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strint_lh_step2.m
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strint_lh_step2.m
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% This loads in the .mat file generated in step 1
clear all
close all
% Select the subject file
str = computer;
if strcmp(str,'MACI64') == 1
cd('/Volumes/mnl/Data/Adaptation/structural_interference/Post_Step_1_test');
fname = uigetfile('*lh.mat');
fcTrials = xlsread('/Volumes/mnl/Data/Adaptation/structural_interference/Force_Channel_Trials_12_03_16.xlsx','Sheet1');
else
cd('Z:\Data\Adaptation\structural_interference\Post_Step_1_test\'); % Lab PCs
%cd('C:\Users\Alex\Desktop\IFDosing\Post_Step_1_test'); % Home PC
fname = uigetfile('*lh.mat');
fcTrials = xlsread('Z:Data\Adaptation\structural_interference\Force_Channel_Trials_12_03_16.xlsx','Sheet1'); %Lab PCs
%fcTrials = xlsread('C:\Users\Alex\Desktop\IFDosing\Force_Channel_Trials_12_03_16.xlsx','Sheet1'); % Home PC
end
load(fname);
subID = fname(1:18);
numTrials = size(sortData,1); % Number of Trials
fs = 1000; % Sample Rate (Hz)
delta_t = 1/fs; %Sample Period
% toss out trials 41 and 42, these are transition trials between
% kinesthetic and kin+rotation
wrong_trial(41) = 1;
wrong_trial(42) = 1;
channel_trial = zeros(1,numTrials);
channel_trial(fcTrials) = 1;
% Conversion between global and local reference frame (this is due to all
% x,y hand positions being referenced in the global frame, whereas the
% targets in the target table are referenced in a local frame specified in
% Deterit-E
Tx = sortData(1,1).TARGET_TABLE.X_GLOBAL(1) - sortData(1,1).TARGET_TABLE.X(1);
Ty = sortData(1,1).TARGET_TABLE.Y_GLOBAL(1) - sortData(1,1).TARGET_TABLE.Y(1);
%visual baseline, kinesthetic baseline, exposure, and post-exposure
%trial numbers in the sequence
vbTrials = 1:20;
kbTrials = 21:40;
exTrials = 43:182;
peTrials = 183:222;
rotation_type = sortData(1,1).TP_TABLE.Rotation_Type(5);
rotation_amount = sortData(1,1).TP_TABLE.Rotation_Amount(5);
theta(vbTrials) = 0; % rotation in degrees during exposure phase
theta(kbTrials) = 0;
if rotation_type == 1 && strcmp(sortData(1,1).EXPERIMENT.ACTIVE_ARM, 'LEFT') == 1
theta(exTrials) = rotation_amount;
else
theta(exTrials) = 0;
end
theta(peTrials) = 0;
% Find the Cursor Position
% First, translate rotation point to global origin
% Then apply rotation, and translate back to target origin
cursorPosX = cell(numTrials,1);
cursorPosY = cell(numTrials,1);
handPosX = cell(numTrials,1);
handPosY = cell(numTrials,1);
for i = 1:numTrials
handPosX{i,1} = sortData(i).Left_HandX - (-1)*sortData(1).TARGET_TABLE.X_GLOBAL(2)/100; % Translate to global origin
handPosY{i,1} = sortData(i).Left_HandY - sortData(1).TARGET_TABLE.Y_GLOBAL(2)/100;
cursorPosX{i,1} = handPosX{i,1}.*cosd(theta(i)) - handPosY{i,1}.*sind(theta(i)); % Reverse the rotation
cursorPosY{i,1} = handPosX{i,1}.*sind(theta(i)) + handPosY{i,1}.*cosd(theta(i));
cursorPosX{i,1} = cursorPosX{i,1} + (-1)*sortData(1).TARGET_TABLE.X_GLOBAL(2)/100; % Translate back to target origin
cursorPosY{i,1} = cursorPosY{i,1} + sortData(1).TARGET_TABLE.Y_GLOBAL(2)/100;
end
% Find the "Up" and "Down" trials
upBool = zeros(numTrials,1);
for i = 1:numTrials
upBool(i) = sortData(i).TRIAL.TP == 1 || sortData(i).TRIAL.TP == 3 || sortData(i).TRIAL.TP == 5;
end
upBool = upBool';
upTrials = find(upBool == 1); % Trial numbers of "up" targets
upTrials = upTrials';
downTrials = find(upBool == 0);
downTrials = downTrials';
numDataPoints = zeros(numTrials,1);
for i = 1:numTrials
numDataPoints(i) = size(sortData(i).Left_HandX,1); % Number of Data points in each trial
end
vel = cell(numTrials,1);
velPeak = zeros(numTrials,1);
indPeak = zeros(numTrials,1);
for i = 1:numTrials
%Calculate hand speed
vel{i,1} = sqrt(sortData(i,1).Left_HandXVel.^2 + sortData(i,1).Left_HandYVel.^2);
%Find Peak velocity
if wrong_trial(i) == 0
[velPeak(i), indPeak(i)] = max(vel{i,1}(onset(i):offset(i))); % Calculates peak v for movement only
indPeak(i) = indPeak(i) + onset(i); % reindexes to start of data collection
else
velPeak(i) = NaN; indPeak(i) = NaN;
end
end
velPeakTime = indPeak - onset;
%% Movement Time (MT)
%%%%%%%%%%%%%%%%%%%%%%%%%%% Movement Time %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
MT = (offset - onset)/fs;
MT(wrong_trial==1) = NaN;
MT(channel_trial==1) = NaN;
%% MT outlier analysis
data = outlier_t(MT(upTrials(1:10))); % Outlier for visual baseline
MT_c(upTrials(1:10)) = data;
data = outlier_t(MT(downTrials(1:10)));
MT_c(downTrials(1:10)) = data;
clear data;
data = outlier_t(MT(upTrials(11:20))); % Outlier for kin baseline
MT_c(upTrials(11:20)) = data;
data = outlier_t(MT(downTrials(11:20)));
