bci3eval.m

matlab数字信号处理工具箱

function [o] = bci3eval(x1,x2,DIM)
% BCI3eval evaluations a BCI-result as suggested in [1,2]. 
%   - It returns the classification error, the signal to noise ratio, 
%   the mutual information, as well as mean, standard error and 
%   standard deviation for both classes. 
%   - time course of these resulting parameters are supported
%   - Missing values can be encoded as NaN.   
% 
%
% [o] = bci3eval(x1, x2 [,DIM])
%
% x1 is the bci output for class 1 
% x2 is the bci output for class 2
% o is a struct with various results  
%
%
% see also: SUMSKIPNAN, PLOTA
%
% REFERENCES: 
% [1] Schl鰃l A., Neuper C. Pfurtscheller G.
%	Estimating the mutual information of an EEG-based Brain-Computer-Interface
%	Biomedizinische Technik 47(1-2): 3-8, 2002.
% [2] A. Schl鰃l, C. Keinrath, R. Scherer, G. Pfurtscheller,
%	Information transfer of an EEG-based Bran-computer interface.
%	Proceedings of the 1st International IEEE EMBS Conference on Neural Engineering, pp.641-644, Mar 20-22, 2003. 
% [3] A. Schl鰃l, Evaluation of the dataset III of the BCI-competition 2003. 
%	http://ida.first.fraunhofer.de/projects/bci/competition/results/TR_BCI2003_III.pdf


%    $Revision: 1.3 $
%    $Id: bci3eval.m,v 1.3 2003/05/27 14:32:43 schloegl Exp $
%    Copyright (C) 2003 by Alois Schloegl <[e-mail]>	

%    This program is free software; you can redistribute it and/or modify
%    it under the terms of the GNU General Public License as published by
%    the Free Software Foundation; either version 2 of the License, or
%    (at your option) any later version.
%
%    This program is distributed in the hope that it will be useful,
%    but WITHOUT ANY WARRANTY; without even the implied warranty of
%    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
%    GNU General Public License for more details.
%
%    You should have received a copy of the GNU General Public License
%    along with this program; if not, write to the Free Software
%    Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA  02111-1307  USA

%	$Revision$
%	$Id$
%	Copyright (C) 1997-2004 by Alois Schloegl <[e-mail]>	
%    	This is part of the BIOSIG-toolbox http://biosig.sf.net/


if nargin<3,
        DIM=min(find(size(x1)>1));
        if isempty(DIM), DIM=1; end;
end;

% classification error 
if DIM==1,
	o.ERR = (1-mean(sign([-x1;x2]),DIM))/2;
elseif DIM==2,
	o.ERR = (1-mean(sign([-x1,x2]),DIM))/2;
end;

o.BCG1 = (1+mean(sign(-x1),DIM))/2;
o.BCG2 = (1+mean(sign( x2),DIM))/2;
o.T = (1:length(o.BCG1))'/flag_implicit_samplerate;

%%%%% 2nd order statistics
[i1.SUM,o.N1,i1.SSQ] = sumskipnan(x1,DIM);       
[i2.SUM,o.N2,i2.SSQ] = sumskipnan(x2,DIM);       

o.MEAN1 = i1.SUM./o.N1;	% mean
v1    = i1.SSQ-i1.SUM.*o.MEAN1;	% n*var
o.SD1 = sqrt(v1./o.N1); % standard deviation 
%o.SE1 = sqrt(v1)./o.N1; % standard error of the mean 

o.MEAN2 = i2.SUM./o.N2;
v2    = i2.SSQ-i2.SUM.*o.MEAN2;
o.SD2 = sqrt(v2./o.N2);
%o.SE2 = sqrt(v2)./o.N2;


%%%%% Signal-to-Noise Ratio 

	% intra-class variability
if DIM==1,
	vd = var([-x1;x2],[],DIM);        
elseif DIM==2,
	vd = var([-x1,x2],[],DIM);        
end;

o.SNR = 1/4*(o.MEAN2-o.MEAN1).^2./vd; 

%%%%% Mutual Information 
o.I   = 1/2*log2(o.SNR+1);

o.datatype = 'TSD_BCI7';  % useful for PLOTA