<?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%">Martens, S M M</style></author><author><style face="normal" font="default" size="100%">Mooij, J M</style></author><author><style face="normal" font="default" size="100%">Jeremy Jeremy Hill</style></author><author><style face="normal" font="default" size="100%">Farquhar, Jason</style></author><author><style face="normal" font="default" size="100%">Schölkopf, B</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A graphical model framework for decoding in the visual ERP-based BCI speller.</style></title><secondary-title><style face="normal" font="default" size="100%">Neural Comput</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Neural Comput</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Artificial Intelligence</style></keyword><keyword><style  face="normal" font="default" size="100%">Computer User Training</style></keyword><keyword><style  face="normal" font="default" size="100%">Discrimination Learning</style></keyword><keyword><style  face="normal" font="default" size="100%">Electroencephalography</style></keyword><keyword><style  face="normal" font="default" size="100%">Evoked Potentials</style></keyword><keyword><style  face="normal" font="default" size="100%">Evoked Potentials, Visual</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Language</style></keyword><keyword><style  face="normal" font="default" size="100%">Models, Neurological</style></keyword><keyword><style  face="normal" font="default" size="100%">Models, Theoretical</style></keyword><keyword><style  face="normal" font="default" size="100%">Reading</style></keyword><keyword><style  face="normal" font="default" size="100%">Signal Processing, Computer-Assisted</style></keyword><keyword><style  face="normal" font="default" size="100%">User-Computer Interface</style></keyword><keyword><style  face="normal" font="default" size="100%">Visual Cortex</style></keyword><keyword><style  face="normal" font="default" size="100%">Visual Perception</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2011</style></year><pub-dates><date><style  face="normal" font="default" size="100%">01/2011</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/pubmed/20964540</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">23</style></volume><pages><style face="normal" font="default" size="100%">160-82</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;span style=&quot;font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;&quot;&gt;We present a graphical model framework for decoding in the visual ERP-based speller system. The proposed framework allows researchers to build generative models from which the decoding rules are obtained in a straightforward manner. We suggest two models for generating&amp;nbsp;&lt;/span&gt;&lt;span class=&quot;highlight&quot; style=&quot;font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;&quot;&gt;brain&lt;/span&gt;&lt;span style=&quot;font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;&quot;&gt;&amp;nbsp;signals conditioned on the stimulus events. Both models incorporate letter frequency information but assume different dependencies between&amp;nbsp;&lt;/span&gt;&lt;span class=&quot;highlight&quot; style=&quot;font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;&quot;&gt;brain&lt;/span&gt;&lt;span style=&quot;font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;&quot;&gt;&amp;nbsp;signals and stimulus events. For both models, we derive decoding rules and perform a discriminative training. We show on real visual speller data how decoding performance improves by incorporating letter frequency information and using a more realistic graphical model for the dependencies between the&amp;nbsp;&lt;/span&gt;&lt;span class=&quot;highlight&quot; style=&quot;font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;&quot;&gt;brain&lt;/span&gt;&lt;span style=&quot;font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;&quot;&gt;&amp;nbsp;signals and the stimulus events. Furthermore, we discuss how the standard approach to decoding can be seen as a special case of the graphical model framework. The letter also gives more insight into the discriminative approach for decoding in the visual speller system.&lt;/span&gt;&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">1</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%">Pomeroy, Valerie</style></author><author><style face="normal" font="default" size="100%">Aglioti, Salvatore M</style></author><author><style face="normal" font="default" size="100%">Mark, Victor W</style></author><author><style face="normal" font="default" size="100%">Dennis J. McFarland</style></author><author><style face="normal" font="default" size="100%">Stinear, Cathy</style></author><author><style face="normal" font="default" size="100%">Wolf, Steven L</style></author><author><style face="normal" font="default" size="100%">Corbetta, Maurizio</style></author><author><style face="normal" font="default" size="100%">Fitzpatrick, Susan M</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Neurological principles and rehabilitation of action disorders: rehabilitation interventions.