<?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%">Dennis J. McFarland</style></author><author><style face="normal" font="default" size="100%">Anderson, Charles W.</style></author><author><style face="normal" font="default" size="100%">Müller, Klaus-Robert</style></author><author><style face="normal" font="default" size="100%">Schlögl, Alois</style></author><author><style face="normal" font="default" size="100%">Krusienski, Dean J.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">BCI Meeting 2005–workshop on BCI signal processing: feature extraction and translation.</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Brain-computer interface (BCI)</style></keyword><keyword><style  face="normal" font="default" size="100%">prediction</style></keyword><keyword><style  face="normal" font="default" size="100%">Signal Processing</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2006</style></year><pub-dates><date><style  face="normal" font="default" size="100%">06/2006</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/pubmed/16792278</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">14</style></volume><pages><style face="normal" font="default" size="100%">135–138</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">This paper describes the outcome of discussions held during the Third International BCI Meeting at a workshop charged with reviewing and evaluating the current state of and issues relevant to brain-computer interface (BCI) feature extraction and translation. The issues discussed include a taxonomy of methods and applications, time-frequency spatial analysis, optimization schemes, the role of insight in analysis, adaptation, and methods for quantifying BCI feedback.</style></abstract></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%">Dennis J. McFarland</style></author><author><style face="normal" font="default" size="100%">Krusienski, Dean J.</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%">Brain-computer interface signal processing at the Wadsworth Center: mu and sensorimotor beta rhythms.</style></title><secondary-title><style face="normal" font="default" size="100%">Progress in brain research</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">adaptation</style></keyword><keyword><style  face="normal" font="default" size="100%">BCI</style></keyword><keyword><style  face="normal" font="default" size="100%">Signal Processing</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2006</style></year><pub-dates><date><style  face="normal" font="default" size="100%">02/2006</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/pubmed/17071245</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">159</style></volume><pages><style face="normal" font="default" size="100%">411–419</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">The Wadsworth brain-computer interface (BCI), based on mu and beta sensorimotor rhythms, uses one- and two-dimensional cursor movement tasks and relies on user training. This is a real-time closed-loop system. Signal processing consists of channel selection, spatial filtering, and spectral analysis. Feature translation uses a regression approach and normalization. Adaptation occurs at several points in this process on the basis of different criteria and methods. It can use either feedforward (e.g., estimating the signal mean for normalization) or feedback control (e.g., estimating feature weights for the prediction equation). We view this process as the interaction between a dynamic user and a dynamic system that coadapt over time. Understanding the dynamics of this interaction and optimizing its performance represent a major challenge for BCI research.</style></abstract></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%">Jonathan Wolpaw</style></author><author><style face="normal" font="default" size="100%">Ramoser, H.</style></author><author><style face="normal" font="default" size="100%">Dennis J. McFarland</style></author><author><style face="normal" font="default" size="100%">Pfurtscheller, G.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">EEG-based communication: improved accuracy by response verification.</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE transactions on rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Computer-Assisted</style></keyword><keyword><style  face="normal" font="default" size="100%">Signal Processing</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">1998</style></year><pub-dates><date><style  face="normal" font="default" size="100%">09/1998</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/pubmed/9749910</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">6</style></volume><pages><style face="normal" font="default" size="100%">326–333</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Humans can learn to control the amplitude of electroencephalographic (EEG) activity in specific frequency bands over sensorimotor cortex and use it to move a cursor to a target on a computer screen. EEG-based communication could provide a new augmentative communication channel for individuals with motor disabilities. In the present system, each dimension of cursor movement is controlled by a linear equation. While the intercept in the equation is continually updated, it does not perfectly eliminate the impact of spontaneous variations in EEG amplitude. This imperfection reduces the accuracy of cursor movement. We evaluated a response verification (RV) procedure in which each outcome is determined by two opposite trials (e.g., one top-target trial and one bottom-target trial). Success, or failure, on both is required for a definitive outcome. The RV procedure reduces errors due to imperfection in intercept selection. Accuracy for opposite-trial pairs exceeds that predicted from the accuracies of individual trials, and greatly exceeds that for same-trial pairs. The RV procedure should be particularly valuable when the first trial has &gt;2 possible targets, because the second trial need only confirm or deny the outcome of the first, and it should be applicable to nonlinear as well as to linear algorithms.</style></abstract></record></records></xml>