MT_c(downTrials(11:20)) = data;
clear data;
MT_c(upTrials(21)) = NaN; MT_c(downTrials(21)) = NaN; % Toss out catch trials
% data = outlier_t(MT(upTrials(14:23))); % Outlier for first 20 exposure
% MT_c(upTrials(14:23)) = data;
% data = outlier_t(MT(downTrials(14:23)));
% MT_c(downTrials(14:23)) = data;
% clear data;
MT_c(upTrials(22:31)) = MT(upTrials(22:31));
MT_c(downTrials(22:31)) = MT(downTrials(22:31));
data = outlier_t(MT(upTrials(32:91))); % Outlier for last 120 exposure
MT_c(upTrials(32:91)) = data;
data = outlier_t(MT(downTrials(32:91)));
MT_c(downTrials(32:91)) = data;
clear data;
data = outlier_t(MT(upTrials(92:96))); % Outlier for first 10 post-exp
MT_c(upTrials(92:96)) = data;
data = outlier_t(MT(downTrials(92:96)));
MT_c(downTrials(92:96)) = data;
clear data;
data = outlier_t(MT(upTrials(97:111))); % Outlier for last 30 post-exp
MT_c(upTrials(97:111)) = data;
data = outlier_t(MT(downTrials(97:111)));
MT_c(downTrials(97:111)) = data;
clear data;
% transpose and calculate standardized variable
MT_c = MT_c';
bkup_mean = nanmean(MT_c(upTrials(11:20)));
bkup_std = nanstd(MT_c(upTrials(11:20)));
MT_up_st = (MT_c(upTrials) - bkup_mean)/bkup_std;
bkdown_mean = nanmean(MT_c(downTrials(11:20)));
bkdown_std = nanstd(MT_c(downTrials(11:20)));
MT_down_st = (MT_c(downTrials) - bkdown_mean)/bkdown_std;
clear bkup_mean; clear bkup_std; clear bkdown_mean; clear bkdown_std;
%% Plotting Code for MT
figure
set(gcf,'Color','w','Position',[560 528 600 420])
hold on;
subplot('Position',[0.06 0.2 0.1 0.6]); hold on;
plot(upTrials(1:10),MT(upTrials(1:10)),'bo');
hold on
plot(upTrials(1:10),MT_c(upTrials(1:10)),'bx');
hold on
plot(downTrials(1:10),MT(downTrials(1:10)),'ro');
hold on
plot(downTrials(1:10),MT_c(downTrials(1:10)),'rx');
axis([0 20 0 6]); set(gca,'LineWidth',2,'XTick',[1 10 20],'YTick',0:1:6,'YTickLabel',0:1:6,'FontName','Arial','FontSize',10); ylabel('MT [s]'); title('vis-pre','fontsize',11);
hold on
subplot('Position',[0.19 0.2 0.1 0.6]); hold on;
plot(upTrials(11:20)-kbTrials(1)+1,MT(upTrials(11:20)),'bo');
hold on
plot(upTrials(11:20)-kbTrials(1)+1,MT_c(upTrials(11:20)),'bx');
hold on
plot(downTrials(11:20)-kbTrials(1)+1,MT(downTrials(11:20)),'ro');
hold on
plot(downTrials(11:20)-kbTrials(1)+1,MT_c(downTrials(11:20)),'rx');
axis([0 20 0 6]); set(gca,'LineWidth',2,'XTick',[1 10 20],'YTick',[],'YTickLabel',[],'FontName','Arial','FontSize',10); title('kin-pre','fontsize',11);
hold on
subplot('Position',[0.32 0.2 0.4 0.6]); hold on;
plot(upTrials(22:91)-exTrials(1)+1,MT(upTrials(22:91)),'bo');
hold on
plot(upTrials(22:91)-exTrials(1)+1,MT_c(upTrials(22:91)),'bx');
hold on
plot(downTrials(22:91)-exTrials(1)+1,MT(downTrials(22:91)),'ro');
hold on
plot(downTrials(22:91)-exTrials(1)+1,MT_c(downTrials(22:91)),'rx');
axis([0 140 0 6]); set(gca,'LineWidth',2,'XTick',[1 20 70 120 140],'YTick',[],'YTickLabel',[],'FontName','Arial','FontSize',10); title(['exposure'], 'fontsize',11); xlabel('Trials','fontsize',11);
hold on
subplot('Position',[0.75 0.2 0.24 0.6]); hold on;
plot(upTrials(92:111)-peTrials(1)+1,MT(upTrials(92:111)),'bo');
hold on
plot(upTrials(92:111)-peTrials(1)+1,MT_c(upTrials(92:111)),'bx');
hold on
plot(downTrials(92:111)-peTrials(1)+1,MT(downTrials(92:111)),'ro');
hold on
plot(downTrials(92:111)-peTrials(1)+1,MT_c(downTrials(92:111)),'rx');
axis([0 40 0 6]); set(gca,'LineWidth',2,'XTick',[1 20 40],'YTick',[],'YTickLabel',[],'FontName','Arial','FontSize',10); title(['post-exposure'], 'fontsize',11); xlabel('Trials','fontsize',11);
title([subID(7:9), ' ', 'MT'])
%% IDE
%%%%%%%%%%%%%%%%%%%%%%% Initial Directional Error %%%%%%%%%%%%%%%%%%%%%%%%
% Defined as the angle between the vector from hand position at movement
% onset to target position and a vector pointing to the hand
% position at peak velocity from movement onset hand position
upTargetPos = [(-1)*sortData(1,1).TARGET_TABLE.X(3) sortData(1,1).TARGET_TABLE.Y(3)];
downTargetPos = [(-1)*sortData(1,1).TARGET_TABLE.X(4) sortData(1,1).TARGET_TABLE.Y(4)];
xPeak = zeros(numTrials,1);
yPeak = zeros(numTrials,1);
xStart = zeros(numTrials,1);
yStart = zeros(numTrials,1);
imd = zeros(numTrials,2); % initial movement direction (x,y)
itd = zeros(numTrials,2); % initial target direction (x,y)
ide = zeros(numTrials,1);
for i = 1:numTrials
if wrong_trial(i) == 0 && channel_trial(i) == 0
% Hand Position at movement onset
xStart(i) = cursorPosX{i,1}(onset(i))*100-Tx; %in cm and workspace ref frame
yStart(i) = cursorPosY{i,1}(onset(i))*100-Ty;
% Hand Position at peak velocity
xPeak(i) = cursorPosX{i,1}(indPeak(i))*100-Tx; %in cm and workspace ref frame
yPeak(i) = cursorPosY{i,1}(indPeak(i))*100-Ty;
