<?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%">Principe, Jose</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Correntropy as a novel measure for nonlinearity tests.</style></title><secondary-title><style face="normal" font="default" size="100%">Signal Processing</style></secondary-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Correntropy</style></keyword><keyword><style  face="normal" font="default" size="100%">kernel methods</style></keyword><keyword><style  face="normal" font="default" size="100%">nonlinearity tests</style></keyword><keyword><style  face="normal" font="default" size="100%">surrogate methods</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%">01/2009</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.sciencedirect.com/science/article/pii/S0165168408002119</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">89</style></volume><pages><style face="normal" font="default" size="100%">14 - 23</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: #2e2e2e; font-family: 'Arial Unicode MS', 'Arial Unicode', Arial, 'URW Gothic L', Helvetica, Tahoma, 'Cambria Math', sans-serif; font-size: 13px; line-height: 20px; text-align: justify; word-spacing: -1.010229468345642px;&quot;&gt;Nonlinearity tests have become an essential step in system analysis and modeling due to the computational demands and complexity of analysis involved in nonlinear modeling. Standard nonlinear measures are either too complicated to estimate accurately (such as Lyapunov exponents and correlation dimension), or not able to capture sufficient but not necessary conditions of nonlinearity (such as time asymmetry). Correntropy is a kernel-based similarity measure which contains the information of both statistical and temporal structure of the underlying dataset. The capability of preserving nonlinear characteristics makes correntropy a suitable candidate as a measure for determining nonlinear dynamics. Moreover, since correntropy makes use of kernel methods, its estimation is computationally efficient. Using correntropy as the test statistic, nonlinearity tests based on the null hypothesis that signals of interest are realizations of linear Gaussian stochastic processes are carried out via surrogate data methods. Experiments performed on linear Gaussian, linear non-Gaussian, and nonlinear systems with varying in-band noise levels, data lengths, and kernel sizes confirm that correntropy can be employed as a discriminative measure for detecting nonlinear characteristics in time series. Results of tests performed on data collected from natural systems are in agreement with findings in time series analysis literature.&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%">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%">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%">Principe, Jose</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Electrocorticographic interictal spike removal via denoising source separation for improved neuroprosthesis control.</style></title><secondary-title><style face="normal" font="default" size="100%">Conf Proc IEEE Eng Med Biol Soc</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Conf Proc IEEE Eng Med Biol Soc</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Algorithms</style></keyword><keyword><style  face="normal" font="default" size="100%">Artifacts</style></keyword><keyword><style  face="normal" font="default" size="100%">Diagnosis, Computer-Assisted</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, Motor</style></keyword><keyword><style  face="normal" font="default" size="100%">Motor Cortex</style></keyword><keyword><style  face="normal" font="default" size="100%">Reproducibility of Results</style></keyword><keyword><style  face="normal" font="default" size="100%">Sensitivity and Specificity</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%">08/2008</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/pubmed/19163895</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">2008</style></volume><pages><style face="normal" font="default" size="100%">5224-7</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;Electrocorticographic (ECoG) neuroprosthesis is a promising area of research that could provide channels of communication and control for patients who have lost their motor functions due to damage to the nervous system. However, implantation of subdural electrodes are clinically restricted to diagnostics of pre-surgical epileptic patients. Hence, interictal activity is present in the recordings across various areas of the sensorimotor cortex and suppresses the amplitude modulated features extracted to model hand trajectories. Denoising source separation is a recently introduced framework which extracts hidden structures of interest within 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;data&lt;/span&gt;&lt;span style=&quot;font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;&quot;&gt;&amp;nbsp;through denoising the source estimates with filters designed around prior knowledge on the observations. Herein, we exploit the high amplitude quasiperiodic nature of the observed interictal spikes and show that removal of the interictal activity improves linear prediction of hand trajectories.