<?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%">Shahriari, Yalda</style></author><author><style face="normal" font="default" size="100%">Vaughan, Theresa</style></author><author><style face="normal" font="default" size="100%">McCane, Lynn</style></author><author><style face="normal" font="default" size="100%">Allison, Brendan</style></author><author><style face="normal" font="default" size="100%">Wolpaw, Jonathan</style></author><author><style face="normal" font="default" size="100%">Krusienski, Dean</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">An exploration of BCI performance variations in people with amyotrophic lateral sclerosis using longitudinal EEG data</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Neural Engineering</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">amyotrophic lateral sclerosis (ALS)</style></keyword><keyword><style  face="normal" font="default" size="100%">Brain-computer interface (BCI)</style></keyword><keyword><style  face="normal" font="default" size="100%">Longitudinal Electroencephalogram (EEG)</style></keyword><keyword><style  face="normal" font="default" size="100%">P300 speller</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2019</style></year><pub-dates><date><style  face="normal" font="default" size="100%">05/2019</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://iopscience.iop.org/article/10.1088/1741-2552/ab22ea</style></url></web-urls></urls><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Objective. Brain-computer interface (BCI) technology enables people to use direct measures of brain activity for communication and control. The National Center for Adaptive Neurotechnologies (NCAN) and Helen Hayes Hospital are studying long-term independent home use of P300-based BCIs by people with amyotrophic lateral sclerosis (ALS). This BCI use takes place without technical oversight, and users can encounter substantial variation in their day-to-day BCI performance. The purpose of this study is to identify and evaluate features in the electroencephalogram (EEG) that correlate with successful BCI performance during home use with the goal of improving BCI for people with neuromuscular disorders. Approach. Nine people with ALS used a P300-based BCI at home over several months for communication and computer control. Sessions from a routine calibration task were categorized as successful (≥70%) or unsuccessful (&lt;70%) BCI performance. The correlation of temporal and spectral EEG features with BCI performance was then evaluated. Main Results. BCI performance was positively correlated with an increase in alpha-band (8-14 Hz) activity at locations PO8, P3, Pz, and P4; and beta-band (15-30 Hz) activity at occipital locations. In addition, performance was significantly positively correlated with a positive deflection in EEG amplitude around 220 ms at frontal mid-line locations (i.e., Fz and Cz). BCI performance was negatively correlated with delta-band (1-3 Hz) activity recorded from occipital locations. Significance. These results highlight the variability found in the EEG and describe EEG features that correlate with successful BCI performance during day-to-day use of a P300-based BCI by people with ALS. These results should inform studies focused on improved BCI reliability for people with neuromuscular disorders.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Brunner, Clemens</style></author><author><style face="normal" font="default" size="100%">Andreoni, G</style></author><author><style face="normal" font="default" size="100%">Bianchi, L</style></author><author><style face="normal" font="default" size="100%">Benjamin Blankertz</style></author><author><style face="normal" font="default" size="100%">Breitwieser, C.</style></author><author><style face="normal" font="default" size="100%">Kanoh, S.</style></author><author><style face="normal" font="default" size="100%">Kothe, C. A.</style></author><author><style face="normal" font="default" size="100%">Lecuyer, A.</style></author><author><style face="normal" font="default" size="100%">Makeig, S</style></author><author><style face="normal" font="default" size="100%">Mellinger, J.</style></author><author><style face="normal" font="default" size="100%">Perego, P.</style></author><author><style face="normal" font="default" size="100%">Renard, Y.</style></author><author><style face="normal" font="default" size="100%">Gerwin Schalk</style></author><author><style face="normal" font="default" size="100%">Susila, I.P.</style></author><author><style face="normal" font="default" size="100%">Venthur, B</style></author><author><style face="normal" font="default" size="100%">Mueller-Putz, G.R.</style></author><author><style face="normal" font="default" size="100%">Brendan Z. Allison</style></author><author><style face="normal" font="default" size="100%">Dunne, S.</style></author><author><style face="normal" font="default" size="100%">Leeb, R.</style></author><author><style face="normal" font="default" size="100%">Del R. Millán, J.</style></author><author><style face="normal" font="default" size="100%">A. Nijholt</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">BCI Software Platforms.</style></title><secondary-title><style face="normal" font="default" size="100%">Towards Practical Brain-Computer Interfaces</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2013</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://link.springer.com/chapter/10.1007/978-3-642-29746-5_16</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Biological and Medical Physics</style></publisher><isbn><style face="normal" font="default" size="100%">978-3-642-29745-8</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">In this chapter, we provide an overview of publicly available software platforms for brain–computer interfaces. We have identified seven major BCI platforms and one platform specifically targeted towards feedback and stimulus presentation. We describe the intended target user group (which includes researchers, programmers, and end users), the most important features of each platform such as availability on different operating systems, licences, programming languages involved, supported devices, and so on. These seven platforms are: (1) BCI2000, (2) OpenViBE, (3) TOBI Common Implementation Platform (CIP), (4) BCILAB, (5) BCI++, (6) xBCI, and (7) BF++. The feedback framework is called Pyff. Our conclusion discusses possible synergies and future developments, such as combining different components of different platforms. With this overview, we hope to identify the strengths and weaknesses of each available platform, which should help anyone in the BCI research field in their decision which platform to use for their specific purposes.