% Vector from start position to peak velocity position
imd(i,:) = [xPeak(i) - xStart(i) yPeak(i) - yStart(i)];
if yPeak(i) > 0
itd(i,:) = [upTargetPos(1) - xStart(i) upTargetPos(2) - yStart(i)];
elseif yPeak(i) < 0
itd(i,:) = [downTargetPos(1) - xStart(i) downTargetPos(2) - yStart(i)];
end
ide(i) = acosd(dot(itd(i,:),imd(i,:))./(norm(itd(i,:)).*norm(imd(i,:))));
% Make ide the the 1st and 3rd quad negative
if imd(i,1) > 0 && imd(i,2) > 0
ide(i) = -ide(i);
elseif imd(i,1) < 0 && imd(i,2) < 0
ide(i) = -ide(i);
end
else
xPeak(i) = NaN;
yPeak(i) = NaN;
xStart(i) = NaN;
yStart(i) = NaN;
imd(i,:) = NaN;
ide(i) = NaN;
end
end
%ide(exTrials) = ide(exTrials)-40;
%% ide outlier analysis
data = outlier_t(ide(upTrials(1:10)));
ide_c(upTrials(1:10)) = data;
data = outlier_t(ide(downTrials(1:10)));
ide_c(downTrials(1:10)) = data;
clear data;
data = outlier_t(ide(upTrials(11:20)));
ide_c(upTrials(11:20)) = data;
data = outlier_t(ide(downTrials(11:20)));
ide_c(downTrials(11:20)) = data;
clear data;
ide_c(upTrials(21)) = NaN; ide_c(downTrials(21)) = NaN;
% data = outlier_t(ide(upTrials(14:23)));
% ide_c(upTrials(14:23)) = data;
% data = outlier_t(ide(downTrials(14:23)));
% ide_c(downTrials(14:23)) = data;
% clear data;
ide_c(upTrials(22:31)) = ide(upTrials(22:31));
ide_c(downTrials(22:31)) = ide(downTrials(22:31));
data = outlier_t(ide(upTrials(32:91)));
ide_c(upTrials(32:91)) = data;
data = outlier_t(ide(downTrials(32:91)));
ide_c(downTrials(32:91)) = data;
clear data;
data = outlier_t(ide(upTrials(92:96)));
ide_c(upTrials(92:96)) = data;
data = outlier_t(ide(downTrials(92:96)));
ide_c(downTrials(92:96)) = data;
clear data;
data = outlier_t(ide(upTrials(97:111)));
ide_c(upTrials(97:111)) = data;
data = outlier_t(ide(downTrials(97:111)));
ide_c(downTrials(97:111)) = data;
clear data;
% transpose and calculate standardized variable
ide_c = ide_c';
bkup_mean = nanmean(ide_c(upTrials(11:20)));
bkup_std = nanstd(ide_c(upTrials(11:20)));
ide_up_st = (ide_c(upTrials) - bkup_mean)/bkup_std;
bkdown_mean = nanmean(ide_c(downTrials(11:20)));
bkdown_std = nanstd(ide_c(downTrials(11:20)));
ide_down_st = (ide_c(downTrials) - bkdown_mean)/bkdown_std;
clear bkup_mean; clear bkup_std; clear bkdown_mean; clear bkdown_std;
%% Plotting Code for ide
figure
set(gcf,'Color','w','Position',[560 528 600 420])
hold on;
subplot('Position',[0.06 0.2 0.1 0.6]); hold on;
plot(upTrials(1:10),ide(upTrials(1:10)),'bo');
hold on
plot(upTrials(1:10),ide_c(upTrials(1:10)),'bx');
hold on
plot(downTrials(1:10),ide(downTrials(1:10)),'ro');
hold on
plot(downTrials(1:10),ide_c(downTrials(1:10)),'rx');
axis([0 20 -80 80]); set(gca,'LineWidth',2,'XTick',[1 10 20],'YTick',-80:20:80,'YTickLabel',-80:20:80,'FontName','Arial','FontSize',10); ylabel('ide [s]'); title('vis-pre','fontsize',11);
hold on
subplot('Position',[0.19 0.2 0.1 0.6]); hold on;
plot(upTrials(11:20)-kbTrials(1)+1,ide(upTrials(11:20)),'bo');
hold on
plot(upTrials(11:20)-kbTrials(1)+1,ide_c(upTrials(11:20)),'bx');
hold on
plot(downTrials(11:20)-kbTrials(1)+1,ide(downTrials(11:20)),'ro');
hold on
plot(downTrials(11:20)-kbTrials(1)+1,ide_c(downTrials(11:20)),'rx');
axis([0 20 -80 80]); set(gca,'LineWidth',2,'XTick',[1 10 20],'YTick',[],'YTickLabel',[],'FontName','Arial','FontSize',10); title('kin-pre','fontsize',11);
hold on
subplot('Position',[0.32 0.2 0.4 0.6]); hold on;
plot(upTrials(22:91)-exTrials(1)+1,ide(upTrials(22:91)),'bo');
hold on
plot(upTrials(22:91)-exTrials(1)+1,ide_c(upTrials(22:91)),'bx');
hold on
plot(downTrials(22:91)-exTrials(1)+1,ide(downTrials(22:91)),'ro');
hold on
plot(downTrials(22:91)-exTrials(1)+1,ide_c(downTrials(22:91)),'rx');
axis([0 140 -80 80]); set(gca,'LineWidth',2,'XTick',[1 20 70 120 140],'YTick',[],'YTickLabel',[],'FontName','Arial','FontSize',10); title(['exposure'], 'fontsize',11); xlabel('Trials','fontsize',11);
hold on
subplot('Position',[0.75 0.2 0.24 0.6]); hold on;
plot(upTrials(92:111)-peTrials(1)+1,ide(upTrials(92:111)),'bo');
hold on
plot(upTrials(92:111)-peTrials(1)+1,ide_c(upTrials(92:111)),'bx');
hold on
plot(downTrials(92:111)-peTrials(1)+1,ide(downTrials(92:111)),'ro');
hold on
plot(downTrials(92:111)-peTrials(1)+1,ide_c(downTrials(92:111)),'rx');
axis([0 40 -80 80]); set(gca,'LineWidth',2,'XTick',[1 20 40],'YTick',[],'YTickLabel',[],'FontName','Arial','FontSize',10); title(['post-exposure'], 'fontsize',11); xlabel('Trials','fontsize',11);
title([subID(7:9), ' ', 'ide'])
%% EDE
%%%%%%%%%%%%%%%%%%%%%%% Endpoint Directional Error %%%%%%%%%%%%%%%%%%%%%%%%
% Defined as the angle between the vector from hand position at movement
% onset to target position and a vector pointing to the hand
% position at movement endpoint from movement onset hand position
