<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="6.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Andre Ferreira</style></author><author><style face="normal" font="default" size="100%">Teodiano Freire</style></author><author><style face="normal" font="default" size="100%">Mario Sarcinelli</style></author><author><style face="normal" font="default" size="100%">Jose Luis Martin</style></author><author><style face="normal" font="default" size="100%">Garcia, Juan Carlos</style></author><author><style face="normal" font="default" size="100%">Manuel Mazo</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Improvements of a Brain-Computer Interface Applied to a Robotic Wheelchair</style></title><secondary-title><style face="normal" font="default" size="100%">Biomedical Engineering Systems and Technologies</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">Communications in Computer and Information Science</style></tertiary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Brain-Computer Interfaces</style></keyword><keyword><style  face="normal" font="default" size="100%">Power Spectral Density components</style></keyword><keyword><style  face="normal" font="default" size="100%">RoboticWheelchair.</style></keyword><keyword><style  face="normal" font="default" size="100%">Support-Vector Machines</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2010</style></year><pub-dates><date><style  face="normal" font="default" size="100%">03/2010</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.springerlink.com/content/wx411876025n1613/</style></url></web-urls></urls><edition><style face="normal" font="default" size="100%">Ana Fred, Joaquim Filipe and Hugo Gamboa</style></edition><publisher><style face="normal" font="default" size="100%">Springer Berlin Heidelberg</style></publisher><pub-location><style face="normal" font="default" size="100%">Berlin</style></pub-location><volume><style face="normal" font="default" size="100%">52</style></volume><pages><style face="normal" font="default" size="100%">64-73</style></pages><isbn><style face="normal" font="default" size="100%">978-3-642-11720-6 (Print), 978-3-642-11721-3 (Online)</style></isbn><language><style face="normal" font="default" size="100%">English</style></language><abstract><style face="normal" font="default" size="100%">Two distinct signal features suitable to be used as input to a Support-Vector Machine (SVM) classifier in an application involving hands motor imagery and the correspondent EEG signal are evaluated in this paper. Such features are the Power Spectral Density (PSD) components and the Adaptive Autoregressive (AAR) parameters. The best result (an accuracy of 97.1%) is obtained when using PSD components, while the AAR parameters generated an accuracy of 91.4%. The results also demonstrate that it is possible to use only two EEG channels (bipolar configuration around C_3 and C_4), discarding the bipolar configuration around C_z. The algorithms were tested with a proprietary EEG data set involving 4 individuals and with a data set provided by the University of Graz (Austria) as well. The resulting classification system is now being implemented in a Brain-Computer Interface (BCI) used to guide a robotic wheelchair.</style></abstract><section><style face="normal" font="default" size="100%">Improvements of a Brain-Computer Interface Applied to a Robotic Wheelchair</style></section></record></records></xml>