FINDCLASSIFIER2 is very similar to FINDCLASSIFIER1 but uses a different criterion for selecting the time segment. [CC,Q,TSD,MD]=findclassifier2(D,TRIG,Class,class_times,t_ref); D data, each row is one time point TRIG trigger time points Class class information class_times classification times, combinations of times must be in one row t_ref reference time for Class 0 (optional) CC contains LDA and MD classifiers Q is a list of classification quality for each time of 'class_times' TSD returns the LDA classification MD returns the MD classification [CC,Q,TSD,MD]=findclassifier2(AR,find(trig>0.5)-257,~mod(1:80,2),reshape(1:14*128,16,14*8)'); Reference(s): [1] Schl�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�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, Capri, Italy, Mar 20-22, 2003

- decovm decompose extended covariance matrix into mean (mu),
- perm PERM gives a vector containing all possible sums of two vectors

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