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Neural Systems and Rehabilitation Engineering, IEEE Transactions on

Issue 2 • Date March 2012

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Displaying Results 1 - 17 of 17
  • [Front cover]

    Publication Year: 2012 , Page(s): C1
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  • IEEE Transactions on Neural Systems and Rehabilitation Engineering publication information

    Publication Year: 2012 , Page(s): C2
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  • Guest Editorial Special Issue of DARPA NEST Proceedings

    Publication Year: 2012 , Page(s): 113 - 116
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  • Mammalian Muscle Model for Predicting Force and Energetics During Physiological Behaviors

    Publication Year: 2012 , Page(s): 117 - 133
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1417 KB) |  | HTML iconHTML  

    Muscles convert metabolic energy into mechanical work. A computational model of muscle would ideally compute both effects efficiently for the entire range of muscle activation and kinematic conditions (force and length). We have extended the original Virtual Muscle algorithm (Cheng , 2000) to predict energy consumption for both slow- and fast-twitch muscle fiber types, partitioned according to the activation process (Ea), cross-bridge cycling (Exb) and ATP/PCr recovery (Erecovery). Because the terms of these functions correspond to identifiable physiological processes, their coefficients can be estimated directly from the types of experiments that are usually performed and extrapolated to dynamic conditions of natural motor behaviors. We also implemented a new approach to lumped modeling of the gradually recruited and frequency modulated motor units comprising each fiber type, which greatly reduced computational time. The emergent behavior of the model has significant implications for studies of optimal motor control and development of rehabilitation strategies because its trends were quite different from traditional estimates of energy (e.g., activation, force, stress, work, etc.). The model system was scaled to represent three different human experimental paradigms in which muscle heat was measured during voluntary exercise; predicted and observed energy rate agreed well both qualitatively and quantitatively. View full abstract»

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  • Real-Time Animation Software for Customized Training to Use Motor Prosthetic Systems

    Publication Year: 2012 , Page(s): 134 - 142
    Cited by:  Papers (4)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1150 KB) |  | HTML iconHTML  

    Research on control of human movement and development of tools for restoration and rehabilitation of movement after spinal cord injury and amputation can benefit greatly from software tools for creating precisely timed animation sequences of human movement. Despite their ability to create sophisticated animation and high quality rendering, existing animation software are not adapted for application to neural prostheses and rehabilitation of human movement. We have developed a software tool known as MSMS (MusculoSkeletal Modeling Software) that can be used to develop models of human or prosthetic limbs and the objects with which they interact and to animate their movement using motion data from a variety of offline and online sources. The motion data can be read from a motion file containing synthesized motion data or recordings from a motion capture system. Alternatively, motion data can be streamed online from a real-time motion capture system, a physics-based simulation program, or any program that can produce real-time motion data. Further, animation sequences of daily life activities can be constructed using the intuitive user interface of Microsoft's PowerPoint software. The latter allows expert and nonexpert users alike to assemble primitive movements into a complex motion sequence with precise timing by simply arranging the order of the slides and editing their properties in PowerPoint. The resulting motion sequence can be played back in an open-loop manner for demonstration and training or in closed-loop virtual reality environments where the timing and speed of animation depends on user inputs. These versatile animation utilities can be used in any application that requires precisely timed animations but they are particularly suited for research and rehabilitation of movement disorders. MSMS's modeling and animation tools are routinely used in a number of research laboratories around the country to study the control of movement and to develop and test ne- ral prostheses for patients with paralysis or amputations. View full abstract»

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  • Connectivity Analysis as a Novel Approach to Motor Decoding for Prosthesis Control

    Publication Year: 2012 , Page(s): 143 - 152
    Cited by:  Papers (4)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1628 KB) |  | HTML iconHTML  