upTargetPos = [(-1)*sortData(1,1).TARGET_TABLE.X(3) sortData(1,1).TARGET_TABLE.Y(3)];
downTargetPos = [(-1)*sortData(1,1).TARGET_TABLE.X(4) sortData(1,1).TARGET_TABLE.Y(4)];
xOff = zeros(numTrials,1);
yOff = zeros(numTrials,1);
xStart = zeros(numTrials,1);
yStart = zeros(numTrials,1);
imd = zeros(numTrials,2); % initial movement direction (x,y)
itd = zeros(numTrials,2); % initial target direction (x,y)
ede = zeros(numTrials,1);
for i = 1:numTrials
if wrong_trial(i) == 0 && channel_trial(i) == 0
% Hand Position at movement onset
xStart(i) = cursorPosX{i,1}(onset(i))*100-Tx; %in cm and workspace ref frame
yStart(i) = cursorPosY{i,1}(onset(i))*100-Ty;
% Hand Position at peak velocity
xOff(i) = cursorPosX{i,1}(offset(i))*100-Tx; %in cm and workspace ref frame
yOff(i) = cursorPosY{i,1}(offset(i))*100-Ty;
% Vector from start position to peak velocity position
imd(i,:) = [xOff(i) - xStart(i) yOff(i) - yStart(i)];
if yOff(i) > 0
itd(i,:) = [upTargetPos(1) - xStart(i) upTargetPos(2) - yStart(i)];
elseif yOff(i) < 0
itd(i,:) = [downTargetPos(1) - xStart(i) downTargetPos(2) - yStart(i)];
end
ede(i) = acosd(dot(itd(i,:),imd(i,:))./(norm(itd(i,:)).*norm(imd(i,:))));
% Make ede the the 1st and 3rd quad negative
if imd(i,1) > 0 && imd(i,2) > 0
ede(i) = -ede(i);
elseif imd(i,1) < 0 && imd(i,2) < 0
ede(i) = -ede(i);
end
else
xOff(i) = NaN;
yOff(i) = NaN;
xStart(i) = NaN;
yStart(i) = NaN;
imd(i,:) = NaN;
ede(i) = NaN;
end
end
%ede(exTrials) = ede(exTrials)-40;
%% ede outlier analysis
data = outlier_t(ede(upTrials(1:10)));
ede_c(upTrials(1:10)) = data;
data = outlier_t(ede(downTrials(1:10)));
ede_c(downTrials(1:10)) = data;
clear data;
data = outlier_t(ede(upTrials(11:20)));
ede_c(upTrials(11:20)) = data;
data = outlier_t(ede(downTrials(11:20)));
ede_c(downTrials(11:20)) = data;
clear data;
ede_c(upTrials(21)) = NaN; ede_c(downTrials(21)) = NaN;
% data = outlier_t(ede(upTrials(14:23)));
% ede_c(upTrials(14:23)) = data;
% data = outlier_t(ede(downTrials(14:23)));
% ede_c(downTrials(14:23)) = data;
% clear data;
ede_c(upTrials(22:31)) = ede(upTrials(22:31));
ede_c(downTrials(22:31)) = ede(downTrials(22:31));
data = outlier_t(ede(upTrials(32:91)));
ede_c(upTrials(32:91)) = data;
data = outlier_t(ede(downTrials(32:91)));
ede_c(downTrials(32:91)) = data;
clear data;
data = outlier_t(ede(upTrials(92:96)));
ede_c(upTrials(92:96)) = data;
data = outlier_t(ede(downTrials(92:96)));
ede_c(downTrials(92:96)) = data;
clear data;
data = outlier_t(ede(upTrials(97:111)));
ede_c(upTrials(97:111)) = data;
data = outlier_t(ede(downTrials(97:111)));
ede_c(downTrials(97:111)) = data;
clear data;
% transpose and calculate standardized variable
ede_c = ede_c';
bkup_mean = nanmean(ede_c(upTrials(11:20)));
bkup_std = nanstd(ede_c(upTrials(11:20)));
ede_up_st = (ede_c(upTrials) - bkup_mean)/bkup_std;
bkdown_mean = nanmean(ede_c(downTrials(11:20)));
bkdown_std = nanstd(ede_c(downTrials(11:20)));
ede_down_st = (ede_c(downTrials) - bkdown_mean)/bkdown_std;
clear bkup_mean; clear bkup_std; clear bkdown_mean; clear bkdown_std;
%% Plotting Code for ede
figure
set(gcf,'Color','w','Position',[560 528 600 420])
hold on;
subplot('Position',[0.06 0.2 0.1 0.6]); hold on;
plot(upTrials(1:10),ede(upTrials(1:10)),'bo');
hold on
plot(upTrials(1:10),ede_c(upTrials(1:10)),'bx');
hold on
plot(downTrials(1:10),ede(downTrials(1:10)),'ro');
hold on
plot(downTrials(1:10),ede_c(downTrials(1:10)),'rx');
axis([0 20 -80 80]); set(gca,'LineWidth',2,'XTick',[1 10 20],'YTick',-80:20:80,'YTickLabel',-80:20:80,'FontName','Arial','FontSize',10); ylabel('ede [s]'); title('vis-pre','fontsize',11);
hold on
subplot('Position',[0.19 0.2 0.1 0.6]); hold on;
plot(upTrials(11:20)-kbTrials(1)+1,ede(upTrials(11:20)),'bo');
hold on
plot(upTrials(11:20)-kbTrials(1)+1,ede_c(upTrials(11:20)),'bx');
hold on
plot(downTrials(11:20)-kbTrials(1)+1,ede(downTrials(11:20)),'ro');
hold on
plot(downTrials(11:20)-kbTrials(1)+1,ede_c(downTrials(11:20)),'rx');
axis([0 20 -80 80]); set(gca,'LineWidth',2,'XTick',[1 10 20],'YTick',[],'YTickLabel',[],'FontName','Arial','FontSize',10); title('kin-pre','fontsize',11);
hold on
subplot('Position',[0.32 0.2 0.4 0.6]); hold on;
plot(upTrials(22:91)-exTrials(1)+1,ede(upTrials(22:91)),'bo');
hold on
plot(upTrials(22:91)-exTrials(1)+1,ede_c(upTrials(22:91)),'bx');
hold on
plot(downTrials(22:91)-exTrials(1)+1,ede(downTrials(22:91)),'ro');
hold on
plot(downTrials(22:91)-exTrials(1)+1,ede_c(downTrials(22:91)),'rx');
axis([0 140 -80 80]); set(gca,'LineWidth',2,'XTick',[1 20 70 120 140],'YTick',[],'YTickLabel',[],'FontName','Arial','FontSize',10); title(['exposure'], 'fontsize',11); xlabel('Trials','fontsize',11);
hold on
