A P300-based BCI classification algorithm using median filtering and Bayesian feature extraction

A brain computer interface (BCI) system translates a person´s brain activity into useful control or communication signals. In this paper, an effective P300-based BCI identification algorithm using median filtering and Bayesian classifier is proposed to improve the classification accuracy and computation efficiency of P300-based BCI. Median filtering is firstly applied to remove noises and Bayesian Linear Discriminant Analysis (BLDA) is then employed for classification. Testing on the P300 speller paradigm in dataset II of 2004 BCI Competition III, we show that a 90% average classification accuracy can be achieved and the highest accuracy is 100%. The proposed method is also computationally efficient and thus it represents a practical implementation for man-computer communication control, especially for on-line applications.