<?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%">Gupta, Disha</style></author><author><style face="normal" font="default" size="100%">Brangaccio, Jodi</style></author><author><style face="normal" font="default" size="100%">Hill, NJ</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Methodological optimization for eliciting robust median nerve somatosensory evoked potentials for realtime single trial applications.</style></title><secondary-title><style face="normal" font="default" size="100%">J Neural Eng</style></secondary-title><alt-title><style face="normal" font="default" size="100%">J Neural Eng</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Adult</style></keyword><keyword><style  face="normal" font="default" size="100%">Computer Systems</style></keyword><keyword><style  face="normal" font="default" size="100%">Electric Stimulation</style></keyword><keyword><style  face="normal" font="default" size="100%">Electroencephalography</style></keyword><keyword><style  face="normal" font="default" size="100%">Evoked Potentials, Somatosensory</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%">Median Nerve</style></keyword><keyword><style  face="normal" font="default" size="100%">Middle Aged</style></keyword><keyword><style  face="normal" font="default" size="100%">Reproducibility of Results</style></keyword><keyword><style  face="normal" font="default" size="100%">Spinal Cord Injuries</style></keyword><keyword><style  face="normal" font="default" size="100%">Young Adult</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2026</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2026 Jan 09</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">23</style></volume><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Single-trial measurement of median nerve somatosensory evoked potentials (SEPs) with noninvasive electroencephalography (EEG) is challenging due to low signal-to-noise ratio (SNR), limiting its use in real-time neurorehabilitation applications. We describe and evaluate methodological optimizations for eliciting reliable median nerve SEPs measurable in real time, with reduced reliance on post-processing.In twelve healthy participants, two sessions each, SEPs were assessed at three pulse widths (0.1, 0.5, 1 ms), at a low-frequency stimulation (0.5 Hz ± 10%), and at an intensity sufficient to evoke consistent and robust sensory nerve action potentials and compound muscle action potentials. The evoked potential operant conditioning system platform was used to monitor responses in real time. Feasibility was also evaluated in a participant with incomplete spinal cord injury (iSCI).SEP P50 and N70 were reliably elicited in healthy participants, and in individual with iSCI, across all tested pulse widths with minimal discomfort. N70 amplitude increased significantly with pulse width (χ2= 17.64,= 0.0001,= 0.80), while P50 amplitude remained unchanged. SNR showed a significant pulse width-dependent increase (χ2= 7.82,= 0.02,= 0.35) with improvements of 40% and 52% at 0.5 and 1 ms, respectively. N70 single-trial separability significantly improved at 1 ms (AUC of 0.83,χ2= 8.17,= 0.017), including the iSCI participant (0.84-less impaired hand, 0.79-more impaired hand). Test-retest reliability (intraclass correlation coefficient = 0.70-0.84,&lt; 0.05) was highest at 0.5 ms, indicating more consistent N70 and P50 measurements across sessions at a longer pulse width.Robust median nerve SEPs can be measured at single trials with methodological optimizations such as a longer pulse width (0.5-1 ms), low frequency (0.5 Hz), a consistent afferent excitation guided by nerve and muscle responses, and a robust EEG acquisition system. This setup can be useful for real time SEP-based brain computer interface applications for rehabilitation.&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%">Miller, V M</style></author><author><style face="normal" font="default" size="100%">Disha Gupta</style></author><author><style face="normal" font="default" size="100%">Neu, N</style></author><author><style face="normal" font="default" size="100%">Cotroneo, A</style></author><author><style face="normal" font="default" size="100%">Chadwick B. Boulay</style></author><author><style face="normal" font="default" size="100%">Seegal, R F</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Novel inter-hemispheric white matter connectivity in the BTBR mouse model of autism.</style></title><secondary-title><style face="normal" font="default" size="100%">Brain Res</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Brain Res.</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Analysis of Variance</style></keyword><keyword><style  face="normal" font="default" size="100%">Animals</style></keyword><keyword><style  face="normal" font="default" size="100%">Autistic Disorder</style></keyword><keyword><style  face="normal" font="default" size="100%">Brain</style></keyword><keyword><style  face="normal" font="default" size="100%">Corpus Callosum</style></keyword><keyword><style  face="normal" font="default" size="100%">Disease Models, Animal</style></keyword><keyword><style  face="normal" font="default" size="100%">Electroencephalography</style></keyword><keyword><style  face="normal" font="default" size="100%">Enzyme-Linked Immunosorbent Assay</style></keyword><keyword><style  face="normal" font="default" size="100%">Female</style></keyword><keyword><style  face="normal" font="default" size="100%">Functional Laterality</style></keyword><keyword><style  face="normal" font="default" size="100%">Image Processing, Computer-Assisted</style></keyword><keyword><style  face="normal" font="default" size="100%">Male</style></keyword><keyword><style  face="normal" font="default" size="100%">Mice</style></keyword><keyword><style  face="normal" font="default" size="100%">Mice, Inbred C57BL</style></keyword><keyword><style  face="normal" font="default" size="100%">Mice, Neurologic Mutants</style></keyword><keyword><style  face="normal" font="default" size="100%">Microtubule-Associated Proteins</style></keyword><keyword><style  face="normal" font="default" size="100%">Myelin Basic Protein</style></keyword><keyword><style  face="normal" font="default" size="100%">Nerve Fibers, Myelinated</style></keyword><keyword><style  face="normal" font="default" size="100%">Neuroimaging</style></keyword><keyword><style  face="normal" font="default" size="100%">Spectrum Analysis</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2013</style></year><pub-dates><date><style  face="normal" font="default" size="100%">06/2013</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/pubmed/23570707</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">1513</style></volume><pages><style face="normal" font="default" size="100%">26-33</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Alterations in the volume, density, connectivity and functional activation of white matter tracts are reported in some individuals with autism and may contribute to their abnormal behaviors. The BTBR (BTBR T+tf/J) inbred strain of mouse, is used to model facets of autism because they develop low social behaviors, stereotypical and immune changes similar to those found in people with autism. Previously, it was thought a total absence of corpus callosal interhemispheric connective tissues in the BTBR mice may underlie their abnormal behaviors. However, postnatal lesions of the corpus callosum do not precipitate social behavioral problems in other strains of mice suggesting a flaw in this theory. In this study we used digital pathological methods to compare subcortical white matter connective tracts in the BTBR strain of mice with those found in the C57Bl/6 mouse and those reported in a standardized mouse brain atlas. We report, for the first time, a novel connective subcortical interhemispheric bridge of tissue in the posterior, but not anterior, cerebrum of the BTBR mouse. These novel connective tissues are comprised of myelinated fibers, with reduced myelin basic protein levels (MBP) compared to levels in the C57Bl/6 mouse. We used electrophysiological analysis and found increased inter-hemispheric connectivity in the posterior hemispheres of the BTBR strain compared with the anterior hemispheres. The conduction velocity was slower than that reported in normal mice. This study shows there is novel abnormal interhemispheric connectivity in the BTBR strain of mice, which may contribute to their behavioral abnormalities.