subplot('Position',[0.75 0.2 0.24 0.6]); hold on;
plot(upTrials(92:111)-peTrials(1)+1,ede(upTrials(92:111)),'bo');
hold on
plot(upTrials(92:111)-peTrials(1)+1,ede_c(upTrials(92:111)),'bx');
hold on
plot(downTrials(92:111)-peTrials(1)+1,ede(downTrials(92:111)),'ro');
hold on
plot(downTrials(92:111)-peTrials(1)+1,ede_c(downTrials(92:111)),'rx');
axis([0 40 -80 80]); set(gca,'LineWidth',2,'XTick',[1 20 40],'YTick',[],'YTickLabel',[],'FontName','Arial','FontSize',10); title(['post-exposure'], 'fontsize',11); xlabel('Trials','fontsize',11);
title([subID(7:9), ' ', 'ede'])
%% RMSE
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% RMSE %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% Taken straight from kinsym2 step 2 files
rmse=zeros(numTrials,1); % allocate space for rmse
mov_int = zeros(numTrials,1);
for i=1:numTrials
if (wrong_trial(i)==0 && channel_trial(i)==0)
xx=cursorPosX{i,1}(onset(i):offset(i))*1000; % convert to mm
yy=cursorPosY{i,1}(onset(i):offset(i))*1000;
% spatial resampling of movement path
N= 2000; N1= length(xx); % Computes equally-spaced vector assuming 1000 samples
xc= 1/(N-1)*(0:N-1)*(xx(N1)-xx(1))+xx(1);
yc= 1/(N-1)*(0:N-1)*(yy(N1)-yy(1))+yy(1);
% integrates the movement length
mov_int(i)=sum(sqrt(diff(xx).^2+ diff(yy).^2));
di=(0:N-1)*mov_int(i)/(N-1);
d=[0; (cumsum(sqrt((diff(xx).^2)+ (diff(yy).^2))))];
% interpolates the movement path to make it equally spaced
x2i= interp1q(d,xx,di');
y2i= interp1q(d,yy,di');
x2i(N)=xc(N);
y2i(N)=yc(N);
optimal =[xc', yc'];
resampled_path =[x2i, y2i];
rmse(i) = sqrt(sum(sum((resampled_path - optimal).^2))/N);
else rmse(i)=NaN;
end
end
%% rmse outlier analysis
data = outlier_t(rmse(upTrials(1:10))); % Outlier for visual baseline
rmse_c(upTrials(1:10)) = data;
data = outlier_t(rmse(downTrials(1:10)));
rmse_c(downTrials(1:10)) = data;
clear data;
data = outlier_t(rmse(upTrials(11:20))); % Outlier for kin baseline
rmse_c(upTrials(11:20)) = data;
data = outlier_t(rmse(downTrials(11:20)));
rmse_c(downTrials(11:20)) = data;
clear data;
rmse_c(upTrials(21)) = NaN; rmse_c(downTrials(21)) = NaN; % Toss out catch trials
% data = outlier_t(rmse(upTrials(14:23))); % Outlier for first 20 exposure
% rmse_c(upTrials(14:23)) = data;
% data = outlier_t(rmse(downTrials(14:23)));
% rmse_c(downTrials(14:23)) = data;
% clear data;
rmse_c(upTrials(22:31)) = rmse(upTrials(22:31));
rmse_c(downTrials(22:31)) = rmse(downTrials(22:31));
data = outlier_t(rmse(upTrials(32:91))); % Outlier for last 120 exposure
rmse_c(upTrials(32:91)) = data;
data = outlier_t(rmse(downTrials(32:91)));
rmse_c(downTrials(32:91)) = data;
clear data;
data = outlier_t(rmse(upTrials(92:96))); % Outlier for first 10 post-exp
rmse_c(upTrials(92:96)) = data;
data = outlier_t(rmse(downTrials(92:96)));
rmse_c(downTrials(92:96)) = data;
clear data;
data = outlier_t(rmse(upTrials(97:111))); % Outlier for last 30 post-exp
rmse_c(upTrials(97:111)) = data;
data = outlier_t(rmse(downTrials(97:111)));
rmse_c(downTrials(97:111)) = data;
clear data;
% transpose and calculate standardized variable
rmse_c = rmse_c';
bkup_mean = nanmean(rmse_c(upTrials(11:20)));
bkup_std = nanstd(rmse_c(upTrials(11:20)));
rmse_up_st = (rmse_c(upTrials) - bkup_mean)/bkup_std;
bkdown_mean = nanmean(rmse_c(downTrials(11:20)));
bkdown_std = nanstd(rmse_c(downTrials(11:20)));
rmse_down_st = (rmse_c(downTrials) - bkdown_mean)/bkdown_std;
clear bkup_mean; clear bkup_std; clear bkdown_mean; clear bkdown_std;
%% Plotting Code for rmse
figure
set(gcf,'Color','w','Position',[560 528 600 420])
hold on;
subplot('Position',[0.06 0.2 0.1 0.6]); hold on;
plot(upTrials(1:10),rmse(upTrials(1:10)),'bo');
hold on
plot(upTrials(1:10),rmse_c(upTrials(1:10)),'bx');
hold on
plot(downTrials(1:10),rmse(downTrials(1:10)),'ro');
hold on
plot(downTrials(1:10),rmse_c(downTrials(1:10)),'rx');
axis([0 20 0 60]); set(gca,'LineWidth',2,'XTick',[1 10 20],'YTick',0:10:60,'YTickLabel', 0:10:60,'FontName','Arial','FontSize',10); ylabel('rmse [s]'); title('vis-pre','fontsize',11);
hold on
subplot('Position',[0.19 0.2 0.1 0.6]); hold on;
plot(upTrials(11:20)-kbTrials(1)+1,rmse(upTrials(11:20)),'bo');
hold on
plot(upTrials(11:20)-kbTrials(1)+1,rmse_c(upTrials(11:20)),'bx');
hold on
plot(downTrials(11:20)-kbTrials(1)+1,rmse(downTrials(11:20)),'ro');
hold on
plot(downTrials(11:20)-kbTrials(1)+1,rmse_c(downTrials(11:20)),'rx');
axis([0 20 0 60]); set(gca,'LineWidth',2,'XTick',[1 10 20],'YTick',[],'YTickLabel',[],'FontName','Arial','FontSize',10); title('kin-pre','fontsize',11);
hold on
subplot('Position',[0.32 0.2 0.4 0.6]); hold on;
plot(upTrials(22:91)-exTrials(1)+1,rmse(upTrials(22:91)),'bo');
hold on
plot(upTrials(22:91)-exTrials(1)+1,rmse_c(upTrials(22:91)),'bx');
