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fmrib_pas_EEGActivations.m
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fmrib_pas_EEGActivations.m
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% fmrib_pas_EEGActivations() - Remove pulse artifacts from BCG-related selected
% ICs by PROJIC. A choice of methods is available. This program is merely an
% adaptation of the original code fmrib_pas(), as part of the EEGLAB toolbox,
% within the FMRIB plug-in.
%
% Usage:
% >> data = fmrib_pas_EEGActivations(data, chs, QRSevents, method, npc, avg_window, fs, delay)
%
% Inputs:
% data - IC time-courses returned by a given ICA decomposition
% algorithm
% chs - ICs for which the artefact occurrencs will be corrected
% for
% QRSevents - vector containing the R peak annotations
% method: 'obs' for artifact principal components. You need to specify
% 'npc', which is the number of PC to use in fitting the artifacts. If unsure, use 4.
% 'mean' for simple mean averaging.
% 'gmean' for Gaussian weighted mean.
% 'median' for median filter.
% npc - number of principal components (set n_win=[] when using the 'obs' method)
% avg_window - number of windows (set npc=[] when using the 'mean',
% 'gmean' or 'median' correction methods).
% fs - sampling rate
% delay_QRS - time delay between the R peak annotations and the BCG
%
% Outputs:
% data - IC time-courses corrected for the artefact occurrences using the
% specified method
%
% Author: Rami K. Niazy (adapted by Rodolfo Abreu, ISR/IST, Universidade de Lisboa, 2015)
function data = fmrib_pas_EEGActivations(data, chs, QRSevents, method, npc, avg_window, fs, delay)
nargchk(3,4,nargin);
data = data(chs, :);
switch method
case 'obs'
if nargin < 4
error('Incorrect number of input arguments');
end
%init
%-----
[channels samples]=size(data);
pcs=npc-1;
%standard delay between QRS peak and artifact (allen,1998)
% delay=round(0.21*fs);
Gwindow=20;
GHW=floor(Gwindow/2);
rcount=0;
firstplot=1;
% memory allocation
%------------------
avgdata=zeros(Gwindow,round(fs*2));
drift=zeros(1,round(fs*2)*Gwindow);
fitted_art=zeros(channels,samples);
mPA=zeros(1,round(2*fs));
peakplot=zeros(1,samples);
mEye=eye(channels);
%Extract QRS events
%------------------
peakplot(QRSevents)=1;
sh=zeros(1,delay);
np=length(peakplot);
peakplot=[sh peakplot(1:np-delay)];
peakI=find(peakplot>0);
peakcount=length(peakI);
pa=peakcount;
%make filter
%-----------
a=[0 0 1 1];
f=[0 0.4/(fs/2) 0.9/(fs/2) 1];
ord=round(3*fs/0.5);
fwts=firls(ord,f,a);
%Artifact Subtraction
%---------------------
% for the first window/2 points use arthemitic mean for averageing.
% findg mean QRS peak-to-peak (R-to-R) interval
for ch=1:channels
% if ch==1
% %init waitbar
% %------------
% barth=5;
% barth_step=barth;
% Flag25=0;
% Flag50=0;
% Flag75=0;
% fprintf('\nPulse artifact subtraction in progress...Please wait!\n');
% end
RR=diff(peakI);
mRR=median(RR);
sRR=std(RR);
%PArange=round(1.25*(mRR+sRR)/2); % half RR
PArange=round(1.5*mRR/2); % half RR
midP=PArange+1;
% if ch==1
% while ((QRSevents(pa-1)+2*PArange-1) > samples)
% pa=pa-1;
% end
% steps=channels*pa;
% end
if ch==1
while (peakI(pa)+PArange > samples)
pa=pa-1;
end
steps=channels*pa;
peakcount=pa;
end
eegchan=filtfilt(fwts,1,double(data(ch,:)));
pcamat=zeros(pa-1,2*PArange+1);
dpcamat=pcamat;
for p=2:pa
pcamat(p-1,:)=eegchan(peakI(p)-PArange:peakI(p)+PArange);
end
pcamat=detrend(pcamat','constant')';
meaneffect=mean(pcamat);
dpcamat=detrend(pcamat,'constant');
[apc,ascore,asvar]=pca_calc(dpcamat');
papc=[meaneffect' ascore(:,1:pcs)];
try
pad_fit=double(papc)*(double(papc)\...
