<?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%">Gunduz, Aysegul</style></author><author><style face="normal" font="default" size="100%">Peter Brunner</style></author><author><style face="normal" font="default" size="100%">Sharma, Mohit</style></author><author><style face="normal" font="default" size="100%">Leuthardt, Eric C.</style></author><author><style face="normal" font="default" size="100%">Ritaccio, Anthony L.</style></author><author><style face="normal" font="default" size="100%">Pesaran, Bijan</style></author><author><style face="normal" font="default" size="100%">Schalk, Gerwin</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Differential roles of high gamma and local motor potentials for movement preparation and execution</style></title><secondary-title><style face="normal" font="default" size="100%">Brain-Computer Interfaces</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">BCI</style></keyword><keyword><style  face="normal" font="default" size="100%">brain-computer interfaces</style></keyword><keyword><style  face="normal" font="default" size="100%">ECoG</style></keyword><keyword><style  face="normal" font="default" size="100%">Electrocorticography</style></keyword><keyword><style  face="normal" font="default" size="100%">sensorimotor systems</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2016</style></year><pub-dates><date><style  face="normal" font="default" size="100%">May</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">3</style></volume><pages><style face="normal" font="default" size="100%">88-102</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Determining a person’s intent, such as the planned direction of their movement, directly from their cortical activity could support important applications such as brain-computer interfaces (BCIs). Continuing development of improved BCI systems requires a better understanding of how the brain prepares for and executes movements. To contribute to this understanding, we recorded surface cortical potentials (electrocorticographic signals; ECoG) in 11 human subjects performing a delayed center-out task to establish the differential role of high gamma activity (HGA) and the local motor potential (LMP) as a function of time and anatomical area during movement preparation and execution. High gamma modulations mostly confirm previous findings of sensorimotor cortex involvement, whereas modulations in LMPs are observed in prefrontal cortices. These modulations include directional information during movement planning as well as execution. Our results suggest that sampling signals from these widely distributed cortical areas improves decoding accuracy.</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%">Wang, Z.</style></author><author><style face="normal" font="default" size="100%">Gunduz, Aysegul</style></author><author><style face="normal" font="default" size="100%">Peter Brunner</style></author><author><style face="normal" font="default" size="100%">A L Ritaccio</style></author><author><style face="normal" font="default" size="100%">Ji, Q</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%">Decoding Onset and Direction of Movements using Electrocorticographic (ECoG) Signals in Humans.</style></title><secondary-title><style face="normal" font="default" size="100%">Frontiers in Neuroengineering</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">brain computer interface</style></keyword><keyword><style  face="normal" font="default" size="100%">ECoG</style></keyword><keyword><style  face="normal" font="default" size="100%">movement direction prediction</style></keyword><keyword><style  face="normal" font="default" size="100%">movement onset prediction</style></keyword><keyword><style  face="normal" font="default" size="100%">neurorehabilitation</style></keyword><keyword><style  face="normal" font="default" size="100%">performance augmentation</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2012</style></year><pub-dates><date><style  face="normal" font="default" size="100%">08/2012</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/pubmed/22891058</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">5</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Communication of intent usually requires motor function. This requirement can be limiting when a person is engaged in a task, or prohibitive for some people suffering from neuromuscular disorders. Determining a person's intent, e.g., where and when to move, from brain signals rather than from muscles would have important applications in clinical or other domains. For example, detection of the onset and direction of intended movements may provide the basis for restoration of simple grasping function in people with chronic stroke, or could be used to optimize a user's interaction with the surrounding environment. Detecting the onset and direction of actual movements are a first step in this direction. In this study, we demonstrate that we can detect the onset of intended movements and their direction using electrocorticographic (ECoG) signals recorded from the surface of the cortex in humans. We also demonstrate in a simulation that the information encoded in ECoG about these movements may improve performance in a targeting task. In summary, the results in this paper suggest that detection of intended movement is possible, and may serve useful functions.</style></abstract><issue><style face="normal" font="default" size="100%">15</style></issue></record></records></xml>