  P300 is an electric signal emitted by brain about 300 milliseconds after a
rare, but relevant-for-the-user event. One of the applications of this signal
is sentence spelling that enables subjects who lost the control of their motor
pathways to communicate by selecting characters in a matrix containing all the
alphabet symbols. Although this technology has made considerable progress in
the last years, it still suffers from both low communication rate and high
error rate. This article presents a P300 speller, named PolyMorph, that
introduces two major novelties in the field: the selection matrix polymorphism,
that reduces the size of the selection matrix itself by removing useless
symbols, and sentence-based predictions, that exploit all the spelt characters
of a sentence to determine the probability of a word. In order to measure the
effectiveness of the presented speller, we describe two sets of tests: the
first one in vivo and the second one in silico. The results of these
experiments suggest that the use of PolyMorph in place of the naive
character-by-character speller both increases the number of spelt characters
per time unit and reduces the error rate.