    The use of neural signals for prosthesis control is an emerging frontier of research to restore lost function to amputees and the paralyzed. Electrocorticography (ECoG) brain-machine interfaces (BMI) are an alternative to EEG and neural spiking and local field potential BMI approaches. Conventional ECoG BMIs rely on spectral analysis at specific electrode sites to extract signals for controlling prostheses. We compare traditional features with information about the connectivity of an ECoG electrode network. We use time-varying dynamic Bayesian networks (TV-DBN) to determine connectivity between ECoG channels in humans during a motor task. We show that, on average, TV-DBN connectivity decreases from baseline preceding movement and then becomes negative, indicating an alteration in the phase relationship between electrode pairs. In some subjects, this change occurs preceding and during movement, before changes in low or high frequency power. We tested TV-DBN output in a hand kinematic decoder and obtained an average correlation coefficient (r2) between actual and predicted joint angle of 0.40, and as high as 0.66 in one subject. This result compares favorably with spectral feature decoders, for which the average correlation coefficient was 0.13. This work introduces a new feature set based on connectivity and demonstrates its potential to improve ECoG BMI accuracy. View full abstract»

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  • Electrostimulation as a Prosthesis for Repair of Information Flow in a Computer Model of Neocortex

    Publication Year: 2012 , Page(s): 153 - 160
    Cited by:  Papers (3)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1244 KB) |  | HTML iconHTML  

    Damage to a cortical area reduces not only information transmitted to other cortical areas, but also activation of these areas. This phenomenon, whereby the dynamics of a follower area are dramatically altered, is typically manifested as a marked reduction in activity. Ideally, neuroprosthetic stimulation would replace both information and activation. However, replacement of activation alone may be valuable as a means of restoring dynamics and information processing of other signals in this multiplexing system. We used neuroprosthetic stimulation in a computer model of the cortex to repair activation dynamics, using a simple repetitive stimulation to replace the more complex, naturalistic stimulation that had been removed. We found that we were able to restore activity in terms of neuronal firing rates. Additionally, we were able to restore information processing, measured as a restoration of causality between an experimentally recorded signal fed into the in silico brain and a cortical output. These results indicate that even simple neuroprosthetics that do not restore lost information may nonetheless be effective in improving the functionality of surrounding areas of cortex. View full abstract»

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  • An Electric Field Model for Prediction of Somatosensory (S1) Cortical Field Potentials Induced by Ventral Posterior Lateral (VPL) Thalamic Microstimulation

    Publication Year: 2012 , Page(s): 161 - 169
    Cited by:  Papers (1)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1274 KB) |  | HTML iconHTML  

    Microstimulation (MiSt) is used experimentally and clinically to activate localized populations of neural elements. However, it is difficult to predict-and subsequently control-neural responses to simultaneous current injection through multiple electrodes in an array. This is due to the unknown locations of neuronal elements in the extracellular medium that are excited by the superposition of multiple parallel current sources. We, therefore, propose a model that maps the computed electric field in the 3-D space surrounding the stimulating electrodes in one brain region to the local field potential (LFP) fluctuations evoked in a downstream region. Our model is trained with the recorded LFP waveforms in the primary somatosensory cortex (S1) resulting from MiSt applied in multiple electrode configurations in the ventral posterolateral nucleus (VPL) of the quiet awake rat. We then predict the cortical responses to MiSt in “novel” electrode configurations, a result that suggests that this technique could aid in the design of spatially optimized MiSt patterns through a multielectrode array. View full abstract»

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  • A Programmable Laboratory Testbed in Support of Evaluation of Functional Brain Activation and Connectivity

    Publication Year: 2012 , Page(s): 170 - 183
    Cited by:  Papers (3)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2863 KB) |  | HTML iconHTML  

    An important determinant of the value of quantitative neuroimaging studies is the reliability of the derived information, which is a function of the data collection conditions. Near infrared spectroscopy (NIRS) and electroencelphalography are independent sensing domains that are well suited to explore principal elements of the brain's response to neuroactivation, and whose integration supports development of compact, even wearable, systems suitable for use in open environments. In an effort to maximize the translatability and utility of such resources, we have established an experimental laboratory testbed that supports measures and analysis of simulated macroscopic bioelectric and hemodynamic responses of the brain. Principal elements of the testbed include 1) a programmable anthropomorphic head phantom containing a multisignal source array embedded within a matrix that approximates the background optical and bioelectric properties of the brain, 2) integrated translatable headgear that support multimodal studies, and 3) an integrated data analysis environment that supports anatomically based mapping of experiment-derived measures that are directly and not directly observable. Here, we present a description of system components and fabrication, an overview of the analysis environment, and findings from a representative study that document the ability to experimentally validate effective connectivity models based on NIRS tomography. View full abstract»

