  The P300 event-related potential (ERP), evoked in scalp-recorded
electroencephalography (EEG) by external stimuli, has proven to be a reliable
response for controlling a BCI. The P300 component of an event related
potential is thus widely used in brain-computer interfaces to translate the
subjects' intent by mere thoughts into commands to control artificial devices.
The main challenge in the classification of P300 trials in
electroencephalographic (EEG) data is the low signal-to-noise ratio (SNR) of
the P300 response. To overcome the low SNR of individual trials, it is common
practice to average together many consecutive trials, which effectively
diminishes the random noise. Unfortunately, when more repeated trials are
required for applications such as the P300 speller, the communication rate is
greatly reduced. This has resulted in a need for better methods to improve
single-trial classification accuracy of P300 response. In this work, we use
Principal Component Analysis (PCA) as a preprocessing method and use Linear
Discriminant Analysis (LDA)and neural networks for classification. The results
show that a combination of PCA with these methods provided as high as 13\%
accuracy gain for single-trial classification while using only 3 to 4 principal
components.