</style></title><secondary-title><style face="normal" font="default" size="100%">Neurorehabil Neural Repair</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Neurorehabil Neural Repair</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Models, Neurological</style></keyword><keyword><style  face="normal" font="default" size="100%">Movement Disorders</style></keyword><keyword><style  face="normal" font="default" size="100%">Recovery of Function</style></keyword><keyword><style  face="normal" font="default" size="100%">Upper Extremity</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2011</style></year><pub-dates><date><style  face="normal" font="default" size="100%">06/2011</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/pubmed/21613536</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">25</style></volume><pages><style face="normal" font="default" size="100%">33S-43S</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">This third chapter discusses the evidence for the rehabilitation of the most common movement disorders of the upper extremity. The authors also present a framework, building on the computation, anatomy, and physiology (CAP) model, for incorporating some of the principles discussed in the 2 previous chapters by Frey et al and Sathian et al in the practice of rehabilitation and for discussing potentially helpful interventions based on emergent neuroscience principles.</style></abstract><issue><style face="normal" font="default" size="100%">5 Suppl</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%">Peter Brunner</style></author><author><style face="normal" font="default" size="100%">Joshi, S</style></author><author><style face="normal" font="default" size="100%">S Briskin</style></author><author><style face="normal" font="default" size="100%">Jonathan Wolpaw</style></author><author><style face="normal" font="default" size="100%">H Bischof</style></author><author><style face="normal" font="default" size="100%">Gerwin Schalk</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Does the 'P300' speller depend on eye gaze?.</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%">Adult</style></keyword><keyword><style  face="normal" font="default" size="100%">Event-Related Potentials, P300</style></keyword><keyword><style  face="normal" font="default" size="100%">Eye Movements</style></keyword><keyword><style  face="normal" font="default" size="100%">Female</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Male</style></keyword><keyword><style  face="normal" font="default" size="100%">Middle Aged</style></keyword><keyword><style  face="normal" font="default" size="100%">Models, Neurological</style></keyword><keyword><style  face="normal" font="default" size="100%">Photic Stimulation</style></keyword><keyword><style  face="normal" font="default" size="100%">User-Computer Interface</style></keyword><keyword><style  face="normal" font="default" size="100%">Young Adult</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%">10/2010</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/pubmed/20858924</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">7</style></volume><pages><style face="normal" font="default" size="100%">056013</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;span style=&quot;font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;&quot;&gt;Many people affected by debilitating neuromuscular disorders such as amyotrophic lateral sclerosis, brainstem stroke or spinal cord injury are impaired in their ability to, or are even unable to, communicate. A brain-computer interface (BCI) uses brain signals, rather than muscles, to re-establish communication with the outside world. One particular BCI approach is the so-called 'P300 matrix speller' that was first described by Farwell and Donchin (1988 Electroencephalogr. Clin. Neurophysiol. 70 510-23). It has been widely assumed that this method does not depend on the ability to focus on the desired character, because it was thought that it relies primarily on the P300-evoked potential and minimally, if at all, on other EEG features such as the visual-evoked potential (VEP). This issue is highly relevant for the clinical application of this BCI method, because eye movements may be impaired or lost in the relevant user population. This study investigated the extent to which the performance in a 'P300' speller BCI depends on eye gaze. We evaluated the performance of 17 healthy subjects using a 'P300' matrix speller under two conditions. Under one condition ('letter'), the subjects focused their eye gaze on the intended letter, while under the second condition ('center'), the subjects focused their eye gaze on a fixation cross that was located in the center of the matrix. The results show that the performance of the 'P300' matrix speller in normal subjects depends in considerable measure on gaze direction. They thereby disprove a widespread assumption in BCI research, and suggest that this BCI might function more effectively for people who retain some eye-movement control. The applicability of these findings to people with severe neuromuscular disabilities (particularly in eye-movements) remains to be determined.&lt;/span&gt;&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">5</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%">Adam J Wilson</style></author><author><style face="normal" font="default" size="100%">Mellinger, Jürgen</style></author><author><style face="normal" font="default" size="100%">Gerwin Schalk</style></author><author><style face="normal" font="default" size="100%">Williams, Justin C</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">A procedure for measuring latencies in brain-computer interfaces.