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charybdis neuwood
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charybdis neuwood
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ALLEEG=[];EEG=[];alldat=[];allrtvar=[];rts=[];dats=[];allrtvar2=[];alldat2=[];
control={'S 41', 'S 42', 'S 51', 'S 52'}
syn = {'S 71' 'S 80' }
sem = {'S 60' 'S 61'}
nsub = 20
channel = 23
for S = 1:nsub
filename = [num2str((S),'%02i'),'_char_long.set'];
filepath = '/home/jona/Desktop/charybdis/neufilt/'
EEG = pop_loadset('filename',filename,'filepath',filepath);
%EEG = pop_resample( EEG, 100);
EEG = pop_subcomp(EEG,[EEG.clusters.saccade EEG.clusters.blink]);
EEG.icawinv = [];EEG.icasphere = [];EEG.icaweights = [];EEG.icachansind = [];
EEG = pop_reref( EEG, [31 33] );
% EEG = pop_eegfiltnew(EEG, 0.5, 0);
EEG = pop_epoch( EEG, control, [-1 3], 'epochinfo', 'yes');
EEG = pop_rmbase( EEG, []);
dat = squeeze(eeg_getdatact(EEG,'channel',channel));
controlerp=mean(dat,2);
EEG = pop_loadset('filename',filename,'filepath',filepath);
%EEG = pop_resample( EEG, 100);
EEG = pop_subcomp(EEG,[EEG.clusters.saccade EEG.clusters.blink]);
EEG.icawinv = [];EEG.icasphere = [];EEG.icaweights = [];EEG.icachansind = [];
EEG = pop_reref( EEG, [31 33] );
% EEG = pop_eegfiltnew(EEG, 0.5, 0);
EEG = pop_epoch( EEG, syn, [-1 3], 'epochinfo', 'yes');
EEG = pop_rmbase( EEG, []);
dat = squeeze(eeg_getdatact(EEG,'channel',channel));
rtvar = eeg_getepochevent( EEG,{'S196'},[],'latency');
for ii=1:length(dat(1,:))
dat(:,ii)=dat(:,ii)-controlerp;
end
allrtvar = [rtvar,allrtvar];
alldat=[dat,alldat];
EEG = pop_loadset('filename',filename,'filepath',filepath);
%EEG = pop_resample( EEG, 100);
EEG = pop_subcomp(EEG,[EEG.clusters.saccade EEG.clusters.blink]);
EEG.icawinv = [];EEG.icasphere = [];EEG.icaweights = [];EEG.icachansind = [];
EEG = pop_reref( EEG, [31 33] );
% EEG = pop_eegfiltnew(EEG, 0.5, 0);
EEG = pop_epoch( EEG, sem, [-1 3], 'epochinfo', 'yes');
EEG = pop_rmbase( EEG, []);
dat = squeeze(eeg_getdatact(EEG,'channel',channel));
rtvar = eeg_getepochevent( EEG,{'S196'},[],'latency');
for ii=1:length(dat(1,:))
dat(:,ii)=dat(:,ii)-controlerp;
end
allrtvar2 = [rtvar,allrtvar2];
alldat2=[dat,alldat2];
rts{S} = allrtvar;
datas{S} = alldat;
rts2{S} = allrtvar2;
datas2{S} = alldat2;
allrtvar=[];alldat=[];allrtvar2=[];alldat2=[];
end;
pre = 100; % time points before 0
p6onset = 0; % time points from 0 to P6 template onset
p6length = 55; % template length in data points
wrts = rts2; weeg = datas2;
for S = 1:nsub
rtmean(S) = nanmedian(wrts{S});
for X = 1:length(wrts{S})
if 1250 < wrts{S}(X)
wrts{S}(X) = NaN;
end;end;
for X = 1:length(wrts{S})
if 500 > wrts{S}(X)
wrts{S}(X) = NaN;
end;end;
data = weeg{S};rtvar = wrts{S};
[outdata,outvar] = erpimage(data,rtvar, linspace(EEG.xmin*1000, EEG.xmax*1000, EEG.pnts), '', 1, 1, 'filt',[0 6],'NoShow','on');
[q,w,e,r,t,erp] = erpimage(data,rtvar, linspace(EEG.xmin*1000, EEG.xmax*1000, EEG.pnts), '', 1, 1, 'filt',[0 6],'NoShow','on','align',inf,'erp','on');
dasyn{S} = outdata;
vasyn{S} = outvar;
% fliplr for sanity check
% everything that's still there when this line is not commented out is a false positive!
