title: Analysis of p300 classifiers in brain computer interface speller.
abstract: In this paper, the performance of five classifiers in P300 speller paradigm are compared. Theses classifiers are Linear Support Vector Machine (LSVM), Gaussian Support Vector Machine (GSVM), Neural Network (NN), Fisher Linear Discriminant (FLD), and Kernel Fisher Discriminant (KFD). In classification of P300 waves, there has been a trend to use SVM classifiers. Although they have shown a good performance, in this paper, it is shown that the FLD classifiers outperform the SVM classifiers. FLD classifier uses only ten channels of the recorded electroencephalogram (EEG) signals. This makes them a very good candidate for real-time applications. In addition, FLD approach does not need any optimization similar to other methods. In addition, in this paper, it is shown that the efficiency of using Principal Component Analysis (PCA) for feature reduction results in decreasing the time for the classification and increasing the accuracy.
authors.affiliation: BDP Laboratory, Dept. of Electr. Eng., Sharif Univ. of Technol., Tehran. hmirghasemi@ee.sharif.edu
authors.forename: H
authors.initials: H
authors.lastname: Mirghasemi
authors.forename: R
authors.initials: R
authors.lastname: Fazel-Rezai
authors.forename: M B
authors.initials: MB
authors.lastname: Shamsollahi
mesh_terms: D001185:Artificial Intelligence; D001921:Brain / Q000473:pathology*; D001931:Brain Mapping; D003936:Diagnosis, Computer-Assisted; D004566:Electrodes; D004569:Electroencephalography / Q000295:instrumentation* / Q000379:methods; D018913:Event-Related Potentials, P300; D006801:Humans; D015233:Models, Statistical; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated; D025341:Principal Component Analysis; D015203:Reproducibility of Results; D012815:Signal Processing, Computer-Assisted; D014584:User-Computer Interface