</style></abstract></record><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Mellinger, Jürgen</style></author><author><style face="normal" font="default" size="100%">Gerwin Schalk</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">Graimann, Bernhard</style></author><author><style face="normal" font="default" size="100%">Pfurtscheller, Gert</style></author><author><style face="normal" font="default" size="100%">Brendan Z. Allison</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Using BCI2000 in BCI Research.</style></title><secondary-title><style face="normal" font="default" size="100%">Brain-Computer Interfaces: Revolutionizing Human-Computer Interaction</style></secondary-title><tertiary-title><style face="normal" font="default" size="100%">The Frontiers Collection</style></tertiary-title></titles><dates><year><style  face="normal" font="default" size="100%">2010</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://dx.doi.org/10.1007/978-3-642-02091-9_15</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Springer Berlin Heidelberg</style></publisher><pages><style face="normal" font="default" size="100%">259-280</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;color: #333333; font-family: 'Helvetica Neue', Arial, Helvetica, sans-serif; font-size: 13px; line-height: 20px;&quot;&gt;BCI2000 is a general-purpose system for brain–computer interface (BCI) research. It can also be used for data acquisition, stimulus presentation, and brain monitoring applications [18,27]. The mission of the BCI2000 project is to facilitate research and applications in these areas. BCI2000 has been in development since 2000 in a collaboration between the Wadsworth Center of the New York State Department of Health in Albany, New York, and the Institute of Medical Psychology and Behavioral Neurobiology at the University of Tübingen, Germany. Many other individuals at different institutions world-wide have contributed to this project.&lt;/span&gt;&lt;/p&gt;</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%">Brendan Z. Allison</style></author><author><style face="normal" font="default" size="100%">Dennis J. McFarland</style></author><author><style face="normal" font="default" size="100%">Gerwin Schalk</style></author><author><style face="normal" font="default" size="100%">Zheng, Shi Dong</style></author><author><style face="normal" font="default" size="100%">Moore-Jackson, Melody</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%">Towards an independent brain-computer interface using steady state visual evoked potentials.</style></title><secondary-title><style face="normal" font="default" size="100%">Clin Neurophysiol</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Clin Neurophysiol</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Adolescent</style></keyword><keyword><style  face="normal" font="default" size="100%">Adult</style></keyword><keyword><style  face="normal" font="default" size="100%">Attention</style></keyword><keyword><style  face="normal" font="default" size="100%">Brain</style></keyword><keyword><style  face="normal" font="default" size="100%">Brain Mapping</style></keyword><keyword><style  face="normal" font="default" size="100%">Dose-Response Relationship, Radiation</style></keyword><keyword><style  face="normal" font="default" size="100%">Electroencephalography</style></keyword><keyword><style  face="normal" font="default" size="100%">Evoked Potentials, Visual</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%">Pattern Recognition, Visual</style></keyword><keyword><style  face="normal" font="default" size="100%">Photic Stimulation</style></keyword><keyword><style  face="normal" font="default" size="100%">Spectrum Analysis</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%">2008</style></year><pub-dates><date><style  face="normal" font="default" size="100%">02/2008</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/pubmed/18077208</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">119</style></volume><pages><style face="normal" font="default" size="100%">399-408</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;h4 style=&quot;font-size: 13px; margin: 0px 0.25em 0px 0px; text-transform: uppercase; float: left; font-family: arial, helvetica, clean, sans-serif; line-height: 17px;&quot;&gt;OBJECTIVE:&amp;nbsp;&lt;/h4&gt;
&lt;p style=&quot;margin: 0px 0px 0.5em; font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;&quot;&gt;Brain-computer interface (BCI) systems using steady state visual evoked potentials (SSVEPs) have allowed healthy subjects to communicate. However, these systems may not work in severely disabled users because they may depend on gaze shifting. This study evaluates the hypothesis that overlapping stimuli can evoke changes in SSVEP activity sufficient to control a BCI. This would provide evidence that SSVEP BCIs could be used without shifting gaze.&lt;/p&gt;
&lt;h4 style=&quot;font-size: 13px; margin: 0px 0.25em 0px 0px; text-transform: uppercase; float: left; font-family: arial, helvetica, clean, sans-serif; line-height: 17px;&quot;&gt;METHODS:&amp;nbsp;&lt;/h4&gt;
&lt;p style=&quot;margin: 0px 0px 0.5em; font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;&quot;&gt;Subjects viewed a display containing two images that each oscillated at a different frequency. Different conditions used overlapping or non-overlapping images to explore dependence on gaze function. Subjects were asked to direct attention to one or the other of these images during each of 12 one-minute runs.&lt;/p&gt;
&lt;h4 style=&quot;font-size: 13px; margin: 0px 0.25em 0px 0px; text-transform: uppercase; float: left; font-family: arial, helvetica, clean, sans-serif; line-height: 17px;&quot;&gt;RESULTS:&amp;nbsp;&lt;/h4&gt;
