewidth',2,'errorbars','');
% scatter(X_subj(COND==1),Y_subj(COND==1),10,'MarkerEdgeColor',colors_blue(1,:),'MarkerFaceColor',colors_blue(1,:))
% scatter(X_subj(COND==2),Y_subj(COND==2),10,'MarkerEdgeColor',colors_blue(5,:),'MarkerFaceColor',colors_blue(5,:))
legend('','single finger','','chord 3f','','chord 5f');
legend boxoff
xlabel('sess','FontSize',my_font.xlabel)
ylabel([replace(measure,'_',' ')],'FontSize',my_font.title)
% title([replace(measure,'_',' ')],'FontSize',my_font.title)
% ylim([0.2 2.7])
% ylim([0 3500])
ylim([0 500])
h = gca;
h.YTick = linspace(h.YTick(1),h.YTick(end),5);
else
% avg trend acorss sessions:
fig = figure('Units','centimeters', 'Position',[15 15 25 20]);
fontsize(fig, my_font.conf_tick_label, 'points')
errorbar(sem_subj.partitions(sem_subj.cond==1),sem_subj.y(sem_subj.cond==1),sem_subj.sem(sem_subj.cond==1),'LineStyle','none','Color',colors_blue(2,:),'LineWidth',conf.err_width); hold on;
lineplot(x(n==conditions(1)),y(n==conditions(1)),'markertype','o','markersize',12,'markerfill',colors_blue(2,:),'markercolor',colors_blue(2,:),'linecolor',colors_blue(2,:),'linewidth',6,'errorbars','');
hold on
errorbar(sem_subj.partitions(sem_subj.cond==3),sem_subj.y(sem_subj.cond==3),sem_subj.sem(sem_subj.cond==3),'LineStyle','none','Color',colors_blue(3,:),'LineWidth',conf.err_width)
lineplot(x(n==conditions(2)),y(n==conditions(2)),'markertype','o','markersize',12,'markerfill',colors_blue(3,:),'markercolor',colors_blue(3,:),'linecolor',colors_blue(3,:),'linewidth',6,'errorbars','');
errorbar(sem_subj.partitions(sem_subj.cond==5),sem_subj.y(sem_subj.cond==5),sem_subj.sem(sem_subj.cond==5),'LineStyle','none','Color',colors_blue(5,:),'LineWidth',conf.err_width)
lineplot(x(n==conditions(3)),y(n==conditions(3)),'markertype','o','markersize',12,'markerfill',colors_blue(5,:),'markercolor',colors_blue(5,:),'linecolor',colors_blue(5,:),'linewidth',6,'errorbars','');
% scatter(X_subj(COND==1),Y_subj(COND==1),10,'MarkerEdgeColor',colors_blue(1,:),'MarkerFaceColor',colors_blue(1,:))
% scatter(X_subj(COND==2),Y_subj(COND==2),10,'MarkerEdgeColor',colors_blue(5,:),'MarkerFaceColor',colors_blue(5,:))
xlim([0.8 4.2])
lgd = legend('','1-finger','','3-finger','','5-finger');
legend boxoff
fontsize(lgd,my_font.conf_legend,'points')
xlabel('days','FontSize',my_font.conf_label)
ylabel([measure ,' [ms]'],'FontSize',my_font.conf_label)
if measure=='MD'
ylim([0.2 2.7])
elseif measure=='RT'
ylim([150 450])
elseif measure=='MT'
ylim([0 3500])
end
h = gca;
h.YTick = linspace(h.YTick(1),h.YTick(end),3);
h.XAxis.FontSize = my_font.conf_tick_label;
h.YAxis.FontSize = my_font.conf_tick_label;
h.LineWidth = conf.axis_width;
fontname("Arial")
end
case 'var_reliability_overall'
chords = generateAllChords;
measure = 'MD';
centered = 1;
vararginoptions(varargin,{'chords','measure','centered'})
data = dload(fullfile(project_path, 'analysis', 'efc1_chord.tsv'));
% getting the values of measure:
values = eval(['data.' measure]);
% remove nan values - subjects may have missed all 5 reps:
nan_idx = isnan(values);
