A comparison of classification techniques for the P300 Speller.
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 (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.
Krusienski
Dean J
DJ
Wadsworth Center, New York State Department of Health, Albany, NY 12201, USA.
Sellers
Eric W
EW
Cabestaing
François
F
Bayoudh
Sabri
S
McFarland
Dennis J
DJ
Vaughan
Theresa M
TM
Wolpaw
Jonathan R
JR
D000328:Adult; D000465:Algorithms; D003627:Data Interpretation, Statistical; D016002:Discriminant Analysis; D004569:Electroencephalography / Q000145:classification*; D018913:Event-Related Potentials, P300 / Q000502:physiology*; D005260:Female; D006801:Humans; D016014:Linear Models; D008297:Male; D008875:Middle Aged; D017711:Nonlinear Dynamics; D016011:Normal Distribution