&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%">Sanchez, Justin C</style></author><author><style face="normal" font="default" size="100%">Gunduz, Aysegul</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%">Extraction and localization of mesoscopic motor control signals for human ECoG neuroprosthetics.</style></title><secondary-title><style face="normal" font="default" size="100%">J Neurosci Methods</style></secondary-title><alt-title><style face="normal" font="default" size="100%">J. Neurosci. Methods</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Adolescent</style></keyword><keyword><style  face="normal" font="default" size="100%">Biofeedback, Psychology</style></keyword><keyword><style  face="normal" font="default" size="100%">Brain Mapping</style></keyword><keyword><style  face="normal" font="default" size="100%">Cerebral Cortex</style></keyword><keyword><style  face="normal" font="default" size="100%">Electroencephalography</style></keyword><keyword><style  face="normal" font="default" size="100%">Epilepsies, Partial</style></keyword><keyword><style  face="normal" font="default" size="100%">Female</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%">Magnetic Resonance Imaging</style></keyword><keyword><style  face="normal" font="default" size="100%">Physical Therapy Modalities</style></keyword><keyword><style  face="normal" font="default" size="100%">Psychomotor Performance</style></keyword><keyword><style  face="normal" font="default" size="100%">Signal Processing, Computer-Assisted</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%">01/2008</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/pubmed/17582507</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">167</style></volume><pages><style face="normal" font="default" size="100%">63-81</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;Electrocorticogram (&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;ECoG&lt;/span&gt;&lt;span style=&quot;font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;&quot;&gt;) recordings for&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;neuroprosthetics&lt;/span&gt;&lt;span style=&quot;font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;&quot;&gt;&amp;nbsp;provide 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;mesoscopic&lt;/span&gt;&lt;span style=&quot;font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;&quot;&gt;&amp;nbsp;level of abstraction of brain function between microwire single neuron recordings and the electroencephalogram (EEG). Single-trial&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;ECoG&lt;/span&gt;&lt;span style=&quot;font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;&quot;&gt;&amp;nbsp;neural interfaces require appropriate feature&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;extraction&lt;/span&gt;&lt;span style=&quot;font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;&quot;&gt;&amp;nbsp;and signal processing methods to identify and model in real-time signatures of&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;motor&lt;/span&gt;&lt;span style=&quot;font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;&quot;&gt;&amp;nbsp;events in spontaneous brain activity. Here, we develop the clinical experimental paradigm and analysis tools to record broadband (1Hz to 6kHz)&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;ECoG&lt;/span&gt;&lt;span style=&quot;font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;&quot;&gt;&amp;nbsp;from patients participating in a reaching and pointing task. Motivated by the significant role of amplitude modulated rate coding in extracellular spike based brain-machine interfaces (BMIs), we develop methods to quantify spatio-temporal intermittent increased&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;ECoG&lt;/span&gt;&lt;span style=&quot;font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;&quot;&gt;&amp;nbsp;voltages to determine if they provide viable&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;control&lt;/span&gt;&lt;span style=&quot;font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;&quot;&gt;&amp;nbsp;inputs for&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;ECoG&lt;/span&gt;&lt;span style=&quot;font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;&quot;&gt;&amp;nbsp;neural interfaces. This study seeks to explore preprocessing modalities that emphasize amplitude modulation across frequencies and channels in 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;ECoG&lt;/span&gt;&lt;span style=&quot;font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;&quot;&gt;&amp;nbsp;above the level of noisy background fluctuations in order to derive the commands for complex, continuous&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;control&lt;/span&gt;&lt;span style=&quot;font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;&quot;&gt;&amp;nbsp;tasks. Preliminary experiments show that it is possible to derive online predictive models and spatially localize the generation of commands in the cortex for&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;motor&lt;/span&gt;&lt;span style=&quot;font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;&quot;&gt;&amp;nbsp;tasks using amplitude modulated&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;ECoG&lt;/span&gt;&lt;span style=&quot;font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;&quot;&gt;.