double(detrend(data(ch,peakI(1)-PArange:...
peakI(1)+PArange)','constant')));
fitted_art(ch,peakI(1)-PArange:peakI(1)+PArange)=...
pad_fit';
catch
end
%-----------------------------------------------------
for p=2:GHW+1
pad_fit=double(papc)*(double(papc)\...
double(detrend(data(ch,peakI(p)-PArange:...
peakI(p)+PArange)','constant')));
fitted_art(ch,peakI(p)-PArange:peakI(p)+PArange)=...
pad_fit';
%update bar
%----------
% percentdone=floor(((ch-1)*pa+p)*100/steps);
% if floor(percentdone)>=barth
% if percentdone>=25 & Flag25==0
% fprintf('25%% ')
% Flag25=1;
% elseif percentdone>=50 & Flag50==0
% fprintf('50%% ')
% Flag50=1;
% elseif percentdone>=75 & Flag75==0
% fprintf('75%% ')
% Flag75=1;
% elseif percentdone==100
% fprintf('100%%\n')
% else
% fprintf('.')
% end
%
% while barth<=percentdone
% barth=barth+barth_step;
% end
% if barth>100
% barth=100;
% end
% end
end
%---------------- Processing of central data ---------------------
%cycle through peak artifacts identified by peakplot
rcount=GHW;
for p=GHW+2:peakcount-GHW-1
PreP=ceil((peakI(p)-peakI(p-1))/2);
PostP=ceil((peakI(p+1)-peakI(p))/2);
if PreP > PArange
PreP=PArange;
end
if PostP > PArange
PostP=PArange;
end
pad_fit=double(papc(midP-PArange:midP+PArange,:))*...
(double(papc(midP-PArange:midP+PArange,:))\...
double(detrend(data(ch,peakI(p)-PArange:...
peakI(p)+PArange)','constant')));
fitted_art(ch,peakI(p)-PreP:peakI(p)+PostP)=...
pad_fit(midP-PreP:midP+PostP)';
%update bar
%----------
% percentdone=floor(((ch-1)*pa+p)*100/steps);
% if floor(percentdone)>=barth
% if percentdone>=25 & Flag25==0
% fprintf('25%% ')
% Flag25=1;
% elseif percentdone>=50 & Flag50==0
% fprintf('50%% ')
% Flag50=1;
% elseif percentdone>=75 & Flag75==0
% fprintf('75%% ')
% Flag75=1;
% elseif percentdone==100
% fprintf('100%%\n')
% else
% fprintf('.')
% end
%
% while barth<=percentdone
% barth=barth+barth_step;
% end
% if barth>100
% barth=100;
% end
% end
end
%---------------- Processing of last GHW peaks------------------
sectionPoints=samples-(peakI(peakcount-GHW)-PreP)+1;
for p=peakcount-GHW:peakcount
try
pad_fit=double(papc(midP-PArange:midP+PArange,:))*...
(double(papc(midP-PArange:midP+PArange,:))\...
double(detrend(data(ch,peakI(p)-PArange:...
peakI(p)+PArange)','constant')));
fitted_art(ch,peakI(p)-PreP:peakI(p)+PostP)=...
pad_fit(midP-PreP:midP+PostP)';
catch
end
%update bar
%----------
% percentdone=floor(((ch-1)*pa+p)*100/steps);
% if floor(percentdone)>=barth
% if percentdone>=25 & Flag25==0
% fprintf('25%% ')
% Flag25=1;
% elseif percentdone>=50 & Flag50==0
% fprintf('50%% ')
% Flag50=1;
% elseif percentdone>=75 & Flag75==0
% fprintf('75%% ')
% Flag75=1;
% elseif percentdone==100
% fprintf('100%%\n')
% else
% fprintf('.')
