% Demo11 is a demo for EOG regression analysis 
% 
% References: 
% [1] A. Schlögl, C. Keinrath, D. Zimmermann, R. Scherer, R. Leeb, G. Pfurtscheller.
%   A fully automated correction method of EOG artifacts in EEG recordings.
%   Clin.Neurophys. 2007 Jan;118(1):98-104. Epub 2006 Nov 7 Paper(pdf) 
%   available online: http://dx.doi.org/10.1016/j.clinph.2006.09.003
%   preprint: https://pub.ist.ac.at/~schloegl/publications/schloegl2007eog.pdf
%
% [2] A. Schlögl, A. Ziehe, K.-R. Müller (2009)
%   Automated ocular artifact removal: comparing regression and component-based methods
%   Nature Precedings PrePrint
%   available online: http://precedings.nature.com/documents/3446/version/1

% Copyright (C) 2018 Alois Schloegl <alois.schloegl@gmail.com>

if exist('OCTAVE_VERSION','builtin')
        % pkg load mexbiosig
        pkg load signal 
        pkg load nan 
end
if ~exist('fir1','file') || ~exist('fftfilt','file')
        fprintf(2,'error: signal processing toolbox is missing')
end;        
if ~exist('regress_eog','file') || ~exist('mexSLOAD','file')
        fprintf(2,'error: toolbox "biosig for matlab" is missing, its available from http://biosig.sourceforge.net/download.html')
end;        

% load data     
[data,HDR]=mexSLOAD('/home/as/data/test/cnt/barnwell/PRIME_266.cnt');
Fs=HDR.SampleRate;
[HDR.FILE.Path,HDR.FILE.Name,HDR.FILE.Ext]=fileparts(HDR.FileName);

% identify EEG and EOG channels 
eegchan=1:HDR.NS; 
eogchan=find(strcmp(HDR.Label,'VEOG'));
eegchan(eogchan)=[];

% filter for typical eog range (1-6Hz) in order to reduce other (non-EEG and non-EOG) 
% noise sources and get more accurate correction coefficients
B = fir1(HDR.SampleRate,[1 6]*2/HDR.SampleRate);
x = fftfilt(B,data);

% estimate correction coefficients 
R = regress_eog(x, eegchan, eogchan);

% correct for EOG artifcts
data_corrected = data * R.r0; 


%%%%% Write EOG correction matrix %%%%%
LDR.datatype='REREF_MATRIX';
LDR.RR=full(R.r0);
LDR.Label_In=char(HDR.Label);
LDR.Label_Out=char(HDR.Label);
LDR.FileName=[HDR.FILE.Name,'.ldr'];
LDR = openldr(LDR, 'w');


HDR.FileName=[HDR.FileName,'.eog_corrected.gdf'];
mexSSAVE(HDR,data_corrected);



%%%%% display result %%%%%
figure(1)
subplot(211)
plot([1:size(data,1)]'/HDR.SampleRate, [data(:,eegchan)+500, data_corrected(:,eegchan)-500, data(:,eogchan)/2])
set(gca,'xlim',[0,150])
subplot(212)
plot([1:size(data,1)]'/HDR.SampleRate, [data(:,eegchan)+500, data_corrected(:,eegchan)-500, data(:,eogchan)/2])
set(gca,'xlim',[30,40])

ti=[-.3,.3];
figure(2)
for typ=10:12
	[s,sz]=trigg(data,HDR.EVENT.POS(HDR.EVENT.TYP==typ),ti(1)*Fs,ti(2)*Fs);
	s=reshape(s,sz);
	for k=1:HDR.NS,
		subplot(3,6,k+(typ-10)*6);
		plot([ti(1)*Fs:ti(2)*Fs]'/Fs,mean(s(1,:,:),3))
	end
end

figure(3)
for typ=10:12
	[s,sz]=trigg(data_corrected,HDR.EVENT.POS(HDR.EVENT.TYP==typ),ti(1)*Fs,ti(2)*Fs);
	s=reshape(s,sz);
	for k=1:HDR.NS,
		subplot(3,6,k+(typ-10)*6);
		plot([ti(1)*Fs:ti(2)*Fs]'/Fs,mean(s(1,:,:),3))
	end
end