values(nan_idx) = 0;
% reliability estimation:
[v_g, v_gs, v_gse] = reliability_var(values(data.sess>=3), data.sn(data.sess>=3), data.sess(data.sess>=3), 'centered', centered);
% plot:
fig = figure('Units','centimeters','Position',[30 30 6 7]);
fontsize(fig, my_font.tick_label, "points")
bar(1,1,'FaceColor','flat','EdgeColor',[1,1,1],'LineWidth',1,'CData',[0.8 0.8 0.8])
hold on
bar(1,1-(v_gs-v_g)/v_gse,'FaceColor','flat','EdgeColor',[1,1,1],'LineWidth',1,'CData',[36, 168, 255]/255)
bar(1,v_g/v_gse,'FaceColor','flat','EdgeColor',[1,1,1],'LineWidth',1,'CData',[238, 146, 106]/255)
drawline(1,'dir','horz','color',[0.7 0.7 0.7])
drawline(0,'dir','horz','color',[0.7 0.7 0.7])
box off;
h = gca;
h.YTick = [];
% h.XTick = [];
title([measure ' Reliability'],'FontSize',my_font.title)
xlabel('All Chords','FontSize',my_font.xlabel)
% legend('global','subject','noise')
% legend boxoff
ylim([-0.1,1.2])
case 'var_reliability_nfingers'
chords = generateAllChords;
measure = 'MD';
centered = 1;
vararginoptions(varargin,{'chords','measure','centered'})
data = dload(fullfile(project_path, 'analysis', 'efc1_chord.tsv'));
% getting the values of measure:
values = eval(['data.' measure]);
% remove nan values - subjects may have missed all 5 reps:
nan_idx = isnan(values);
values(nan_idx) = 0;
% reliability estimation:
[v_g, v_gs, v_gse] = reliability_var(values(data.sess>=3), data.sn(data.sess>=3), data.sess(data.sess>=3), ...
'cond_vec', data.num_fingers(data.sess>=3), 'centered', centered);
% plot:
y = [];
figure;
ax1 = axes('Units','centimeters', 'Position', [2 2 4 4],'Box','off');
for i = 1:length(unique(data.num_fingers))
y(i,:) = [v_g{i}/v_gse{i} (v_gs{i}-v_g{i})/v_gse{i} (v_gse{i}-v_gs{i})/v_gse{i}];
b = bar(i,y(i,:),'stacked','FaceColor','flat','BarWidth',0.8);
b(1).CData = colors_blue(5,:); % global var
b(2).CData = colors_red(3,:); % subj var
b(3).CData = [0.8 0.8 0.8]; % noise var
b(1).EdgeColor = [1 1 1];
b(2).EdgeColor = [1 1 1];
b(3).EdgeColor = [1 1 1];
b(1).LineWidth = 1;
b(2).LineWidth = 1;
b(3).LineWidth = 1;
hold on
end
drawline(1,'dir','horz','color',[0.7 0.7 0.7])
% drawline(0,'dir','horz','color',[0.7 0.7 0.7])
set(gca, 'XTick', 1:5);
h = gca;
h.YTick = 0:0.2:1;
box off;
% title([measure ' Reliability'],'FontSize',my_font.title)
xlabel('finger count','FontSize',my_font.xlabel)
ylabel('percent variance','FontSize',my_font.ylabel)
% lgd = legend('global','subject','noise');
% legend boxoff
% fontsize(lgd,6,'points')
ylim([0,1.1])
xlim([0.3,5.7])
h.XAxis.FontSize = my_font.tick_label;
h.YAxis.FontSize = my_font.tick_label;
fontname("Arial")
varargout{1} = [v_g, v_gs, v_gse];
case 'repetition_effect'
chords = generateAllChords;
subj_selection = [];
vararginoptions(varargin,{'chords','subj_selection'})
data = dload(fullfile(project_path, 'efc1_all.tsv'));
data = getrow(data,ismember(data.chordID,chords));
if ~isempty(subj_selection)
data = getrow(data,ismember(data.sn,subj_selection));
end
chordID = data.chordID;
% getting the values of measure:
MD = data.MD;
MD(MD==-1) = NaN;
RT = data.RT;
