nt_cca_aad

[D,E,R]=nt_cca_match_aad(xA,xB,y,ssize) - calculate metrics for match-mismatch task

  D: d-prime 
  E: error rate
  R: correlation coefficient over entire trial

  xA,xB: stimulus streams
  y: response 
  ssize: samples, segment size [default: all]

0001 function [D,E,R]=nt_cca_aad(xA,xB,y,ssize)
0002 %[D,E,R]=nt_cca_match_aad(xA,xB,y,ssize) - calculate metrics for match-mismatch task
0003 %
0004 %  D: d-prime
0005 %  E: error rate
0006 %  R: correlation coefficient over entire trial
0007 %
0008 %  xA,xB: stimulus streams
0009 %  y: response
0010 %  ssize: samples, segment size [default: all]
0011 
0012 if nargin<3; error('!'); end
0013 if nargin<4; ssize=[]; end
0014 
0015 if ssize ~= round(ssize); error('!'); end
0016 
0017 % clip all trials to same size multiple of wsize
0018 n=size(xA{1},1); % min size?
0019 for iTrial=1:numel(xA)
0020     if size(xA{iTrial}) ~= size(xB{iTrial}); error('!'); end
0021     if size(xA{iTrial}) ~= size(y{iTrial}); error('!'); end
0022     n=min(n,size(xA{iTrial},1));
0023 end
0024 if isempty(ssize); ssize=n; end
0025 n=ssize*floor(n/ssize); % reduce to multiple of wsize
0026 if n<1; error('!'); end
0027 for iTrial=1:numel(xA)
0028     xA{iTrial}=nt_demean(xA{iTrial}(1:n,:)); % clip trials to new length
0029     xB{iTrial}=nt_demean(xB{iTrial}(1:n,:)); % clip trials to new length
0030     y{iTrial}=nt_demean(y{iTrial}(1:n,:));
0031 end
0032 nsegments=n/ssize;
0033 ntrials=numel(xA);
0034 
0035 % merge stimulus streams
0036 x={};
0037 for iTrial=1:numel(xA)
0038     x{iTrial}=[xA{iTrial},xB{iTrial}];
0039 end
0040 
0041 % CCA
0042 shifts=[0];
0043 [AA,BB,RR]=nt_cca_crossvalidate(xA,y,shifts);
0044 R=mean(RR,3);
0045 
0046 for iTrial=1:ntrials
0047     
0048     % calculate model on data excluding this trial
0049     others=setdiff(1:ntrials,iTrial);
0050     
0051     % CCs
0052     xA_CC=nt_mmat(xA(others),AA{iTrial});
0053     xB_CC=nt_mmat(xB(others),AA{iTrial});
0054     yCC=nt_mmat(y(others),BB{iTrial});
0055     ncomp=size(xA_CC{1},2);
0056 
0057     % cut into segments
0058     XA=zeros(ssize,ncomp,numel(others),nsegments);
0059     XB=zeros(ssize,ncomp,numel(others),nsegments);
0060     Y=zeros(ssize,ncomp,numel(others),nsegments);
0061     for iTrial2=1:numel(others)
0062         for iWindow=1:nsegments
0063             start=(iWindow-1)*ssize;
0064             XA(:,:,iTrial2,iWindow)=nt_normcol(nt_demean(xA_CC{iTrial2}(start+(1:ssize),:))); % all mean 0 norm 1
0065             XB(:,:,iTrial2,iWindow)=nt_normcol(nt_demean(xB_CC{iTrial2}(start+(1:ssize),:))); 
0066             Y(:,:,iTrial2,iWindow)=nt_normcol(nt_demean(yCC{iTrial2}(start+(1:ssize),:)));
0067         end
0068     end
0069     
0070     % Euclidean distance between EEG and envelope segments
0071     
0072 %     figure(1); clf;
0073 %     subplot 211;
0074 %     plot([XA(:,1,1,1),Y(:,1,1,1)]); title(corr(XA(:,1,1,1),Y(:,1,1,1)))
0075 %     subplot 212;
0076 %     plot([XB(:,1,1,1),Y(:,1,1,1)]); title(corr(XB(:,1,1,1),Y(:,1,1,1))); pause
0077     
0078     % match
0079     D_match=sqrt(mean((XA-Y).^2));
0080     sz=size(D_match); D_match=reshape(D_match,sz(2:end)); % remove initial singleton
0081     D_match=D_match(:,:)'; % trials X comps
0082 
0083     % mismatch
0084     D_mismatch=sqrt(mean((XB-Y).^2));
0085     sz=size(D_mismatch); D_mismatch=reshape(D_mismatch,sz(2:end));