% end
%
% while barth<=percentdone
% barth=barth+barth_step;
% end
% if barth>100
% barth=100;
% end
% end
end
end
data=data-fitted_art;
otherwise
%init
%-----
[channels samples]=size(data);
%standard delay between QRS peak and artifact (allen,1998)
% delay=round(0.21*fs);
if isempty(avg_window), Gwindow=20; else Gwindow = avg_window; end;
GHW=floor(Gwindow/2);
rcount=0;
firstplot=1;
% memory allocation
%------------------
avgdata=zeros(channels,round(fs*2),Gwindow);
drift=zeros(channels,round(fs*2)*Gwindow);
clean=zeros(channels,samples);
mPA=zeros(channels,round(2*fs));
mMag=zeros(channels,Gwindow);
peakplot=zeros(1,samples);
mEye=eye(channels);
%Extract QRS events
%------------------
peakplot(QRSevents)=1;
sh=zeros(1,delay);
np=length(peakplot);
peakplot=[sh peakplot(1:np-delay)];
peakI=find(peakplot>0);
peakcount=length(peakI);
%Artifact Subtraction
%---------------------
% for the first window/2 points use arthemitic mean for averageing.
% findg mean QRS peak-to-peak (R-to-R) interval
for p=2:GHW+1
RR(p-1)=peakI(p)-peakI(p-1);
end
mRR=mean(RR);
sRR=std(RR);
PArange=round((mRR+sRR)/2); % half RR
t=(0:peakI(GHW+1)+PArange-1)/fs;
% subtraction of low freq trend; could use detrend.m function instead
for n=1:channels
pdrift=polyfit(t,data(n,1:peakI(GHW+1)+PArange),1);
drift(n,1:peakI(GHW+1)+PArange)=polyval(pdrift,t);
end
data(:,1:peakI(GHW+1)+PArange)=...
data(:,1:peakI(GHW+1)+PArange)-...
drift(:,1:peakI(GHW+1)+PArange);
for p=2:GHW+1
avgdata(:,1:2*PArange+1,p-1)=...
data(:,peakI(p)-PArange:peakI(p)+PArange);
end
for chan =1:channels
avgdata(chan,:,:)=detrend(squeeze(avgdata(chan,:,:)),'constant');
end
mPA(:,1:2*PArange+1)=mean(avgdata(:,1:1+2*PArange,1:GHW),3);
if peakI(1) > PArange
alphaV=...
sum(detrend(data(:,peakI(1)-PArange:peakI(1)+PArange)','constant')'...
.*mPA(:,1:2*PArange+1),2)./...
sum(mPA(:,1:2*PArange+1).*mPA(:,1:2*PArange+1),2);
alphaD=diag(alphaV);
else
alphaV=sum(detrend(data(:,1:peakI(1)+PArange)','constant')'...
.*mPA(:,PArange-peakI(1)+2:2*PArange+1),2)./...
sum(mPA(:,PArange-peakI(1)+2:2*PArange+1).*...
mPA(:,PArange-peakI(1)+2:2*PArange+1),2);
alphaD=diag(alphaV);
end
data(:,1:peakI(GHW+1)+PArange)=...
data(:,1:peakI(GHW+1)+PArange)+...
drift(:,1:peakI(GHW+1)+PArange);
%init waitbar
%------------
% barth=5;
% barth_step=barth;
% Flag25=0;
% Flag50=0;
% Flag75=0;
% fprintf('\nPulse artifact subtraction in progress...Please wait!\n');
if peakI(1) > PArange
clean(:,peakI(1)-PArange:peakI(1)+PArange)=...
data(:,peakI(1)-PArange:peakI(1)+PArange)-alphaD*mPA(:,1:2*PArange+1);
startpoints=peakI(1)-PArange-1;
clean(:,1:startpoints)=data(:,1:startpoints);
else
clean(:,1:peakI(1)+PArange)=...
data(:,1:peakI(1)+PArange)-alphaD*mPA(:,PArange-peakI(1)+2:2*PArange+1);
end
%update bar
%----------
% percentdone=floor(1*100/peakcount);
% if floor(percentdone)>=barth
% if percentdone>=25 & Flag25==0
% fprintf('25%% ')
% Flag25=1;
% elseif percentdone>=50 & Flag50==0
% fprintf('50%% ')
% Flag50=1;
% elseif percentdone>=75 & Flag75==0
% fprintf('75%% ')
% Flag75=1;
% elseif percentdone==100
% fprintf('100%%\n')
% else
% fprintf('.')
