<?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%">Charles M Gaona</style></author><author><style face="normal" font="default" size="100%">Sharma, Mohit</style></author><author><style face="normal" font="default" size="100%">Zachary V. Freudenberg</style></author><author><style face="normal" font="default" size="100%">Breshears, Jonathan</style></author><author><style face="normal" font="default" size="100%">Bundy, David T</style></author><author><style face="normal" font="default" size="100%">Roland, Jarod</style></author><author><style face="normal" font="default" size="100%">Barbour, Dennis L</style></author><author><style face="normal" font="default" size="100%">Gerwin Schalk</style></author><author><style face="normal" font="default" size="100%">Leuthardt, E C</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Nonuniform high-gamma (60-500 Hz) power changes dissociate cognitive task and anatomy in human cortex.</style></title><secondary-title><style face="normal" font="default" size="100%">J Neurosci</style></secondary-title><alt-title><style face="normal" font="default" size="100%">J. Neurosci.</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Acoustic Stimulation</style></keyword><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%">Analysis of Variance</style></keyword><keyword><style  face="normal" font="default" size="100%">Brain Mapping</style></keyword><keyword><style  face="normal" font="default" size="100%">Brain Waves</style></keyword><keyword><style  face="normal" font="default" size="100%">Cerebral Cortex</style></keyword><keyword><style  face="normal" font="default" size="100%">Cognition Disorders</style></keyword><keyword><style  face="normal" font="default" size="100%">Electroencephalography</style></keyword><keyword><style  face="normal" font="default" size="100%">Epilepsy</style></keyword><keyword><style  face="normal" font="default" size="100%">Evoked Potentials</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%">Neuropsychological Tests</style></keyword><keyword><style  face="normal" font="default" size="100%">Nonlinear Dynamics</style></keyword><keyword><style  face="normal" font="default" size="100%">Photic Stimulation</style></keyword><keyword><style  face="normal" font="default" size="100%">Reaction Time</style></keyword><keyword><style  face="normal" font="default" size="100%">Spectrum Analysis</style></keyword><keyword><style  face="normal" font="default" size="100%">Time Factors</style></keyword><keyword><style  face="normal" font="default" size="100%">Vocabulary</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/21307246</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">31</style></volume><pages><style face="normal" font="default" size="100%">2091-100</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;High-gamma-band (&amp;gt;60 Hz) power changes in cortical electrophysiology are a reliable indicator of focal, event-related cortical activity. Despite discoveries of oscillatory subthreshold and synchronous suprathreshold activity at the cellular level, there is an increasingly popular view that high-gamma-band amplitude changes recorded from cellular ensembles are the result of asynchronous firing activity that yields wideband and uniform power increases. Others have demonstrated independence of power changes in the low- and high-gamma bands, but to date, no studies have shown evidence of any such independence above 60 Hz. Based on nonuniformities in time-frequency analyses of electrocorticographic (ECoG) signals, we hypothesized that induced high-gamma-band (60-500 Hz) power changes are more heterogeneous than currently understood. Using single-word repetition tasks in six human subjects, we showed that functional responsiveness of different ECoG high-gamma sub-bands can discriminate cognitive task (e.g., hearing, reading, speaking) and cortical locations. Power changes in these sub-bands of the high-gamma range are consistently present within single trials and have statistically different time courses within the trial structure. Moreover, when consolidated across all subjects within three task-relevant anatomic regions (sensorimotor, Broca's area, and superior temporal gyrus), these behavior- and location-dependent power changes evidenced nonuniform&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;trends&lt;/span&gt;&lt;span style=&quot;font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;&quot;&gt;&amp;nbsp;across the population. Together, the independence and nonuniformity of power changes across a broad range of frequencies suggest that a new approach to evaluating high-gamma-band cortical activity is necessary. These findings show that in addition to time and location, frequency is another fundamental dimension of high-gamma dynamics.&lt;/span&gt;&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">6</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%">Gunduz, Aysegul</style></author><author><style face="normal" font="default" size="100%">Sanchez, Justin C</style></author><author><style face="normal" font="default" size="100%">Carney, Paul R</style></author><author><style face="normal" font="default" size="100%">Principe, Jose</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Mapping broadband electrocorticographic recordings to two-dimensional hand trajectories in humans Motor control features.</style></title><secondary-title><style face="normal" font="default" size="100%">Neural Netw</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Neural Netw</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%">Brain Mapping</style></keyword><keyword><style  face="normal" font="default" size="100%">Electrodes, Implanted</style></keyword><keyword><style  face="normal" font="default" size="100%">Electrodiagnosis</style></keyword><keyword><style  face="normal" font="default" size="100%">Epilepsy</style></keyword><keyword><style  face="normal" font="default" size="100%">Feasibility Studies</style></keyword><keyword><style  face="normal" font="default" size="100%">Hand</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%">Motor Activity</style></keyword><keyword><style  face="normal" font="default" size="100%">Neural Networks (Computer)</style></keyword><keyword><style  face="normal" font="default" size="100%">Nonlinear Dynamics</style></keyword><keyword><style  face="normal" font="default" size="100%">Signal Processing, Computer-Assisted</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%">11/2009</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/pubmed/19647981</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">22</style></volume><pages><style face="normal" font="default" size="100%">1257-70</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-machine interfaces (BMIs) aim to translate the motor intent of locked-in patients into neuroprosthetic control commands. Electrocorticographical (ECoG) signals provide promising neural inputs to BMIs as shown in recent studies. In this paper, we utilize a broadband spectrum above the fast gamma ranges and systematically study the role of spectral resolution, in which the broadband is partitioned, on the reconstruction of the patients' hand trajectories. Traditionally, the power of ECoG rhythms (&amp;lt;200-300 Hz) has been computed in short duration bins and instantaneously and linearly mapped to cursor trajectories. Neither time embedding, nor nonlinear mappings have been previously implemented in ECoG neuroprosthesis. Herein, mapping of neural modulations to goal-oriented motor behavior is achieved via linear adaptive filters with embedded memory depths and as a novelty through echo state networks (ESNs), which provide nonlinear mappings without compromising training complexity or increasing the number of model parameters, with up to 85% correlation. Reconstructed hand trajectories are analyzed through spatial, spectral and temporal sensitivities. The superiority of nonlinear mappings in the cases of low spectral resolution and abundance of interictal activity is discussed.&lt;/span&gt;&lt;/p&gt;</style></abstract><issue><style face="normal" font="default" size="100%">9</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></records></xml>