This 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 [5]. 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.
Krusienski, et al., REF report the results of a comparison of different classifier algorithms, which shows that stepwise linear discriminant analysis (SWLDA) and support vector machines (SVMs) perform well compared to the other classifiers.
A comparison of classification techniques for the P300 Speller
Journal of neural engineering
Journal of neural engineering
Computer Science Medicine