% end
%
% while barth<=percentdone
% barth=barth+barth_step;
% end
% if barth>100
% barth=100;
% end
% end
%-----------------------------------------------------
for p=2:GHW+1
alphaV=sum(data(:,peakI(p)-PArange:peakI(p)+PArange).*...
mPA(:,1:2*PArange+1),2)./...
sum(mPA(:,1:2*PArange+1).*mPA(:,1:2*PArange+1),2);
alphaD=diag(alphaV);
clean(:,peakI(p)-PArange:peakI(p)+PArange)=...
data(:,peakI(p)-PArange:peakI(p)+PArange)-...
alphaD*mPA(:,1:2*PArange+1);
%update bar
%----------
% percentdone=floor(p*100/(peakcount-1));
% if floor(percentdone)>=barth
% if percentdone>=25 & Flag25==0
% fprintf('25%% ')
% Flag25=1;
% elseif percentdone>=50 & Flag50==0
% fprintf('50%% ')
% Flag50=1;
% elseif percentdone>=75 & Flag75==0
% fprintf('75%% ')
% Flag75=1;
% elseif percentdone==100
% fprintf('100%%\n')
% else
% fprintf('.')
% end
%
% while barth<=percentdone
% barth=barth+barth_step;
% end
% if barth>100
% barth=100;
% end
% end
end
%---------------- Processing of central data ---------------------
%cycle through peak artifacts identified by peakplot
for p=GHW+2:peakcount-GHW-1
PreP=ceil((peakI(p)-peakI(p-1))/2); % interval before peak artifact
PostP=ceil((peakI(p+1)-peakI(p))/2);% interval after peak artifact
sectionPoints=peakI(p+GHW)+PostP-(peakI(p-GHW)-PreP-1);
% subtract drift
t=(0:sectionPoints-1)/fs;
for n=1:channels
pdrift=...
polyfit(t,data(n,peakI(p-GHW)-PreP:peakI(p+GHW)+PostP),1);
drift(n,1:sectionPoints)=polyval(pdrift,t);
end
data(:,peakI(p-GHW)-PreP:peakI(p+GHW)+PostP)=...
data(:,peakI(p-GHW)-PreP:peakI(p+GHW)+PostP)-...
drift(:,1:sectionPoints);
rr=1;
for r=p-GHW:p+GHW
mMag(:,rr)=mean(abs(data(:,peakI(r)-PreP:peakI(r)+PostP)),2);
rr=rr+1;
end
minMag=min(mMag,[],2);
%only count those EEG data with no motion or line artifact by rejecting
%excessively large variations. Use channel 1 as ref (Cz).
oldavgdata=avgdata;
oldrcount=rcount;
rcount=0;
for r=p-GHW:p+GHW
if (mean(abs(data(1,peakI(r)-PreP:peakI(r)+PostP))) ...
< 4*minMag(1) ) ...
& (max(abs(data(1,peakI(r)-PreP:peakI(r)+PostP)))...
< 2* 175)
avgdata(:,1:PreP+PostP+1,rcount+1)=...
data(:,peakI(r)-PreP:peakI(r)+PostP);
rcount=rcount+1;
end
end
for chan =1:channels
avgdata(chan,:,:)=detrend(squeeze(avgdata(chan,:,:)),'constant');
end
% rcount
%if more than 8 good data sections found then do averaging, else use
%previous.
if rcount > 5
switch method
case 'gmean'
if p==peakcount-GHW-1
mPA=mean(avgdata(:,:,1:rcount),3);
elseif rcount==(2*GHW+1)
G=gausswin(rcount);
G=G/sum(G);
for r=1:rcount
avgdata(:,:,r)=avgdata(:,:,r)*G(r);
end
mPA=sum(avgdata(:,:,1:rcount),3);
else
mPA=median(avgdata(:,:,1:rcount),3);
end
case 'mean'
mPA=mean(avgdata(:,:,1:rcount),3);
case 'median'
mPA=median(avgdata(:,:,1:rcount),3);
end
else
switch method
case 'mean'
mPA=mean(oldavgdata(:,:,1:oldrcount),3);
case 'median'
mPA=median(oldavgdata(:,:,1:oldrcount),3);
case 'gmean'
mPA=mean(oldavgdata(:,:,1:oldrcount),3);
end
end
alphaV=sum(detrend(data(:,peakI(p)-PreP:peakI(p)+PostP)','constant')'.*...