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  • A Nonlinear Model for Hippocampal Cognitive Prosthesis: Memory Facilitation by Hippocampal Ensemble Stimulation

    Publication Year: 2012 , Page(s): 184 - 197
    Cited by:  Papers (5)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2843 KB) |  | HTML iconHTML  

    Collaborative investigations have characterized how multineuron hippocampal ensembles encode memory necessary for subsequent successful performance by rodents in a delayed nonmatch to sample (DNMS) task and utilized that information to provide the basis for a memory prosthesis to enhance performance. By employing a unique nonlinear dynamic multi-input/multi-output (MIMO) model, developed and adapted to hippocampal neural ensemble firing patterns derived from simultaneous recorded CA1 and CA3 activity, it was possible to extract information encoded in the sample phase necessary for successful performance in the nonmatch phase of the task. The extension of this MIMO model to online delivery of electrical stimulation delivered to the same recording loci that mimicked successful CA1 firing patterns, provided the means to increase levels of performance on a trial-by-trial basis. Inclusion of several control procedures provides evidence for the specificity of effective MIMO model generated patterns of electrical stimulation. Increased utility of the MIMO model as a prosthesis device was exhibited by the demonstration of cumulative increases in DNMS task performance with repeated MIMO stimulation over many sessions on both stimulation and nonstimulation trials, suggesting overall system modification with continued exposure. Results reported here are compatible with and extend prior demonstrations and further support the candidacy of the MIMO model as an effective cortical prosthesis. View full abstract»

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  • A Hippocampal Cognitive Prosthesis: Multi-Input, Multi-Output Nonlinear Modeling and VLSI Implementation

    Publication Year: 2012 , Page(s): 198 - 211
    Cited by:  Papers (5)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (2475 KB) |  | HTML iconHTML  

    This paper describes the development of a cognitive prosthesis designed to restore the ability to form new long-term memories typically lost after damage to the hippocampus. The animal model used is delayed nonmatch-to-sample (DNMS) behavior in the rat, and the “core” of the prosthesis is a biomimetic multi-input/multi-output (MIMO) nonlinear model that provides the capability for predicting spatio-temporal spike train output of hippocampus (CA1) based on spatio-temporal spike train inputs recorded presynaptically to CA1 (e.g., CA3). We demonstrate the capability of the MIMO model for highly accurate predictions of CA1 coded memories that can be made on a single-trial basis and in real-time. When hippocampal CA1 function is blocked and long-term memory formation is lost, successful DNMS behavior also is abolished. However, when MIMO model predictions are used to reinstate CA1 memory-related activity by driving spatio-temporal electrical stimulation of hippocampal output to mimic the patterns of activity observed in control conditions, successful DNMS behavior is restored. We also outline the design in very-large-scale integration for a hardware implementation of a 16-input, 16-output MIMO model, along with spike sorting, amplification, and other functions necessary for a total system, when coupled together with electrode arrays to record extracellularly from populations of hippocampal neurons, that can serve as a cognitive prosthesis in behaving animals. View full abstract»

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  • Decoding Intra-Limb and Inter-Limb Kinematics During Treadmill Walking From Scalp Electroencephalographic (EEG) Signals

    Publication Year: 2012 , Page(s): 212 - 219
    Cited by:  Papers (5)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (983 KB) |  | HTML iconHTML  

    Brain-machine interface (BMI) research has largely been focused on the upper limb. Although restoration of gait function has been a long-standing focus of rehabilitation research, surprisingly very little has been done to decode the cortical neural networks involved in the guidance and control of bipedal locomotion. A notable exception is the work by Nicolelis' group at Duke University that decoded gait kinematics from chronic recordings from ensembles of neurons in primary sensorimotor areas in rhesus monkeys. Recently, we showed that gait kinematics from the ankle, knee, and hip joints during human treadmill walking can be inferred from the electroencephalogram (EEG) with decoding accuracies comparable to those using intracortical recordings. Here we show that both intra- and inter-limb kinematics from human treadmill walking can be achieved with high accuracy from as few as 12 electrodes using scalp EEG. Interestingly, forward and backward predictors from EEG signals lagging or leading the kinematics, respectively, showed different spatial distributions suggesting distinct neural networks for feedforward and feedback control of gait. Of interest is that average decoding accuracy across subjects and decoding modes was ~ 0.68±0.08, supporting the feasibility of EEG-based BMI systems for restoration of walking in patients with paralysis. View full abstract»