hold on
plot(downTrials(22:91)-exTrials(1)+1,rmse(downTrials(22:91)),'ro');
hold on
plot(downTrials(22:91)-exTrials(1)+1,rmse_c(downTrials(22:91)),'rx');
axis([0 140 0 60]); set(gca,'LineWidth',2,'XTick',[1 20 70 120 140],'YTick',[],'YTickLabel',[],'FontName','Arial','FontSize',10); title(['exposure'], 'fontsize',11); xlabel('Trials','fontsize',11);
hold on
subplot('Position',[0.75 0.2 0.24 0.6]); hold on;
plot(upTrials(92:111)-peTrials(1)+1,rmse(upTrials(92:111)),'bo');
hold on
plot(upTrials(92:111)-peTrials(1)+1,rmse_c(upTrials(92:111)),'bx');
hold on
plot(downTrials(92:111)-peTrials(1)+1,rmse(downTrials(92:111)),'ro');
hold on
plot(downTrials(92:111)-peTrials(1)+1,rmse_c(downTrials(92:111)),'rx');
axis([0 40 0 60]); set(gca,'LineWidth',2,'XTick',[1 20 40],'YTick',[],'YTickLabel',[],'FontName','Arial','FontSize',10); title(['post-exposure'], 'fontsize',11); xlabel('Trials','fontsize',11);
title([subID(7:9), ' ', 'rmse'])
%% EPE, EP_X, and EP_Y calcs
%%%%%%%%%%%%%%%%%%%%%%%% End-Point Error (EPE)%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
% and EP_X and EP_Y
EPE = zeros(numTrials,1);
EP_X = zeros(numTrials,1);
EP_Y = zeros(numTrials,1);
for i = 1:numTrials
if wrong_trial(i) == 0 && channel_trial(i) == 0
EP_X(i) = (cursorPosX{i,1}(offset(i))*100 - Tx) - (-1)*sortData(i).TARGET_TABLE.X(3);
if upBool(i) == 1
EP_Y(i) = (cursorPosY{i,1}(offset(i))*100 - Ty) - sortData(i).TARGET_TABLE.Y(3);
else
EP_Y(i) = (cursorPosY{i,1}(offset(i))*100 - Ty) - sortData(i).TARGET_TABLE.Y(4);
end
else
EPE(i) = NaN;
EP_X(i) = NaN;
EP_Y(i) = NaN;
end
end
EPE = sqrt(EP_X.^2 + EP_Y.^2);
%% EPE outlier analysis
data = outlier_t(EPE(upTrials(1:10))); % Outlier for visual baseline
EPE_c(upTrials(1:10)) = data;
data = outlier_t(EPE(downTrials(1:10)));
EPE_c(downTrials(1:10)) = data;
clear data;
data = outlier_t(EPE(upTrials(11:20))); % Outlier for kin baseline
EPE_c(upTrials(11:20)) = data;
data = outlier_t(EPE(downTrials(11:20)));
EPE_c(downTrials(11:20)) = data;
clear data;
EPE_c(upTrials(21)) = NaN; EPE_c(downTrials(21)) = NaN; % Toss out catch trials
% data = outlier_t(EPE(upTrials(14:23))); % Outlier for first 20 exposure
% EPE_c(upTrials(14:23)) = data;
% data = outlier_t(EPE(downTrials(14:23)));
% EPE_c(downTrials(14:23)) = data;
% clear data;
EPE_c(upTrials(22:31)) = EPE(upTrials(22:31));
EPE_c(downTrials(22:31)) = EPE(downTrials(22:31));
data = outlier_t(EPE(upTrials(32:91))); % Outlier for last 100 exposure
EPE_c(upTrials(32:91)) = data;
data = outlier_t(EPE(downTrials(32:91)));
EPE_c(downTrials(32:91)) = data;
clear data;
data = outlier_t(EPE(upTrials(92:96))); % Outlier for first 10 post-exp
EPE_c(upTrials(92:96)) = data;
data = outlier_t(EPE(downTrials(92:96)));
EPE_c(downTrials(92:96)) = data;
clear data;
data = outlier_t(EPE(upTrials(97:111))); % Outlier for last 10 post-exp
EPE_c(upTrials(97:111)) = data;
data = outlier_t(EPE(downTrials(97:111)));
EPE_c(downTrials(97:111)) = data;
clear data;
% transpose and calculate standardized variable
EPE_c = EPE_c';
bkup_mean = nanmean(EPE_c(upTrials(11:20)));
bkup_std = nanstd(EPE_c(upTrials(11:20)));
EPE_up_st = (EPE_c(upTrials) - bkup_mean)/bkup_std;
bkdown_mean = nanmean(EPE_c(downTrials(11:20)));
bkdown_std = nanstd(EPE_c(downTrials(11:20)));
EPE_down_st = (EPE_c(downTrials) - bkdown_mean)/bkdown_std;
clear bkup_mean; clear bkup_std; clear bkdown_mean; clear bkdown_std;
%% Plotting Code for EPE
figure
set(gcf,'Color','w','Position',[560 528 600 420])
hold on;
subplot('Position',[0.06 0.2 0.1 0.6]); hold on;
plot(upTrials(1:10),EPE(upTrials(1:10)),'bo');
hold on
plot(upTrials(1:10),EPE_c(upTrials(1:10)),'bx');
hold on
plot(downTrials(1:10),EPE(downTrials(1:10)),'ro');
hold on
plot(downTrials(1:10),EPE_c(downTrials(1:10)),'rx');
axis([0 20 0 20]); set(gca,'LineWidth',2,'XTick',[1 10 20],'YTick',0:4:20,'YTickLabel', 0:4:20,'FontName','Arial','FontSize',10); ylabel('EPE [s]'); title('vis-pre','fontsize',11);
hold on
subplot('Position',[0.19 0.2 0.1 0.6]); hold on;
plot(upTrials(11:20)-kbTrials(1)+1,EPE(upTrials(11:20)),'bo');
hold on
plot(upTrials(11:20)-kbTrials(1)+1,EPE_c(upTrials(11:20)),'bx');
hold on
plot(downTrials(11:20)-kbTrials(1)+1,EPE(downTrials(11:20)),'ro');
hold on
plot(downTrials(11:20)-kbTrials(1)+1,EPE_c(downTrials(11:20)),'rx');
axis([0 20 0 20]); set(gca,'LineWidth',2,'XTick',[1 10 20],'YTick',[],'YTickLabel',[],'FontName','Arial','FontSize',10); title('kin-pre','fontsize',11);
hold on
subplot('Position',[0.32 0.2 0.4 0.6]); hold on;
plot(upTrials(22:91)-exTrials(1)+1,EPE(upTrials(22:91)),'bo');
hold on
plot(upTrials(22:91)-exTrials(1)+1,EPE_c(upTrials(22:91)),'bx');
hold on