mPA(:,1:PreP+PostP+1),2)./...
sum(mPA(:,1:PreP+PostP+1).*mPA(:,1:PreP+PostP+1),2);
alphaD=diag(alphaV);
data(:,peakI(p-GHW)-PreP:peakI(p+GHW)+PostP)=...
data(:,peakI(p-GHW)-PreP:peakI(p+GHW)+PostP)+...
drift(:,1:sectionPoints);
clean(:,peakI(p)-PreP:peakI(p)+PostP)=...
data(:,peakI(p)-PreP:peakI(p)+PostP)-...
alphaD*mPA(:,1:PreP+PostP+1);
%update bar
%----------
% percentdone=floor(p*100/(peakcount-1));
% if floor(percentdone)>=barth
% if percentdone>=25 & Flag25==0
% fprintf('25%% ')
% Flag25=1;
% elseif percentdone>=50 & Flag50==0
% fprintf('50%% ')
% Flag50=1;
% elseif percentdone>=75 & Flag75==0
% fprintf('75%% ')
% Flag75=1;
% elseif percentdone==100
% fprintf('100%%\n')
% else
% fprintf('.')
% end
%
% while barth<=percentdone
% barth=barth+barth_step;
% end
% if barth>100
% barth=100;
% end
% end
end
%---------------- Processing of last GHW peaks------------------
sectionPoints=samples-(peakI(peakcount-GHW)-PreP)+1;
if samples-peakI(peakcount) >= PostP
alphaV=sum(detrend(data(:,peakI(peakcount)-...
PreP:peakI(peakcount)+PostP)','constant')'.*...
mPA(:,1:PreP+PostP+1),2)./...
sum(mPA(:,1:PreP+PostP+1).*mPA(:,1:PreP+PostP+1),2);
alphaD=diag(alphaV);
clean(:,peakI(peakcount)-PreP:peakI(peakcount)+PostP)=...
data(:,peakI(peakcount)-PreP:peakI(peakcount)+PostP)- ...
alphaD*mPA(:,1:PreP+PostP+1);
endpoints=samples-peakI(peakcount)-PostP;
clean(:,samples-endpoints+1:samples)=...
data(:,samples-endpoints+1:samples);
else
alphaV=sum(detrend(data(:,peakI(peakcount)-PreP:samples)','constant')'.*...
mPA(:,1:samples-(peakI(peakcount)-PreP)+1),2)./...
sum(mPA(:,1:samples-(peakI(peakcount)-PreP)+1).*...
mPA(:,1:samples-(peakI(peakcount)-PreP)+1),2);
alphaD=diag(alphaV);
clean(:,peakI(peakcount)-PreP:samples)=...
data(:,peakI(peakcount)-PreP:samples)-...
alphaD*mPA(:,1:samples-(peakI(peakcount)-PreP)+1);
end
for p=peakcount-GHW:peakcount-1
alphaV=sum(detrend(data(:,peakI(p)-PreP:peakI(p)+PostP)','constant')'.*...
mPA(:,1:PreP+PostP+1),2)./...
sum(mPA(:,1:PreP+PostP+1).*mPA(:,1:PreP+PostP+1),2);
alphaD=diag(alphaV);
clean(:,peakI(p)-PreP:peakI(p)+PostP)=...
data(:,peakI(p)-PreP:peakI(p)+PostP)-...
alphaD*mPA(:,1:PreP+PostP+1);
%update bar
%----------
% percentdone=floor(p*100/(peakcount-1));
% if floor(percentdone)>=barth
% if percentdone>=25 & Flag25==0
% fprintf('25%% ')
% Flag25=1;
% elseif percentdone>=50 & Flag50==0
% fprintf('50%% ')
% Flag50=1;
% elseif percentdone>=75 & Flag75==0
% fprintf('75%% ')
% Flag75=1;
% elseif percentdone==100
% fprintf('100%%\n')
% else
% fprintf('.')
% end
%
% while barth<=percentdone
% barth=barth+barth_step;
% end
% if barth>100
% barth=100;
% end
% end
end
data=clean;
end
return;