PolyMorph: Increasing P300 Spelling Efficiency by Selection Matrix Polymorphism and Sentence-Based Predictions

TL;DR

PolyMorph enhances P300 spelling by reducing matrix size through polymorphism and improving prediction accuracy with sentence-based models, leading to faster and more accurate communication for users with motor impairments.

Contribution

The paper introduces PolyMorph, a novel P300 speller that combines polymorphic selection matrices and sentence-based predictions to improve efficiency and accuracy.

Findings

Increases characters spelled per time unit

Reduces error rate compared to naive spellers

Demonstrates effectiveness through in vivo and in silico tests

Abstract

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.