plot(downTrials(22:91)-exTrials(1)+1,EPE(downTrials(22:91)),'ro');
hold on
plot(downTrials(22:91)-exTrials(1)+1,EPE_c(downTrials(22:91)),'rx');
axis([0 140 0 20]); set(gca,'LineWidth',2,'XTick',[1 20 70 120 140],'YTick',[],'YTickLabel',[],'FontName','Arial','FontSize',10); title(['exposure'], 'fontsize',11); xlabel('Trials','fontsize',11);
hold on
subplot('Position',[0.75 0.2 0.24 0.6]); hold on;
plot(upTrials(92:111)-peTrials(1)+1,EPE(upTrials(92:111)),'bo');
hold on
plot(upTrials(92:111)-peTrials(1)+1,EPE_c(upTrials(92:111)),'bx');
hold on
plot(downTrials(92:111)-peTrials(1)+1,EPE(downTrials(92:111)),'ro');
hold on
plot(downTrials(92:111)-peTrials(1)+1,EPE_c(downTrials(92:111)),'rx');
axis([0 40 0 20]); set(gca,'LineWidth',2,'XTick',[1 20 40],'YTick',[],'YTickLabel',[],'FontName','Arial','FontSize',10); title(['post-exposure'], 'fontsize',11); xlabel('Trials','fontsize',11);
title([subID(7:9), ' ', 'EPE'])
%% EP_X outlier analysis
data = outlier_t(EP_X(upTrials(1:10))); % Outlier for visual baseline
EP_X_c(upTrials(1:10)) = data;
data = outlier_t(EP_X(downTrials(1:10)));
EP_X_c(downTrials(1:10)) = data;
clear data;
data = outlier_t(EP_X(upTrials(11:20))); % Outlier for kin baseline
EP_X_c(upTrials(11:20)) = data;
data = outlier_t(EP_X(downTrials(11:20)));
EP_X_c(downTrials(11:20)) = data;
clear data;
EP_X_c(upTrials(21)) = NaN; EP_X_c(downTrials(21)) = NaN; % Toss out catch trials
% data = outlier_t(EP_X(upTrials(14:23))); % Outlier for first 20 exposure
% EP_X_c(upTrials(14:23)) = data;
% data = outlier_t(EP_X(downTrials(14:23)));
% EP_X_c(downTrials(14:23)) = data;
% clear data;
EP_X_c(upTrials(22:31)) = EP_X(upTrials(22:31));
EP_X_c(downTrials(22:31)) = EP_X(downTrials(22:31));
data = outlier_t(EP_X(upTrials(32:91))); % Outlier for last 100 exposure
EP_X_c(upTrials(32:91)) = data;
data = outlier_t(EP_X(downTrials(32:91)));
EP_X_c(downTrials(32:91)) = data;
clear data;
data = outlier_t(EP_X(upTrials(92:96))); % Outlier for first 10 post-exp
EP_X_c(upTrials(92:96)) = data;
data = outlier_t(EP_X(downTrials(92:96)));
EP_X_c(downTrials(92:96)) = data;
clear data;
data = outlier_t(EP_X(upTrials(97:111))); % Outlier for last 10 post-exp
EP_X_c(upTrials(97:111)) = data;
data = outlier_t(EP_X(downTrials(97:111)));
EP_X_c(downTrials(97:111)) = data;
clear data;
% transpose and calculate standardized variable
EP_X_c = EP_X_c';
bkup_mean = nanmean(EP_X_c(upTrials(11:20)));
bkup_std = nanstd(EP_X_c(upTrials(11:20)));
EP_X_up_st = (EP_X_c(upTrials) - bkup_mean)/bkup_std;
bkdown_mean = nanmean(EP_X_c(downTrials(11:20)));
bkdown_std = nanstd(EP_X_c(downTrials(11:20)));
EP_X_down_st = (EP_X_c(downTrials) - bkdown_mean)/bkdown_std;
clear bkup_mean; clear bkup_std; clear bkdown_mean; clear bkdown_std;
%% Plotting Code for EP_X
figure
set(gcf,'Color','w','Position',[560 528 600 420])
hold on;
subplot('Position',[0.06 0.2 0.1 0.6]); hold on;
plot(upTrials(1:10),EP_X(upTrials(1:10)),'bo');
hold on
plot(upTrials(1:10),EP_X_c(upTrials(1:10)),'bx');
hold on
plot(downTrials(1:10),EP_X(downTrials(1:10)),'ro');
hold on
plot(downTrials(1:10),EP_X_c(downTrials(1:10)),'rx');
axis([0 20 -20 20]); set(gca,'LineWidth',2,'XTick',[1 10 20],'YTick',-20:4:20,'YTickLabel', -20:4:20,'FontName','Arial','FontSize',10); ylabel('EP_X [s]'); title('vis-pre','fontsize',11);
hold on
subplot('Position',[0.19 0.2 0.1 0.6]); hold on;
plot(upTrials(11:20)-kbTrials(1)+1,EP_X(upTrials(11:20)),'bo');
hold on
plot(upTrials(11:20)-kbTrials(1)+1,EP_X_c(upTrials(11:20)),'bx');
hold on
plot(downTrials(11:20)-kbTrials(1)+1,EP_X(downTrials(11:20)),'ro');
hold on
plot(downTrials(11:20)-kbTrials(1)+1,EP_X_c(downTrials(11:20)),'rx');
axis([0 20 -20 20]); set(gca,'LineWidth',2,'XTick',[1 10 20],'YTick',[],'YTickLabel',[],'FontName','Arial','FontSize',10); title('kin-pre','fontsize',11);
hold on
subplot('Position',[0.32 0.2 0.4 0.6]); hold on;
plot(upTrials(22:91)-exTrials(1)+1,EP_X(upTrials(22:91)),'bo');
hold on
plot(upTrials(22:91)-exTrials(1)+1,EP_X_c(upTrials(22:91)),'bx');
hold on
plot(downTrials(22:91)-exTrials(1)+1,EP_X(downTrials(22:91)),'ro');
hold on
plot(downTrials(22:91)-exTrials(1)+1,EP_X_c(downTrials(22:91)),'rx');
axis([0 140 -20 20]); set(gca,'LineWidth',2,'XTick',[1 20 70 120 140],'YTick',[],'YTickLabel',[],'FontName','Arial','FontSize',10); title(['exposure'], 'fontsize',11); xlabel('Trials','fontsize',11);
hold on
subplot('Position',[0.75 0.2 0.24 0.6]); hold on;
plot(upTrials(92:111)-peTrials(1)+1,EP_X(upTrials(92:111)),'bo');
hold on
plot(upTrials(92:111)-peTrials(1)+1,EP_X_c(upTrials(92:111)),'bx');
hold on
plot(downTrials(92:111)-peTrials(1)+1,EP_X(downTrials(92:111)),'ro');
hold on
plot(downTrials(92:111)-peTrials(1)+1,EP_X_c(downTrials(92:111)),'rx');
