<?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%">Ignacio Saez</style></author><author><style face="normal" font="default" size="100%">Jack Lin</style></author><author><style face="normal" font="default" size="100%">Arjen Stolk</style></author><author><style face="normal" font="default" size="100%">Edward Chang</style></author><author><style face="normal" font="default" size="100%">Josef Parvizi</style></author><author><style face="normal" font="default" size="100%">Gerwin Schalk</style></author><author><style face="normal" font="default" size="100%">Robert T. Knight</style></author><author><style face="normal" font="default" size="100%">Ming Hsu</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Encoding of Multiple Reward-Related Computations in Transient and Sustained High-Frequency Activity in Human OFC</style></title><secondary-title><style face="normal" font="default" size="100%">Current Biology</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">ECoC</style></keyword><keyword><style  face="normal" font="default" size="100%">Electrocorticography</style></keyword><keyword><style  face="normal" font="default" size="100%">ERP</style></keyword><keyword><style  face="normal" font="default" size="100%">event-related potential</style></keyword><keyword><style  face="normal" font="default" size="100%">field potential</style></keyword><keyword><style  face="normal" font="default" size="100%">FP</style></keyword><keyword><style  face="normal" font="default" size="100%">HFA</style></keyword><keyword><style  face="normal" font="default" size="100%">high-frequency activity</style></keyword><keyword><style  face="normal" font="default" size="100%">OFC</style></keyword><keyword><style  face="normal" font="default" size="100%">orbitofrontal cortex</style></keyword><keyword><style  face="normal" font="default" size="100%">reward-prediction error</style></keyword><keyword><style  face="normal" font="default" size="100%">RPE</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2018</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.sciencedirect.com/science/article/pii/S0960982218309758</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">28</style></volume><pages><style face="normal" font="default" size="100%">2889 - 2899.e3</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Summary Human orbitofrontal cortex (OFC) has long been implicated in value-based decision making. In recent years, convergent evidence from human and model organisms has further elucidated its role in representing reward-related computations underlying decision making. However, a detailed description of these processes remains elusive due in part to (1) limitations in our ability to observe human OFC neural dynamics at the timescale of decision processes and (2) methodological and interspecies differences that make it challenging to connect human and animal findings or to resolve discrepancies when they arise. Here, we sought to address these challenges by conducting multi-electrode electrocorticography (ECoG) recordings in neurosurgical patients during economic decision making to elucidate the electrophysiological signature, sub-second temporal profile, and anatomical distribution of reward-related computations within human OFC. We found that high-frequency activity (HFA) (70–200 Hz) reflected multiple valuation components grouped in two classes of valuation signals that were dissociable in temporal profile and information content: (1) fast, transient responses reflecting signals associated with choice and outcome processing, including anticipated risk and outcome regret, and (2) sustained responses explicitly encoding what happened in the immediately preceding trial. Anatomically, these responses were widely distributed in partially overlapping networks, including regions in the central OFC (Brodmann areas 11 and 13), which have been consistently implicated in reward processing in animal single-unit studies. Together, these results integrate insights drawn from human and animal studies and provide evidence for a role of human OFC in representing multiple reward computations.</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%">Miller, Kai J</style></author><author><style face="normal" font="default" size="100%">Gerwin Schalk</style></author><author><style face="normal" font="default" size="100%">Hermes, Dora</style></author><author><style face="normal" font="default" size="100%">Ojemann, Jeffrey G</style></author><author><style face="normal" font="default" size="100%">Rao, Rajesh P N</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Near-Instantaneous Classification of Perceptual States from Cortical Surface Recordings</style></title><secondary-title><style face="normal" font="default" size="100%">Brain-Computer Interface Research: A State-of-the-Art Summary</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">broadband power</style></keyword><keyword><style  face="normal" font="default" size="100%">Electrocorticography</style></keyword><keyword><style  face="normal" font="default" size="100%">event-related potential</style></keyword><keyword><style  face="normal" font="default" size="100%">fusiform cortex</style></keyword><keyword><style  face="normal" font="default" size="100%">human