</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%">Christopher J James</style></author><author><style face="normal" font="default" size="100%">Disha Gupta</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Seizure prediction for epilepsy using a multi-stage phase synchrony based system.</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%">Artificial Intelligence</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%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Pattern Recognition, Automated</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></keywords><dates><year><style  face="normal" font="default" size="100%">2009</style></year><pub-dates><date><style  face="normal" font="default" size="100%">09/2009</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/pubmed/19965104</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">2009</style></volume><pages><style face="normal" font="default" size="100%">25-8</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">Seizure onset prediction in epilepsy is a challenge which is under investigation using many and varied signal processing techniques. Here we present a multi-stage phase synchrony based system that brings to bear the advantages of many techniques in each substage. The 1(st) stage of the system unmixes continuous long-term (2-4 days) multichannel scalp EEG using spatially constrained Independent Component Analysis and estimates the long term significant phase synchrony dynamics of narrowband (2-8 Hz and 8-14 Hz) seizure components. It then projects multidimensional features onto a 2-D map using Neuroscale and evaluates the probability of predictive events using Gaussian Mixture Models. We show the possibility of seizure onset prediction within a prediction window of 35-65 minutes with a sensitivity of 65-100% and specificity of 65-80% across epileptic 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%">Disha Gupta</style></author><author><style face="normal" font="default" size="100%">Christopher J James</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Narrowband vs. broadband phase synchronization analysis applied to independent components of ictal and interictal EEG.</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%">Electroencephalography</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Predictive Value of Tests</style></keyword><keyword><style  face="normal" font="default" size="100%">Seizures</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%">2007</style></year><pub-dates><date><style  face="normal" font="default" size="100%">08/2007</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/pubmed/18002842</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">2007</style></volume><pages><style face="normal" font="default" size="100%">3864-7</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">This paper presents a comparison of the use of broadband and narrow band signals for phase synchronization analysis as applied to Independent Components of ictal and interictal scalp EEG in the context of seizure onset detection and prediction. Narrow band analysis for phase synchronization is found to be better performed in the present context than the broad band signal analysis. It has been observed that the phase synchronization of Independent Components in a narrow band (particularly the Gamma band) shows a prominent trend of increasing and decreasing synchronization at seizure onset near the epileptogenic area (spatially). This information is not always found to be consistent in analysis with the raw EEG signals, which may show spurious synchronization happening due to volume conduction effects. These observations lead us to believe that tracking changes in phase synchronization of narrow band activity, on continuous data records will be of great value in the context of seizure prediction.</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%">Christopher J James</style></author><author><style face="normal" font="default" size="100%">Abásolo, Daniel</style></author><author><style face="normal" font="default" size="100%">Disha Gupta</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Space-time ICA versus Ensemble ICA for ictal EEG analysis with component differentiation via Lempel-Ziv complexity.</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%">Artificial Intelligence</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%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Pattern Recognition, Automated</style></keyword><keyword><style  face="normal" font="default" size="100%">Principal Component Analysis</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></keywords><dates><year><style  face="normal" font="default" size="100%">2007</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2007</style></date></pub-dates></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/pubmed/18003250</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">08/2007</style></volume><pages><style face="normal" font="default" size="100%">5473-6</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">In this proof-of-principle study we analyzed intracranial electroencephalogram recordings in patients with intractable focal epilepsy. We contrast two implementations of Independent Component Analysis (ICA) - Ensemble (or spatial) ICA (E-ICA) and Space-Time ICA (ST-ICA) in separating out the ictal components underlying the measurements. In each case we assess the outputs of the ICA algorithms by means of a non-linear method known as the Lempel-Ziv (LZ) complexity. LZ complexity quantifies the complexity of a time series and is well suited to the analysis of non-stationary biomedical signals of short length. Our results show that for small numbers of intracranial recordings, standard E-ICA results in marginal improvements in the separation as measured by the LZ complexity changes. ST-ICA using just 2 recording channels both near and far from the epileptic focus result in more distinct ictal components--although at this stage there is a subjective element to the separation process for ST-ICA. Our results are promising showing that it is possible to extract meaningful information from just 2 recording electrodes through ST-ICA, even if they are not directly over the seizure focus. This work is being further expanded for seizure onset analysis.</style></abstract></record></records></xml>