02134nas a2200241 4500008004100000022001400041245009300055210006900148260001200217300001100229490000700240520137400247653003001621653002501651653002001676653001701696653003501713100002601748700002401774700002601798700002001824856004801844 2015 eng d a1741-255200aWord-level language modeling for P300 spellers based on discriminative graphical models.0 aWordlevel language modeling for P300 spellers based on discrimin c04/2015 a0260070 v123 a

OBJECTIVE: In this work we propose a probabilistic graphical model framework that uses language priors at the level of words as a mechanism to increase the performance of P300-based spellers.

APPROACH: This paper is concerned with brain-computer interfaces based on P300 spellers. Motivated by P300 spelling scenarios involving communication based on a limited vocabulary, we propose a probabilistic graphical model framework and an associated classification algorithm that uses learned statistical models of language at the level of words. Exploiting such high-level contextual information helps reduce the error rate of the speller.

MAIN RESULTS: Our experimental results demonstrate that the proposed approach offers several advantages over existing methods. Most importantly, it increases the classification accuracy while reducing the number of times the letters need to be flashed, increasing the communication rate of the system.

SIGNIFICANCE: The proposed approach models all the variables in the P300 speller in a unified framework and has the capability to correct errors in previous letters in a word, given the data for the current one. The structure of the model we propose allows the use of efficient inference algorithms, which in turn makes it possible to use this approach in real-time applications.

10aBrain Computer Interfaces10ainference algorithms10alanguage models10aP300 speller10aprobabilistic graphical models1 aSaa, Jaime, F Delgado1 ade Pesters, Adriana1 aMcFarland, Dennis, J.1 aÇetin, Müjdat uhttp://www.ncbi.nlm.nih.gov/pubmed/25686293