01626nas a2200229   4500008004100000022001400041245006800055210006500123260001200188300001400200490000600214520094100220653002401161100002501185700002201210700002601232700001901258700002601277700002401303700002101327856004801348       2006                        eng d  a1741-256000aA comparison of classification techniques for the P300 Speller.0 acomparison of classification techniques for the P300 Speller  c12/2006  a299–3050 v33 aThis study assesses the relative performance characteristics of five established classification techniques on data collected using the P300 Speller paradigm, originally described by Farwell and Donchin (1988 Electroenceph. Clin. Neurophysiol. 70 510). Four linear methods: Pearson's correlation method (PCM), Fisher's linear discriminant (FLD), stepwise linear discriminant analysis (SWLDA) and a linear support vector machine (LSVM); and one nonlinear method: Gaussian kernel support vector machine (GSVM), are compared for classifying offline data from eight users. The relative performance of the classifiers is evaluated, along with the practical concerns regarding the implementation of the respective methods. The results indicate that while all methods attained acceptable performance levels, SWLDA and FLD provide the best overall performance and implementation characteristics for practical classification of P300 Speller data.10aNormal Distribution1 aKrusienski, Dean, J.1 aSellers, Eric, W.1 aCabestaing, François1 aBayoudh, Sabri1 aMcFarland, Dennis, J.1 aVaughan, Theresa, M1 aWolpaw, Jonathan  uhttp://www.ncbi.nlm.nih.gov/pubmed/17124334