Wavelets and ensemble of FLDs for P300 classification

Over the last few years various P300 classification algorithms have been assessed using the P300 data provided by the Wadsworth center for brain-computer interface (BCI) competitions II and III. In this paper a novel method of P300 classification is presented and compared to the state of the art results obtained for BCI competition II data set lib and BCI competition III data set II. The novel classification method includes discrete-wavelet transform (DWT) preprocessing and an ensemble of Fisher´s linear discriminants for classification. The performance of the proposed method is as good as the state of the art method for the BCI competition II data set and only slightly worse than the state of the art method for BCI competition III data sets. Furthermore the proposed method is far less computationally expensive than the current state of the art method and could be modified for adaptive behavior in an online system.