vision</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2015</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://link.springer.com/chapter/10.1007/978-3-319-25190-5_10</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">Springer International Publishing</style></publisher><pub-location><style face="normal" font="default" size="100%">New York City, NY</style></pub-location><pages><style face="normal" font="default" size="100%">105-114</style></pages><isbn><style face="normal" font="default" size="100%">978-3-319-25188-2</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Human visual processing is of such complexity that, despite decades of focused research, many basic questions remain unanswered. Although we know that the inferotemporal cortex is a key region in object recognition, we don’t fully understand its physiologic role in brain function, nor do we have the full set of tools to explore this question. Here we show that electrical potentials from the surface of the human brain contain enough information to decode a subject’s perceptual state accurately, and with fine temporal precision. Electrocorticographic (ECoG) arrays were placed over the inferotemporal cortical areas of seven subjects. Pictures of faces and houses were quickly presented while each subject performed a simple visual task. Results showed that two well-known types of brain signals—event-averaged broadband power and event-averaged raw potential—can independently or together be used to classify the presented image. When applied to continuously recorded brain activity, our decoding technique could accurately predict whether each stimulus was a face, house, or neither, with  20 ms timing error. These results provide a roadmap for improved brain-computer interfacing tools to help neurosurgeons, research scientists, engineers, and, ultimately, patients.</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%">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%">Emrich, Joseph F</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%">Rapid Communication with a &quot;P300&quot; Matrix Speller Using Electrocorticographic Signals (ECoG).</style></title><secondary-title><style face="normal" font="default" size="100%">Front Neurosci</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Front Neurosci</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">brain-computer interface</style></keyword><keyword><style  face="normal" font="default" size="100%">Electrocorticography</style></keyword><keyword><style  face="normal" font="default" size="100%">event-related potential</style></keyword><keyword><style  face="normal" font="default" size="100%">P300</style></keyword><keyword><style  face="normal" font="default" size="100%">speller</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%">02/2011</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/pubmed/21369351</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">5</style></volume><pages><style face="normal" font="default" size="100%">5</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;A&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-computer interface&lt;/span&gt;&lt;span style=&quot;font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;&quot;&gt;&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;BCI&lt;/span&gt;&lt;span style=&quot;font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;&quot;&gt;) can provide a non-muscular communication channel to severely disabled people. One particular realization of a&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;BCI&lt;/span&gt;&lt;span style=&quot;font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;&quot;&gt;&amp;nbsp;is the P300 matrix speller that was originally described by Farwell and Donchin (1988). This speller uses event-related potentials (ERPs) that include the P300 ERP. All previous online studies of the P300 matrix speller used scalp-recorded electroencephalography (EEG) and were limited in their communication performance to only a few characters per minute. In our study, we investigated the feasibility of using electrocorticographic (ECoG) signals for online operation of the matrix speller, and determined associated spelling rates. We used the matrix speller that is implemented in the BCI2000 system. This speller used ECoG signals that were recorded from frontal, parietal, and occipital areas in one subject. This subject spelled a total of 444 characters in online experiments. The results showed that the subject sustained a rate of 17&amp;thinsp;characters/min (i.e., 69&amp;thinsp;bits/min), and achieved a peak rate of 22&amp;thinsp;characters/min (i.e., 113&amp;thinsp;bits/min). Detailed analysis of the results suggests that ERPs over visual areas (i.e., visual evoked potentials) contribute significantly to the performance of the matrix speller&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;BCI&lt;/span&gt;&lt;span style=&quot;font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;&quot;&gt;&amp;nbsp;system. Our results also point to potential reasons for the apparent advantages in spelling performance of ECoG compared to EEG. Thus, with additional verification in more subjects, these results may further extend the communication options for people with serious neuromuscular disabilities.&lt;/span&gt;&lt;/p&gt;</style></abstract></record></records></xml>