  P300-based spellers are one of the main methods for EEG-based brain-computer
interface, and the detection of the P300 target event with high accuracy is an
important prerequisite. The rapid serial visual presentation (RSVP) protocol is
of high interest because it can be used by patients who have lost control over
their eyes. In this study we wish to explore the suitability of recurrent
neural networks (RNNs) as a machine learning method for identifying the P300
signal in RSVP data. We systematically compare RNN with alternative methods
such as linear discriminant analysis (LDA) and convolutional neural network
(CNN). Our results indicate that LDA performs as well as the neural network
models or better on single subject data, but a network combining CNN and RNN
has advantages when transferring learning among subejcts, and is significantly
more resilient to temporal noise than other methods.
