Beyond principal component analysis: canonical component analysis for data reduction in classification of EPs.
The authors tested a new procedure for the discrimination of EPs obtained in different stimulus situations. In contrast with principal component analysis (PCA) used so far for the purpose of data compression, the method referred to as canonical component analysis (CCA) is optimal for the purpose of discrimination. To illustrate this, the authors performed both PCA and CCA for the same material, then after carrying out discriminant analysis ( SWDA ) for the data transformed in this way, compared the performance of the two procedures in discrimination. In view of both the theoretical and practical considerations, the authors recommend that in the future researchers use CCA instead of PCA in EP studies for data reduction carried out for discrimination.
D003201:Computers; D004569:Electroencephalography / Q000379:methods*; D005074:Evoked Potentials, Visual; D006801:Humans