Enhancements of the classification algorithms for the BCI P300 speller diagram

Brain computer interface is one of the most recent and latest hot field in Computer Science which emerged in order to help some handicapped people. This paper investigates different classification algorithms that deal with the BCI P300 speller diagram. The system used is composed of an ensemble of Support vector machines. Three different methods are used, namely weighted ensemble of SVM, channel selection with optimized SVM´s and row & column based SVM ensemble. Experimental results show that proposed methods obtain better results than published results of competition III dataset II.