RT(RT==-1) = NaN;
chordID(MD==-1) = NaN;
% putting trials in rows:
n_fing = reshape(data.num_fingers,5,[]);
sess = reshape(data.sess,5,[]);
MD = reshape(MD,5,[]);
RT = reshape(RT,5,[]);
chordID = reshape(chordID,5,[]);
subj = reshape(data.sn,5,[]);
repetitions = 5;
% getting averages within each session and n_fing:
n_fing = n_fing(1,:);
n_fing_unique = unique(n_fing);
sess = sess(1,:);
subj = subj(1,:);
subj_unique = unique(subj);
C = [];
% loop on n_fing:
cnt = 1;
for i = 1:length(n_fing_unique)
% loop on sess:
for j = 1:length(unique(sess))
for sn = 1:length(subj_unique)
C.num_fingers(cnt,1) = n_fing_unique(i);
C.sess(cnt,1) = j;
C.sn(cnt,1) = subj_unique(sn);
% selecting the data for each session, finger count and
% subject:
MD_tmp = MD(:, subj==subj_unique(sn) & n_fing==n_fing_unique(i) & sess==j);
tmp = mean(MD_tmp,2,'omitmissing')';
C.MD_subj_rep1(cnt,1) = tmp(1);
C.MD_subj_rep2(cnt,1) = tmp(2);
C.MD_subj_rep3(cnt,1) = tmp(3);
C.MD_subj_rep4(cnt,1) = tmp(4);
C.MD_subj_rep5(cnt,1) = tmp(5);
RT_tmp = RT(:, subj==subj_unique(sn) & n_fing==n_fing_unique(i) & sess==j);
tmp = mean(RT_tmp,2,'omitmissing')';
C.RT_subj_rep1(cnt,1) = tmp(1);
C.RT_subj_rep2(cnt,1) = tmp(2);
C.RT_subj_rep3(cnt,1) = tmp(3);
C.RT_subj_rep4(cnt,1) = tmp(4);
C.RT_subj_rep5(cnt,1) = tmp(5);
cnt = cnt+1;
end
end
end
% stats, improvement from rep1 to avg of rep2-5:
stats = [];
rep_improvement = [];
value_subj = [C.MD_subj_rep1,C.MD_subj_rep2,C.MD_subj_rep3,C.MD_subj_rep4,C.MD_subj_rep5];
for sn = 1:length(subj_unique)
% values across repetitions for subj sn:
tmp = value_subj(C.sn==subj_unique(sn),:);
% difference of values from 1st rep to average of rep 2 to 5:
diff_rep = tmp(:,1) - mean(tmp(:,2:5),2);
rep_improvement = [rep_improvement ; mean(diff_rep,1)];
end
% onesample t-test on rep_improvement:
% size(rep_improvement)
[t,p] = ttest(rep_improvement,[],1,'onesample');
stats.name(1,1) = {'improvement from rep1 to avg of rep2-5'};
stats.t(1,1) = t;
stats.p(1,1) = p;
stats
% stats, imporovement from rep 2 to 5. rm_anova:
tmp_data = value_subj;
% removing rep 1:
tmp_data(:,1) = [];
% repetitions:
rep = kron(2:5,ones(size(tmp_data,1),1));
% subj:
sn = repmat(C.sn,[1,4]);
% vectorizing:
tmp_data = tmp_data(:);
rep = rep(:);
sn = sn(:);
% rm_anova:
fprintf('\n======== RM Anova for rep 2 to 5: ========\n')
T = MANOVArp(sn,rep,tmp_data);
% MANOVArp(sn,rep,tmp_data)
% Improvement of measure from sess1 to sess4:
B = [];
cnt = 1;
% calculating the benefits within subjects:
for i = 1:length(subj_unique)
avg_improvement = 0;
for j = 1:length(n_fing_unique)
value_sess1 = value_subj(C.sn==subj_unique(i) & C.num_fingers==n_fing_unique(j) & C.sess==1,:);
value_sess4 = value_subj(C.sn==subj_unique(i) & C.num_fingers==n_fing_unique(j) & C.sess==4,:);
avg_improvement = avg_improvement + (value_sess1 - value_sess4) ./ value_sess1 * 100 /length(subj_unique);
end
B.sn(cnt,1) = subj_unique(i);
% percent improvement:
B.benefit(cnt,:) = avg_improvement;
cnt = cnt+1;
end
dsave(fullfile(project_path,'analysis','training_repetition.tsv'),C);