02457nas a2200349 4500008004100000022001400041245012600055210006900181260001200250300001200262490000700274520141700281653001501698653001001713653001801723653002601741653002101767653001301788653002401801653000901825653001101834653001801845653001901863653003101882653002301913653004101936100002001977700002301997700002002020700001902040856004802059 2009 eng d a1879-278200aMapping broadband electrocorticographic recordings to two-dimensional hand trajectories in humans Motor control features.0 aMapping broadband electrocorticographic recordings to twodimensi c11/2009 a1257-700 v223 a
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 (<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.
10aAlgorithms10aBrain10aBrain Mapping10aElectrodes, Implanted10aElectrodiagnosis10aEpilepsy10aFeasibility Studies10aHand10aHumans10aLinear Models10aMotor Activity10aNeural Networks (Computer)10aNonlinear Dynamics10aSignal Processing, Computer-Assisted1 aGunduz, Aysegul1 aSanchez, Justin, C1 aCarney, Paul, R1 aPrincipe, Jose uhttp://www.ncbi.nlm.nih.gov/pubmed/1964798102228nas a2200289 4500008004100000022001400041245012100055210006900176260001200245300001100257490000900268520131200277653001501589653001401604653003301618653002701651653001301678653002901691653001701720653003101737653003201768653002801800100002001828700002301848700001901871856004801890 2008 eng d a1557-170X00aElectrocorticographic interictal spike removal via denoising source separation for improved neuroprosthesis control.0 aElectrocorticographic interictal spike removal via denoising sou c08/2008 a5224-70 v20083 aElectrocorticographic (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 data 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.
10aAlgorithms10aArtifacts10aDiagnosis, Computer-Assisted10aElectroencephalography10aEpilepsy10aEvoked Potentials, Motor10aMotor Cortex10aReproducibility of Results10aSensitivity and Specificity10aUser-Computer Interface1 aGunduz, Aysegul1 aSanchez, Justin, C1 aPrincipe, Jose uhttp://www.ncbi.nlm.nih.gov/pubmed/1916389505892nas a2200361 4500008004100000022001400041245010100055210006900156260001200225300001000237490000800247520480000255653001505055653002805070653001805098653002005116653002705136653002405163653001105187653000905198653001105207653003105218653003205249653002805281653004105309653002205350653002805372100002305400700002005423700002005443700001905463856004805482 2008 eng d a0165-027000aExtraction and localization of mesoscopic motor control signals for human ECoG neuroprosthetics.0 aExtraction and localization of mesoscopic motor control signals c01/2008 a63-810 v1673 aElectrocorticogram (ECoG) recordings for neuroprosthetics provide a mesoscopic level of abstraction of brain function between microwire single neuron recordings and the electroencephalogram (EEG). Single-trial ECoG neural interfaces require appropriate feature extraction and signal processing methods to identify and model in real-time signatures of motor events in spontaneous brain activity. Here, we develop the clinical experimental paradigm and analysis tools to record broadband (1Hz to 6kHz) ECoG 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 ECoG voltages to determine if they provide viable control inputs for ECoG neural interfaces. This study seeks to explore preprocessing modalities that emphasize amplitude modulation across frequencies and channels in the ECoG above the level of noisy background fluctuations in order to derive the commands for complex, continuous control tasks. Preliminary experiments show that it is possible to derive online predictive models and spatially localize the generation of commands in the cortex for motor tasks using amplitude modulated ECoG.
10aAdolescent10aBiofeedback, Psychology10aBrain Mapping10aCerebral Cortex10aElectroencephalography10aEpilepsies, Partial10aFemale10aHand10aHumans10aMagnetic Resonance Imaging10aPhysical Therapy Modalities10aPsychomotor Performance10aSignal Processing, Computer-Assisted10aSpectrum Analysis10aUser-Computer Interface1 aSanchez, Justin, C1 aGunduz, Aysegul1 aCarney, Paul, R1 aPrincipe, Jose uhttp://www.ncbi.nlm.nih.gov/pubmed/1758250702708nas a2200373 4500008004100000022001400041245010900055210006900164260001200233300001100245490000600256520162400262653002201886653001201908653001001920653001801930653002501948653001501973653002901988653000902017653002402026653001702050653001202067653000902079653002502088653004102113653002602154100001602180700002302196700001902219700002802238700002002266856004802286 2006 eng d a1557-170X00aAnalysis of the correlation between local field potentials and neuronal firing rate in the motor cortex.0 aAnalysis of the correlation between local field potentials and n c09/2006 a6185-80 v13 aNeuronal firing rate has been the signal of choice for invasive motor brain machine interfaces (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.
10aAction Potentials10aAnimals10aBrain10aBrain Mapping10aElectric Stimulation10aElectrodes10aEvoked Potentials, Motor10aMale10aModels, Statistical10aMotor Cortex10aNeurons10aRats10aRats, Sprague-Dawley10aSignal Processing, Computer-Assisted10aSynaptic Transmission1 aWang, Yiwen1 aSanchez, Justin, C1 aPrincipe, Jose1 aMitzelfelt, Jeremiah, D1 aGunduz, Aysegul uhttp://www.ncbi.nlm.nih.gov/pubmed/17946745