<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Saa, Jaime F Delgado</style></author><author><style face="normal" font="default" size="100%">Pesters, Adriana de</style></author><author><style face="normal" font="default" size="100%">Dennis J. McFarland</style></author><author><style face="normal" font="default" size="100%">Çetin, Müjdat</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Word-level language modeling for P300 spellers based on discriminative graphical models.</style></title><secondary-title><style face="normal" font="default" size="100%">J Neural Eng</style></secondary-title><alt-title><style face="normal" font="default" size="100%">J Neural Eng</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Brain Computer Interfaces</style></keyword><keyword><style  face="normal" font="default" size="100%">inference algorithms</style></keyword><keyword><style  face="normal" font="default" size="100%">language models</style></keyword><keyword><style  face="normal" font="default" size="100%">P300 speller</style></keyword><keyword><style  face="normal" font="default" size="100%">probabilistic graphical models</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2015</style></year><pub-dates><date><style  face="normal" font="default" size="100%">04/2015</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/pubmed/25686293</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">12</style></volume><pages><style face="normal" font="default" size="100%">026007</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;&lt;b&gt;OBJECTIVE: &lt;/b&gt;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.&lt;/p&gt;&lt;p&gt;&lt;b&gt;APPROACH: &lt;/b&gt;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.&lt;/p&gt;&lt;p&gt;&lt;b&gt;MAIN RESULTS: &lt;/b&gt;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.&lt;/p&gt;&lt;p&gt;&lt;b&gt;SIGNIFICANCE: &lt;/b&gt;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.&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">2</style></issue></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Krusienski, D. J.</style></author><author><style face="normal" font="default" size="100%">Sellers, E. W.</style></author><author><style face="normal" font="default" size="100%">Dennis J. McFarland</style></author><author><style face="normal" font="default" size="100%">Theresa M Vaughan</style></author><author><style face="normal" font="default" size="100%">Jonathan Wolpaw</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Toward enhanced P300 speller performance.</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of neuroscience methods</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">brain-computer interface</style></keyword><keyword><style  face="normal" font="default" size="100%">event related potentials</style></keyword><keyword><style  face="normal" font="default" size="100%">P300 speller</style></keyword><keyword><style  face="normal" font="default" size="100%">stepwise linear discriminant analysis</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2008</style></year><pub-dates><date><style  face="normal" font="default" size="100%">01/2008</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/pubmed/17822777</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">167</style></volume><pages><style face="normal" font="default" size="100%">15–21</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">This study examines the effects of expanding the classical P300 feature space on the classification performance of data collected from a P300 speller paradigm [Farwell LA, Donchin E. Talking off the top of your head: toward a mental prosthesis utilizing event-related brain potentials. Electroenceph Clin Neurophysiol 1988;70:510-23]. Using stepwise linear discriminant analysis (SWLDA) to construct a classifier, the effects of spatial channel selection, channel referencing, data decimation, and maximum number of model features are compared with the intent of establishing a baseline not only for the SWLDA classifier, but for related P300 speller classification methods in general. By supplementing the classical P300 recording locations with posterior locations, online classification performance of P300 speller responses can be significantly improved using SWLDA and the favorable parameters derived from the offline comparative analysis.</style></abstract></record></records></xml>