0086     D_mismatch=D_mismatch(:,:)'; % trials X comps
0087 
0088 %     figure(1); clf;
0089 %     plot([D_match(:,1),D_mismatch(:,1)]); pause
0090     
0091     c0=nt_cov(D_mismatch)/size(D_mismatch,1);
0092     c1=nt_cov(D_match)/size(D_match,1);
0093     [todss,pwr0,pwr1]=nt_dss0(c1,c0);
0094     if mean(D_match*todss(:,1))<0; todss=-todss; end
0095     
0096     %figure(1); clf; plot(pwr1./pwr0,'.-'); pause
0097     if 1
0098     DD_match=D_match*todss(:,1);
0099     DD_mismatch=D_mismatch*todss(:,1);
0100     else
0101     DD_match=D_match(:,1);
0102     DD_mismatch=D_mismatch(:,1);
0103     end
0104 
0105 %     figure(1); clf;
0106 %     plot([DD_match(:,1),DD_mismatch(:,1)]); pause
0107     
0108 
0109     dprime(iTrial)=abs(mean(DD_match)-mean(DD_mismatch)) / std([DD_match-mean(DD_match); DD_mismatch-mean(DD_mismatch)]);    
0110 %     disp(dprime(iTrial));
0111 %     DD_match=D_match(:,1);
0112 %     DD_mismatch=D_mismatch(:,1);
0113 %     dprime(iTrial)=abs(mean(DD_match)-mean(DD_mismatch)) / std([DD_match-mean(DD_match); DD_mismatch-mean(DD_mismatch)]);
0114 %     disp(dprime(iTrial));
0115     
0116     %{
0117     We now have a CCA solution and a JD transform calculated
0118     on other trials. 
0119     
0120     We apply them to segments of this trial.
0121     %}
0122     
0123     % apply same CCA transform:
0124     xA_x=nt_mmat(xA{iTrial},AA{iTrial});
0125     xB_x=nt_mmat(xB{iTrial},AA{iTrial});
0126     y_x=nt_mmat(y{iTrial},BB{iTrial});
0127     
0128     % cut CCs into segments
0129     XA_x=zeros(ssize,ncomp,nsegments);
0130     XB_x=zeros(ssize,ncomp,nsegments);
0131     Y_x=zeros(ssize,ncomp,nsegments);
0132     for iWindow=1:nsegments
0133         start=(iWindow-1)*ssize;
0134         XA_x(:,:,iWindow)=nt_normcol(nt_demean(xA_x(start+(1:ssize),:)));
0135         XB_x(:,:,iWindow)=nt_normcol(nt_demean(xB_x(start+(1:ssize),:)));
0136         Y_x(:,:,iWindow)=nt_normcol(nt_demean(y_x(start+(1:ssize),:)));
0137     end
0138     
0139 %     figure(1); clf
0140 %     subplot 211;
0141 %     plot([XA_x(:,1,iWindow), Y_x(:,1,iWindow)]);title(corr(XA_x(:,1,iWindow), Y_x(:,1,iWindow)));
0142 %     subplot 212;
0143 %     plot([XB_x(:,1,iWindow), Y_x(:,1,iWindow)]);title(corr(XB_x(:,1,iWindow), Y_x(:,1,iWindow)));
0144 %
0145 %     figure(2); clf
0146 %     nt_spect_plot(XA_x(:,1,iWindow), 1024,[],[],64); hold on;
0147 %     nt_spect_plot(Y_x(:,1,iWindow), 1024,[],[],64);
0148 %     pause
0149     
0150     % Euclidean distance for matched and mismatched segments
0151     D_match_x=zeros(nsegments,ncomp);
0152     D_mismatch_x=zeros(nsegments,ncomp);
0153     for iWindow=1:nsegments
0154         D_match_x(iWindow,:)=sqrt( mean((XA_x(:,:,iWindow)-Y_x(:,:,iWindow)).^2) );
0155         D_mismatch_x(iWindow,:)=sqrt( mean((XB_x(:,:,iWindow)-Y_x(:,:,iWindow)).^2) );
0156     end        
0157     
0158     if 1
0159         D_match_x=D_match_x*todss(:,1);
0160         D_mismatch_x=D_mismatch_x*todss(:,1);
0161     else
0162         D_match_x=D_match_x(:,1);
0163         D_mismatch_x=D_mismatch_x(:,1);
0164     end     
0165 %       figure(2); clf;
0166 %       plot([D_match_x,D_mismatch_x])
0167 %      pause
0168     
0169     err(iTrial)=numel(find(D_mismatch_x<D_match_x))/nsegments;
0170     %disp(err(iTrial))
0171 end
0172 
0173 D=mean(dprime);
0174 E=mean(err);
0175 
0176