</style></title><secondary-title><style face="normal" font="default" size="100%">IEEE Trans Biomed Eng</style></secondary-title><alt-title><style face="normal" font="default" size="100%">IEEE Trans Biomed Eng</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Brain</style></keyword><keyword><style  face="normal" font="default" size="100%">Computer Systems</style></keyword><keyword><style  face="normal" font="default" size="100%">Electroencephalography</style></keyword><keyword><style  face="normal" font="default" size="100%">Evoked Potentials</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Models, Neurological</style></keyword><keyword><style  face="normal" font="default" size="100%">Reproducibility of Results</style></keyword><keyword><style  face="normal" font="default" size="100%">Signal Processing, Computer-Assisted</style></keyword><keyword><style  face="normal" font="default" size="100%">Time Factors</style></keyword><keyword><style  face="normal" font="default" size="100%">User-Computer Interface</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%">06/2010</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/pubmed/20403781</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">57</style></volume><pages><style face="normal" font="default" size="100%">1785-97</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;span style=&quot;font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;&quot;&gt;Brain-computer interface (BCI) systems must process neural signals with consistent timing in order to support adequate system performance. Thus, it is important to have the capability to determine whether a particular BCI configuration (i.e., hardware and software) provides adequate timing performance for a particular experiment. This report presents a method of measuring and quantifying different aspects of system timing in several typical BCI experiments across a range of settings, and presents comprehensive measures of expected overall system latency for each experimental configuration.&lt;/span&gt;&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">7</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%">Martens, S M M</style></author><author><style face="normal" font="default" size="100%">Jeremy Jeremy Hill</style></author><author><style face="normal" font="default" size="100%">Farquhar, Jason</style></author><author><style face="normal" font="default" size="100%">Schölkopf, B</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Overlap and refractory effects in a brain-computer interface speller based on the visual P300 event-related potential.</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%">Algorithms</style></keyword><keyword><style  face="normal" font="default" size="100%">Brain</style></keyword><keyword><style  face="normal" font="default" size="100%">Cognition</style></keyword><keyword><style  face="normal" font="default" size="100%">Computer Simulation</style></keyword><keyword><style  face="normal" font="default" size="100%">Electroencephalography</style></keyword><keyword><style  face="normal" font="default" size="100%">Event-Related Potentials, P300</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Models, Neurological</style></keyword><keyword><style  face="normal" font="default" size="100%">Pattern Recognition, Automated</style></keyword><keyword><style  face="normal" font="default" size="100%">Photic Stimulation</style></keyword><keyword><style  face="normal" font="default" size="100%">Semantics</style></keyword><keyword><style  face="normal" font="default" size="100%">Signal Processing, Computer-Assisted</style></keyword><keyword><style  face="normal" font="default" size="100%">Task Performance and Analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">User-Computer Interface</style></keyword><keyword><style  face="normal" font="default" size="100%">Writing</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2009</style></year><pub-dates><date><style  face="normal" font="default" size="100%">04/2009</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/pubmed/19255462</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">6</style></volume><pages><style face="normal" font="default" size="100%">026003</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;span style=&quot;font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;&quot;&gt;We reveal the presence of refractory and overlap effects in the event-related potentials in visual P300 speller datasets, and we show their negative impact on the performance of the system. This finding has important implications for how to encode the letters that can be selected for communication. However, we show that such effects are dependent on stimulus parameters: an alternative stimulus type based on apparent motion suffers less from the refractory effects and leads to an improved letter prediction performance.&lt;/span&gt;&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%">Pei, Xiao-Mei</style></author><author><style face="normal" font="default" size="100%">Zheng, Shi Dong</style></author><author><style face="normal" font="default" size="100%">Zhang, Ai-hua</style></author><author><style face="normal" font="default" size="100%">Duan, Fu-jian</style></author><author><style face="normal" font="default" size="100%">Bin, Guang-yu</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Discussion on &quot;Towards a quantitative characterization of functional states of the brain: from the non-linear methodology to the global linear description&quot; by J. Wackermann.