<?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>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Krusienski, Dean J</style></author><author><style face="normal" font="default" size="100%">Gerwin Schalk</style></author><author><style face="normal" font="default" size="100%">Dennis J. McFarland</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%">Tracking of the mu rhythm using an empirically derived matched filter.</style></title><secondary-title><style face="normal" font="default" size="100%">Proc. IEEE International Conference of Neural Engineering</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">bioelectric potentials</style></keyword><keyword><style  face="normal" font="default" size="100%">Brain Computer Interfaces</style></keyword><keyword><style  face="normal" font="default" size="100%">brain modeling</style></keyword><keyword><style  face="normal" font="default" size="100%">brain-computer interface</style></keyword><keyword><style  face="normal" font="default" size="100%">communication device</style></keyword><keyword><style  face="normal" font="default" size="100%">communication system control</style></keyword><keyword><style  face="normal" font="default" size="100%">cortical mu rhythm modulation</style></keyword><keyword><style  face="normal" font="default" size="100%">EEG</style></keyword><keyword><style  face="normal" font="default" size="100%">Electroencephalography</style></keyword><keyword><style  face="normal" font="default" size="100%">empirically derived matched filter</style></keyword><keyword><style  face="normal" font="default" size="100%">handicapped aids</style></keyword><keyword><style  face="normal" font="default" size="100%">laboratories</style></keyword><keyword><style  face="normal" font="default" size="100%">matched filters</style></keyword><keyword><style  face="normal" font="default" size="100%">medical signal detection</style></keyword><keyword><style  face="normal" font="default" size="100%">medical signal processing</style></keyword><keyword><style  face="normal" font="default" size="100%">monitoring</style></keyword><keyword><style  face="normal" font="default" size="100%">motor imagery</style></keyword><keyword><style  face="normal" font="default" size="100%">mu rhythm tracking</style></keyword><keyword><style  face="normal" font="default" size="100%">noninvasive treatment</style></keyword><keyword><style  face="normal" font="default" size="100%">rhythm</style></keyword><keyword><style  face="normal" font="default" size="100%">synchronous motors</style></keyword><keyword><style  face="normal" font="default" size="100%">two-dimensional cursor control data</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2005</style></year><pub-dates><date><style  face="normal" font="default" size="100%">03/2005</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=1419559</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">IEEE</style></publisher><pub-location><style face="normal" font="default" size="100%">Arlington, VA</style></pub-location><isbn><style face="normal" font="default" size="100%">0-7803-8710-4</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language></record></records></xml>