&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%">Wang, Yiwen</style></author><author><style face="normal" font="default" size="100%">Sanchez, Justin C</style></author><author><style face="normal" font="default" size="100%">Principe, Jose</style></author><author><style face="normal" font="default" size="100%">Mitzelfelt, Jeremiah D</style></author><author><style face="normal" font="default" size="100%">Gunduz, Aysegul</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Analysis of the correlation between local field potentials and neuronal firing rate in the motor cortex.</style></title><secondary-title><style face="normal" font="default" size="100%">Conf Proc IEEE Eng Med Biol Soc</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Conf Proc IEEE Eng Med Biol Soc</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Action Potentials</style></keyword><keyword><style  face="normal" font="default" size="100%">Animals</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%">Electric Stimulation</style></keyword><keyword><style  face="normal" font="default" size="100%">Electrodes</style></keyword><keyword><style  face="normal" font="default" size="100%">Evoked Potentials, Motor</style></keyword><keyword><style  face="normal" font="default" size="100%">Male</style></keyword><keyword><style  face="normal" font="default" size="100%">Models, Statistical</style></keyword><keyword><style  face="normal" font="default" size="100%">Motor Cortex</style></keyword><keyword><style  face="normal" font="default" size="100%">Neurons</style></keyword><keyword><style  face="normal" font="default" size="100%">Rats</style></keyword><keyword><style  face="normal" font="default" size="100%">Rats, Sprague-Dawley</style></keyword><keyword><style  face="normal" font="default" size="100%">Signal Processing, Computer-Assisted</style></keyword><keyword><style  face="normal" font="default" size="100%">Synaptic Transmission</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%">09/2006</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/pubmed/17946745</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">1</style></volume><pages><style face="normal" font="default" size="100%">6185-8</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;Neuronal firing rate has been the signal of choice for invasive motor&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;machine&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;interfaces&lt;/span&gt;&lt;span style=&quot;font-family: arial, helvetica, clean, sans-serif; font-size: 13px; line-height: 17px;&quot;&gt;&amp;nbsp;(BMI). The use of local field potentials (LFP) in BMI experiments may provide additional dendritic information about movement intent and may improve performance. Here we study the time-varying amplitude modulated relationship between local field potentials (LFP) and single unit activity (SUA) in the motor cortex. We record LFP and SUA in the primary motor cortex of rats trained to perform a lever pressing task, and evaluate the correlation between pairs of peri-event time histograms (PETH) and movement evoked local field potentials (mEP) at the same electrode. Three different correlation coefficients were calculated and compared between the neuronal PETH and the magnitude and power of the mEP. Correlation as high as 0.7 for some neurons occurred between the PETH and the mEP magnitude. As expected, the correlations between the single trial LFP and SUV are much lower due to the inherent variability of both signals.&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%">Gunduz, Aysegul</style></author><author><style face="normal" font="default" size="100%">Principe, Jose</style></author><author><style face="normal" font="default" size="100%">Freeman, Walter</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">On-line Detection of Perceptual Signatures in Multichannel ECoG.</style></title><secondary-title><style face="normal" font="default" size="100%">Conf Proc IEEE Eng Med Biol Soc</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Conf Proc IEEE Eng Med Biol Soc</style></alt-title></titles><dates><year><style  face="normal" font="default" size="100%">2005</style></year><pub-dates><date><style  face="normal" font="default" size="100%">09/2005</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=1617281&amp;abstractAccess=no&amp;userType=inst</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">4</style></volume><pages><style face="normal" font="default" size="100%">3683-6</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Neocortical ECoG studies have unveiled the presence of active states - spatial patterns of amplitude modulation- in the beta- gamma ranges in the presence of conditioned stimuli that resemble cinematographic frames. These sequences of active frames emerge with abrupt phase resettings, followed by resynchronization and stabilization over channels, and magnified intensity. An online pattern recognizer that captures the spatial and spectral characteristics of the active frames is presented. The results of detection are confirmed via high occurrences of pragmatic information, defined by the ratio of pattern intensity to pattern stability.&lt;/p&gt;</style></abstract></record></records></xml>