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  • Normal Molecular Repair Mechanisms in Regenerative Peripheral Nerve Interfaces Allow Recording of Early Spike Activity Despite Immature Myelination

    Publication Year: 2012 , Page(s): 220 - 227
    Cited by:  Papers (2)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1203 KB) |  | HTML iconHTML  

    Clinical use of neurally controlled prosthetics has advanced in recent years, but limitations still remain, including lacking fine motor control and sensory feedback. Indwelling multi-electrode arrays, cuff electrodes, and regenerative sieve electrodes have been reported to serve as peripheral neural interfaces, though long-term stability of the nerve-electrode interface has remained a formidable challenge. We recently developed a regenerative multi-electrode interface (REMI) that is able to record neural activity as early as seven days post-implantation. While this activity might represent normal neural depolarization during axonal regrowth, it can also be the result of altered nerve regeneration around the REMI. This study evaluated high-throughput expression levels of 84 genes involved in nerve injury and repair, and the histological changes that occur in parallel to this early neural activity. Animals exhibiting spike activity increased from 29% to 57% from 7 to 14 days following REMI implantation with a corresponding increase in firing rate of 113%. Two weeks after implantation, numbers of neurofilament-positive axons in the control and REMI implanted nerves were comparable, and in both cases the number of myelinated axons was low. During this time, expression levels of genes related to nerve injury and repair were similar in regenerated nerves, both in the presence or absence of the electrode array. Together, these results indicate that the early neural activity is intrinsic to the regenerating axons, and not induced by the REMI neurointerface. View full abstract»

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  • Dry and Noncontact EEG Sensors for Mobile Brain–Computer Interfaces

    Publication Year: 2012 , Page(s): 228 - 235
    Cited by:  Papers (11)
    Save to Project icon | Request Permissions | Click to expandQuick Abstract | PDF file iconPDF (1480 KB) |  | HTML iconHTML  

    Dry and noncontact electroencephalographic (EEG) electrodes, which do not require gel or even direct scalp coupling, have been considered as an enabler of practical, real-world, brain-computer interface (BCI) platforms. This study compares wet electrodes to dry and through hair, noncontact electrodes within a steady state visual evoked potential (SSVEP) BCI paradigm. The construction of a dry contact electrode, featuring fingered contact posts and active buffering circuitry is presented. Additionally, the development of a new, noncontact, capacitive electrode that utilizes a custom integrated, high-impedance analog front-end is introduced. Offline tests on 10 subjects characterize the signal quality from the different electrodes and demonstrate that acquisition of small amplitude, SSVEP signals is possible, even through hair using the new integrated noncontact sensor. Online BCI experiments demonstrate that the information transfer rate (ITR) with the dry electrodes is comparable to that of wet electrodes, completely without the need for gel or other conductive media. In addition, data from the noncontact electrode, operating on the top of hair, show a maximum ITR in excess of 19 bits/min at 100% accuracy (versus 29.2 bits/min for wet electrodes and 34.4 bits/min for dry electrodes), a level that has never been demonstrated before. The results of these experiments show that both dry and noncontact electrodes, with further development, may become a viable tool for both future mobile BCI and general EEG applications. View full abstract»

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    Publication Year: 2012 , Page(s): 236
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  • IEEE Transactions on Neural Systems and Rehabilitation Engineering information for authors

    Publication Year: 2012 , Page(s): C3
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  • Table of contents

    Publication Year: 2012 , Page(s): C4
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Aims & Scope

IEEE Transactions on Neural Systems and Rehabilitation Engineering focuses on the rehabilitative and neural aspects of biomedical engineering.

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Meet Our Editors

Editor-in-Chief
Paul Sajda
Columbia University