% vasyn{S} = randsample(outvar,length(outvar));
vasyn2{S} = outvar;
erps{S} = erp;
end;
eegdata=[];rtdata=[];
for x = 1:nsub
eegdata=[eegdata,dasyn{x}];
rtdata=[rtdata,vasyn{x}];
end;
% figure;[outdata5,outrtvar5,outtrials5] = erpimage(eegdata, rtdata, linspace(EEG.xmin*1000, EEG.xmax*1000, EEG.pnts), 'PZ ERP sorted by reaction time', 6, 1 ,'erp',1,'avg_type','Gaussian','cbar','on','cbar_title','\muV');
collects=[];means=[];vars=[]; corrvals=[];
for S = 1:nsub;
erp = erps{S};
collect=[];corvall=[];
data = dasyn{S};
% the following step either nulls every data point 300 msec after the response
% or subtracts the mean response-locked ERP at that timepoint
% this might sound problematic, but 1. we still get a strong correlation without this step, 2. it’s used to null the ERP time-locked to the feedback, which in turn is perfectly time-locked to the response!! so I wouldn't feel comfortable without it
for L = 1:length(vasyn{S})
% data((vasyn{S}(L)/10)+pre+30:600,L) = 0;
data((vasyn2{S}(L)/10)+pre+40:end,L) = data((vasyn2{S}(L)/10)+pre+40:end,L)- erp((vasyn2{S}(L)/10)+pre+40:end).';
end;
datarm{S} = data;
% figure;[outdata5,outrtvar5,outtrials5] = erpimage(data, vasyn{S}, linspace(EEG.xmin*1000, EEG.xmax*1000, EEG.pnts), 'PZ ERP sorted by reaction time', 2, 1 ,'erp',1,'avg_type','Gaussian','cbar','on','cbar_title','\muV');
datamean = mean(data(pre+p6onset:pre+p6onset+p6length,:),2);
for N = 1:100
trials=[];
for X = 1:length(vasyn{S})
out = xcorr(data(pre+p6onset:pre+p6onset+p6length,X),datamean);
[Y,I]=sort(out);
thepoint=I(end)-p6length;
trialpoint = data(pre+p6onset+thepoint:pre+p6onset+p6length+thepoint,X);
trials = [trials,trialpoint];
datamean = mean(trials,2);
end;
end;
for X = 1:length(vasyn{S})
out = xcorr(data(pre+p6onset:pre+p6onset+p6length,X),mean(trials,2));
[Y,I]=sort(out);
thepoint=I(end)-p6length;
collect = [collect,thepoint];
end;
means = [means,mean(trials,2)];
vars = [vars,collect];
collects{S} = collect;
end;
vars=[];lats=[];dats=[];
for S = 1:nsub
dat =dasyn{S};
varf = vasyn{S};
lat = (collects{S}+p6onset)*10;
rtmean = nanmedian(vasyn{S});
dats=[dats,dat];
vars=[vars,varf];
lats=[lats,lat];
end;
a = [(lats).'; (lats).'-vars.'];
b = [ones(length(vars),1); ones(length(vars),1)*2];
[p,stats]=vartestn(a, b,'on','robust');
% calculate individual correlation coefficients using the robust corr toolbox
for S = 1:nsub
% sbplot(4,5,S);
x = vasyn{S};
y = (collects{S}+p6onset)*10;
[r,t,h,outid] = skipped_correlation(x,y,0);
rvals(S) = squeeze(r.Pearson);
end;
rvals
z=0.5*log((1+rvals)./(1-rvals));
%calculate CI
[H,P,CI95]=ttest(z,0,1-.95);
%[H,P,CI68]=ttest(z,0,1-.68);
zci =CI95;
zmean = mean(z);
% inverse fisher
r=(exp(2*zci)-1)./(exp(2*zci)+1)
r=(exp(2*zmean)-1)./(exp(2*zmean)+1)
r
% calculate robust linear regression, no constant, give the slope + standard error and plot them
figure;
for S = 1:nsub
x = vasyn{S};
y = (collects{S}+p6onset)*10;
sbplot(4,5,S);
% mdlr = LinearModel.fit(x,y,'linear','RobustOpts','on');
% mdlr = LinearModel.fit(x,y,'linear','RobustOpts','on','intercept',false,'VarNames',{'RT','P6'})
% plotResiduals(mdlr,'probability')
% [estimateo(S),stats] = robustfit(x,y,'ols',[],'off');
[estimatel(S),stats] = robustfit(x,y,'logistic',[],'off');
plot(x, y, 'x'); hold on;
plot(x,estimatel(S)*x,'g','LineWidth',2); hold on;
plot(x,(estimatel(S)*x)+stats.se,'r','LineWidth',1); hold on;
plot(x,(estimatel(S)*x)-stats.se,'r','LineWidth',1); hold on;
se(S) = stats.se;
xlim([0 1500]);ylim([0 1500]);
%% plot slope and its standard error
% hold on; plot(S,estimate(S),'x'); hold on; plot(S,estimate(S)+stats.se,'^'); hold on; plot(S,estimate(S)-stats.se,'v');
end;
estimatel
mean(estimatel)
mean(se(S))
% ylim([0 1]);xlim([0 21]);
% calculate individual correlation coefficients using the robust corr toolbox
for S = 1:nsub
% sbplot(4,5,S);
x = vasyn{S};
y = (collects{S}+p6onset)*10;
[r,t,h,outid] = skipped_correlation(x,y,0);
rvals(S) = squeeze(r.Pearson);
end;
rvals
mean(rvals)
figure;
for S = 1:nsub
sbplot(4,5,S);
[outdata5,outrtvar5,outtrials5] = erpimage(datarm{S}, vasyn{S}, linspace(EEG.xmin*1000, EEG.xmax*1000, EEG.pnts), 'PZ ERP sorted by reaction time', 2, 1 ,'avg_type','Gaussian','cbar','on','cbar_title','\muV','align',inf,'erp','on');
end
figure;
for S = 1:nsub
sbplot(4,5,S);
[outdata5,outrtvar5,outtrials5] = erpimage(dasyn{S}, vasyn{S}, linspace(EEG.xmin*1000, EEG.xmax*1000, EEG.pnts), 'PZ ERP sorted by reaction time', 2, 1 ,'avg_type','Gaussian','cbar','on','cbar_title','\muV','align',inf,'erp','on');
end
figure;
for S = 1:nsub
x = vasyn{S};
y = (collects{S}+p6onset)*10;
sbplot(4,5,S);
% mdlr = LinearModel.fit(x,y,'linear','RobustOpts','on');
mdlr = LinearModel.fit(x,y,'linear','RobustOpts','on','intercept',false,'VarNames',{'RT','P6'})
plotResiduals(mdlr,'probability')
end;
% fisher transform
%