</style></title><secondary-title><style face="normal" font="default" size="100%">Int J Psychophysiol</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Int J Psychophysiol</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Algorithms</style></keyword><keyword><style  face="normal" font="default" size="100%">Brain</style></keyword><keyword><style  face="normal" font="default" size="100%">Diagnostic Imaging</style></keyword><keyword><style  face="normal" font="default" size="100%">Functional Laterality</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Linear Models</style></keyword><keyword><style  face="normal" font="default" size="100%">Models, Neurological</style></keyword><keyword><style  face="normal" font="default" size="100%">Nonlinear Dynamics</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%">06/2005</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/pubmed/15866324</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">56</style></volume><pages><style face="normal" font="default" size="100%">201-7</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;span style=&quot;font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;&quot;&gt;Wackermann (1999) [Wackermann, J., 1999. Towards a quantitative characterization of functional states of the&amp;nbsp;&lt;/span&gt;&lt;span class=&quot;highlight&quot; style=&quot;font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;&quot;&gt;brain&lt;/span&gt;&lt;span style=&quot;font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;&quot;&gt;: from the non-linear methodology to the global linear description. Int. J. Psychophysiol. 34, 65-80] proposed Sigma-phi-Omega system for describing the global&amp;nbsp;&lt;/span&gt;&lt;span class=&quot;highlight&quot; style=&quot;font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;&quot;&gt;brain&lt;/span&gt;&lt;span style=&quot;font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;&quot;&gt;&amp;nbsp;macro-state, in which Omega complexity was used to quantify the degree of synchrony between spatially distributed EEG processes. In this paper the effect of signal power on Omega complexity is discussed, which was not considered in Wackermann's paper (1999). Then an improved method for eliminating the effect of signal power on Omega complexity is proposed. Finally a case study on the degree of synchrony between two-channel EEG signals over different&amp;nbsp;&lt;/span&gt;&lt;span class=&quot;highlight&quot; style=&quot;font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;&quot;&gt;brain&lt;/span&gt;&lt;span style=&quot;font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;&quot;&gt;&amp;nbsp;regions during hand motor imagery is given. The results show that the improved Omega complexity measure would characterize the true degree of synchrony among the EEG signals by eliminating the influence of signal power.&lt;/span&gt;&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">3</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%">Baxter, Bill</style></author><author><style face="normal" font="default" size="100%">Dow, B M</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Horizontal organization of orientation-sensitive cells in primate visual cortex.</style></title><secondary-title><style face="normal" font="default" size="100%">Biol Cybern</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Biol Cybern</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Animals</style></keyword><keyword><style  face="normal" font="default" size="100%">Electron Transport Complex IV</style></keyword><keyword><style  face="normal" font="default" size="100%">Form Perception</style></keyword><keyword><style  face="normal" font="default" size="100%">Models, Neurological</style></keyword><keyword><style  face="normal" font="default" size="100%">Pattern Recognition, Visual</style></keyword><keyword><style  face="normal" font="default" size="100%">Visual Cortex</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">1989</style></year><pub-dates><date><style  face="normal" font="default" size="100%">07/1989</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/pubmed/2548628</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">61</style></volume><pages><style face="normal" font="default" size="100%">171-82</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;span style=&quot;font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;&quot;&gt;In the visual cortex of the monkey the horizontal organization of the preferred orientations of orientation-selective cells follows two opposing rules: (1) neighbors tend to have similar orientation preferences, and (2) many different orientations are observed in a local region. We have described a classification for orientation maps based on the types of topological singularities and the spacing of these singularities relative to the cytochrome oxidase blobs. Using the orientation drift rate as a measure we have compared simulated orientation maps to published records of horizontal electrode recordings.&lt;/span&gt;&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">3</style></issue></record></records></xml>