PolyMorph: Increasing the Spelling Efficiency of P300 by Selection Matrix PolyMorphism and Sentence-Based Predictions

Alberto Casagrande, Joanna Jarmolowska, Marcello Maria Turconi, Pierpaolo Busan, Francesco Fabris, Piero Paolo Battaglini

Research output: Contribution to journalArticlepeer-review

Abstract

One application of the P300 brain electric signal is sentence spelling, which enables subjects who have lost control of their motor pathways to communicate by selecting characters in a matrix containing all alphabet symbols. This technology still suffers from both low communication/high error rates. A P300 speller, named PolyMorph, which jointly introduces the selection matrix polymorphism (reducing the matrix size by removing useless symbols) and sentence-based predictions (which forecast words on the basis of language statistics) is presented. This is accomplished by using a custom dynamic knowledge-base, tailored to the subject lexicon, and updated in real time with the selections of the subject. The effectiveness of the presented speller is measured in vivo and in silico. The results suggest that the use of PolyMorph increases the number of spelt characters per time unit and reduces the error rate.

Original languageEnglish
Pages (from-to)1-20
Number of pages20
JournalInternational Journal of Human-Computer Interaction
DOIs
Publication statusAccepted/In press - Dec 31 2017

Keywords

  • Brain–computer interface
  • EEG
  • event-related potential
  • P300
  • predictive speller

ASJC Scopus subject areas

  • Human Factors and Ergonomics
  • Human-Computer Interaction
  • Computer Science Applications

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