axis([0 40 -20 20]); set(gca,'LineWidth',2,'XTick',[1 20 40],'YTick',[],'YTickLabel',[],'FontName','Arial','FontSize',10); title(['post-exposure'], 'fontsize',11); xlabel('Trials','fontsize',11);
title([subID(7:9), ' ', 'EP_X'])
%% EP_Y outlier analysis
data = outlier_t(EP_Y(upTrials(1:10))); % Outlier for visual baseline
EP_Y_c(upTrials(1:10)) = data;
data = outlier_t(EP_Y(downTrials(1:10)));
EP_Y_c(downTrials(1:10)) = data;
clear data;
data = outlier_t(EP_Y(upTrials(11:20))); % Outlier for kin baseline
EP_Y_c(upTrials(11:20)) = data;
data = outlier_t(EP_Y(downTrials(11:20)));
EP_Y_c(downTrials(11:20)) = data;
clear data;
EP_Y_c(upTrials(21)) = NaN; EP_Y_c(downTrials(21)) = NaN; % Toss out catch trials
% data = outlier_t(EP_Y(upTrials(14:23))); % Outlier for first 20 exposure
% EP_Y_c(upTrials(14:23)) = data;
% data = outlier_t(EP_Y(downTrials(14:23)));
% EP_Y_c(downTrials(14:23)) = data;
% clear data;
EP_Y_c(upTrials(22:31)) = EP_Y(upTrials(22:31));
EP_Y_c(downTrials(22:31)) = EP_Y(downTrials(22:31));
data = outlier_t(EP_Y(upTrials(32:91))); % Outlier for last 100 exposure
EP_Y_c(upTrials(32:91)) = data;
data = outlier_t(EP_Y(downTrials(32:91)));
EP_Y_c(downTrials(32:91)) = data;
clear data;
data = outlier_t(EP_Y(upTrials(92:96))); % Outlier for first 10 post-exp
EP_Y_c(upTrials(92:96)) = data;
data = outlier_t(EP_Y(downTrials(92:96)));
EP_Y_c(downTrials(92:96)) = data;
clear data;
data = outlier_t(EP_Y(upTrials(97:111))); % Outlier for last 10 post-exp
EP_Y_c(upTrials(97:111)) = data;
data = outlier_t(EP_Y(downTrials(97:111)));
EP_Y_c(downTrials(97:111)) = data;
clear data;
% transpose and calculate standardized variable
EP_Y_c = EP_Y_c';
bkup_mean = nanmean(EP_Y_c(upTrials(11:20)));
bkup_std = nanstd(EP_Y_c(upTrials(11:20)));
EP_Y_up_st = (EP_Y_c(upTrials) - bkup_mean)/bkup_std;
bkdown_mean = nanmean(EP_Y_c(downTrials(11:20)));
bkdown_std = nanstd(EP_Y_c(downTrials(11:20)));
EP_Y_down_st = (EP_Y_c(downTrials) - bkdown_mean)/bkdown_std;
clear bkup_mean; clear bkup_std; clear bkdown_mean; clear bkdown_std;
%% Plotting Code for EP_Y
figure
set(gcf,'Color','w','Position',[560 528 600 420])
hold on;
subplot('Position',[0.06 0.2 0.1 0.6]); hold on;
plot(upTrials(1:10),EP_Y(upTrials(1:10)),'bo');
hold on
plot(upTrials(1:10),EP_Y_c(upTrials(1:10)),'bx');
hold on
plot(downTrials(1:10),EP_Y(downTrials(1:10)),'ro');
hold on
plot(downTrials(1:10),EP_Y_c(downTrials(1:10)),'rx');
axis([0 20 -20 20]); set(gca,'LineWidth',2,'XTick',[1 10 20],'YTick',-20:4:20,'YTickLabel', -20:4:20,'FontName','Arial','FontSize',10); ylabel('EP_Y [s]'); title('vis-pre','fontsize',11);
hold on
subplot('Position',[0.19 0.2 0.1 0.6]); hold on;
plot(upTrials(11:20)-kbTrials(1)+1,EP_Y(upTrials(11:20)),'bo');
hold on
plot(upTrials(11:20)-kbTrials(1)+1,EP_Y_c(upTrials(11:20)),'bx');
hold on
plot(downTrials(11:20)-kbTrials(1)+1,EP_Y(downTrials(11:20)),'ro');
hold on
plot(downTrials(11:20)-kbTrials(1)+1,EP_Y_c(downTrials(11:20)),'rx');
axis([0 20 -20 20]); set(gca,'LineWidth',2,'XTick',[1 10 20],'YTick',[],'YTickLabel',[],'FontName','Arial','FontSize',10); title('kin-pre','fontsize',11);
hold on
subplot('Position',[0.32 0.2 0.4 0.6]); hold on;
plot(upTrials(22:91)-exTrials(1)+1,EP_Y(upTrials(22:91)),'bo');
hold on
plot(upTrials(22:91)-exTrials(1)+1,EP_Y_c(upTrials(22:91)),'bx');
hold on
plot(downTrials(22:91)-exTrials(1)+1,EP_Y(downTrials(22:91)),'ro');
hold on
plot(downTrials(22:91)-exTrials(1)+1,EP_Y_c(downTrials(22:91)),'rx');
axis([0 140 -20 20]); set(gca,'LineWidth',2,'XTick',[1 20 70 120 140],'YTick',[],'YTickLabel',[],'FontName','Arial','FontSize',10); title(['exposure'], 'fontsize',11); xlabel('Trials','fontsize',11);
hold on
subplot('Position',[0.75 0.2 0.24 0.6]); hold on;
plot(upTrials(92:111)-peTrials(1)+1,EP_Y(upTrials(92:111)),'bo');
hold on
plot(upTrials(92:111)-peTrials(1)+1,EP_Y_c(upTrials(92:111)),'bx');
hold on
plot(downTrials(92:111)-peTrials(1)+1,EP_Y(downTrials(92:111)),'ro');
hold on
plot(downTrials(92:111)-peTrials(1)+1,EP_Y_c(downTrials(92:111)),'rx');
axis([0 40 -20 20]); set(gca,'LineWidth',2,'XTick',[1 20 40],'YTick',[],'YTickLabel',[],'FontName','Arial','FontSize',10); title(['post-exposure'], 'fontsize',11); xlabel('Trials','fontsize',11);
title([subID(7:9), ' ', 'EP_Y'])
%% Movement Length
%%%%%%%%%%%%%%%%%%%%%%% Movement Length %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
mov_int = zeros(numTrials,1);
for i = 1:numTrials
if wrong_trial(i) == 0 && channel_trial(i) == 0
mov_int(i) = sum(sqrt(diff(cursorPosX{i,1}(onset(i):offset(i))).^2 + diff(cursorPosY{i,1}(onset(i):offset(i))).^2)) * 100; %movement length in cm
else
mov_int(i) = NaN;
end
end