&lt;p style=&quot;margin: 0px 0px 0.5em; font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;&quot;&gt;Half of the subjects produced differences in SSVEP activity elicited by overlapping stimuli that could support BCI control. In all remaining users, differences did exist at corresponding frequencies but were not strong enough to allow effective control.&lt;/p&gt;
&lt;h4 style=&quot;font-size: 13px; margin: 0px 0.25em 0px 0px; text-transform: uppercase; float: left; font-family: arial, helvetica, clean, sans-serif; line-height: 17px;&quot;&gt;CONCLUSIONS:&amp;nbsp;&lt;/h4&gt;
&lt;p style=&quot;margin: 0px 0px 0.5em; font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;&quot;&gt;The&amp;nbsp;&lt;span class=&quot;highlight&quot;&gt;data&lt;/span&gt;&amp;nbsp;demonstrate that SSVEP differences sufficient for BCI control may be elicited by selective attention to one of two overlapping stimuli. Thus, some SSVEP-based BCI approaches may not depend on gaze control. The nature and extent of any BCI's dependence on muscle activity is a function of many factors, including the display, task, environment, and user.&lt;/p&gt;
&lt;h4 style=&quot;font-size: 13px; margin: 0px 0.25em 0px 0px; text-transform: uppercase; float: left; font-family: arial, helvetica, clean, sans-serif; line-height: 17px;&quot;&gt;SIGNIFICANCE:&amp;nbsp;&lt;/h4&gt;
&lt;p style=&quot;margin: 0px 0px 0.5em; font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;&quot;&gt;SSVEP BCIs might function in severely disabled users unable to reliably control gaze. Further research with these users is necessary to explore the optimal parameters of such a system and validate online performance in a home environment.&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%">Brendan Z. Allison</style></author><author><style face="normal" font="default" size="100%">Wolpaw, Elizabeth Winter</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 systems: progress and prospects.</style></title><secondary-title><style face="normal" font="default" size="100%">Expert review of medical devices</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">ALS</style></keyword><keyword><style  face="normal" font="default" size="100%">assistive communication</style></keyword><keyword><style  face="normal" font="default" size="100%">BCI</style></keyword><keyword><style  face="normal" font="default" size="100%">BMI</style></keyword><keyword><style  face="normal" font="default" size="100%">brain-acuated control</style></keyword><keyword><style  face="normal" font="default" size="100%">brain-computer interface</style></keyword><keyword><style  face="normal" font="default" size="100%">brain-machine interface</style></keyword><keyword><style  face="normal" font="default" size="100%">EEG</style></keyword><keyword><style  face="normal" font="default" size="100%">ERP</style></keyword><keyword><style  face="normal" font="default" size="100%">locked-in syndrome</style></keyword><keyword><style  face="normal" font="default" size="100%">slow cortical potential</style></keyword><keyword><style  face="normal" font="default" size="100%">SSVEP</style></keyword><keyword><style  face="normal" font="default" size="100%">Stroke</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2007</style></year><pub-dates><date><style  face="normal" font="default" size="100%">07/2007</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/pubmed/17605682</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">4</style></volume><pages><style face="normal" font="default" size="100%">463–474</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Brain-computer interface (BCI) systems support communication through direct measures of neural activity without muscle activity. BCIs may provide the best and sometimes the only communication option for users disabled by the most severe neuromuscular disorders and may eventually become useful to less severely disabled and/or healthy individuals across a wide range of applications. This review discusses the structure and functions of BCI systems, clarifies terminology and addresses practical applications. Progress and opportunities in the field are also identified and explicated.</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%">Loeb, Gerald E.</style></author><author><style face="normal" font="default" size="100%">Brendan Z. Allison</style></author><author><style face="normal" font="default" size="100%">Emanuel Donchin</style></author><author><style face="normal" font="default" size="100%">do Nascimento, Omar Feix</style></author><author><style face="normal" font="default" size="100%">Heetderks, William J.</style></author><author><style face="normal" font="default" size="100%">Nijboer, Femke</style></author><author><style face="normal" font="default" size="100%">Shain, William G.</style></author><author><style face="normal" font="default" size="100%">Turner, James N.</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">BCI Meeting 2005–workshop on signals and recording methods.</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%">electrophysiological signals</style></keyword><keyword><style  face="normal" font="default" size="100%">Rehabilitation</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/16792279</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">14</style></volume><pages><style face="normal" font="default" size="100%">138–141</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 highlights of presentations and discussions during the Third International BCI Meeting in a workshop that evaluated potential brain-computer interface (BCI) signals and currently available recording methods. It defined the main potential user populations and their needs, addressed the relative advantages and disadvantages of noninvasive and implanted (i.e., invasive) methodologies, considered ethical issues, and focused on the challenges involved in translating BCI systems from the laboratory to widespread clinical use. The workshop stressed the critical importance of developing useful applications that establish the practical value of BCI technology.</style></abstract></record></records></xml>