Residual Motor Signal in Long-Term Human Severed Peripheral Nerves and Feasibility of Neural Signal-Controlled Artificial Limb

Jia, Xin; Koenig, Matthew A.; Zhang, Xiaowen; Zhang, Jian; Chen, Tongyi; Chen, Zhongwei

doi:10.1016/j.jhsa.2007.02.021

Cited by 59 publications

(48 citation statements)

References 36 publications

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“…Peripheral nerve function has been shown to remain intact many years after amputation [87], and successful direct nerve recording systems have been demonstrated in amputees [80,88,89], as well as cortical recording in patients with spinal cord injuries [90,91]. In addition, it may be possible to permanently link implanted electrodes through a bone-anchored conduit [22] or wirelessly [20].…”

Section: Implantable Methodsmentioning

confidence: 99%

Prosthetic Myoelectric Control Strategies: A Clinical Perspective

et al. 2014

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Control algorithms for upper limb myoelectric prostheses have been in development since the mid-1940s. Despite advances in computing power and in the performance of these algorithms, clinically available prostheses are still based on the earliest control strategies. The aim of this review paper is to detail the development, advantages and disadvantages of prosthetic control systems and to highlight areas that are current barriers for the transition from laboratory to clinical practice. Current surgical strategies and future research directions to achieve multifunctional control will also be discussed. The findings from this review suggest that regression algorithms may offer an alternative feed-forward approach to direct and pattern recognition control, while virtual rehabilitation environments and tactile feedback could improve the overall prosthetic control.

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Section: Implantable Methodsmentioning

confidence: 99%

Prosthetic Myoelectric Control Strategies: A Clinical Perspective

et al. 2014

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“…Neural interfaces that allow nerve signal detection may be used for direct control of prostheses, for example, controlling artificial hands after amputation [8]; or neuroprostheses, for example, providing slip detection in artificial control of grasp [7]. There are many types of nerve interface ranging from electrode cuffs that encircle the nerve through various types of penetrating electrodes to so-called regeneration electrodes which are sieve-like structures through which the nerve must re-grow.…”

Section: Introductionmentioning

confidence: 99%

Noise and selectivity of velocity-selective multi-electrode nerve cuffs

Donaldson

Rieger

Schüettler

et al. 2008

Med Biol Eng Comput

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Using a multi-electrode nerve-signal recording cuff and a method of signal processing described previously, activity in axons with different propagation velocities can be distinguished, and the relative amplitude of the small-fibre signals increased. This paper is, largely, an analysis of the selectivity and noise of this system though impedance measurements from an actual cuff are included. The signal processor includes narrow band-pass filters. It is shown that the selectivity and noise both increase with the centre frequencies of these filters. A convenient approach is to make the filter frequencies inversely related to the artificial time delays so that the filter 'Q's are approximately constant and the noise densities are equal for all velocity filters. Numerical calculations, using formulae for this system and for the conventional tripole, based on a fixed cuff size and tissue resistivity, find the number of action potentials per second that must pass through the cuff so that the signal power equals the noise power. For slow fibres (20 m/s), the rate is 14 times lower for the multi-electrode cuff than the tripole, a significant advantage for recording from these fibres.

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“…ENG is potentially able to provide more information than EMG to control more degrees of freedom (unless approaches such as targeted reinnervation [2] are used), at the expenses of a significantly higher computational complexity [3]. In order to control a motor neuroprosthesis, the decoding algorithm must be implemented onto an embedded signal processing platform, characterized by limited resources, low operating frequency and tight real-time bounds to fulfil.…”

mentioning

confidence: 99%

Real-Time Neural Signals Decoding onto Off-the-Shelf DSP Processors for Neuroprosthetic Applications

Pani

Barabino

Citi

et al. 2016

IEEE Trans. Neural Syst. Rehabil. Eng.

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Abstract-The control of upper limb neuroprostheses through the peripheral nervous system (PNS) can allow restoring motor functions in amputees. At present, the important aspect of the real-time implementation of neural decoding algorithms on embedded systems has been often overlooked, notwithstanding the impact that limited hardware resources have on the efficiency/effectiveness of any given algorithm. Present study is addressing the optimization of a template matching based algorithm for PNS signals decoding that is a milestone for its real-time, full implementation onto a floating-point Digital Signal Processor (DSP).The proposed optimized real-time algorithm achieves up to 96% of correct classification on real PNS signals acquired through LIFE electrodes on animals, and can correctly sort spikes of a synthetic cortical dataset with sufficiently uncorrelated spike morphologies (93% average correct classification) comparably to the results obtained with top spike sorter (94% on average on the same dataset). The power consumption enables more than 24 hours processing at the maximum load, and latency model has been derived to enable a fair performance assessment. The final embodiment demonstrates the real-time performance onto a low-power off-the-shelf DSP, opening to experiments exploiting the efferent signals to control a motor neuroprosthesis.

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Residual Motor Signal in Long-Term Human Severed Peripheral Nerves and Feasibility of Neural Signal-Controlled Artificial Limb

Cited by 59 publications

References 36 publications

Prosthetic Myoelectric Control Strategies: A Clinical Perspective

Prosthetic Myoelectric Control Strategies: A Clinical Perspective

Noise and selectivity of velocity-selective multi-electrode nerve cuffs

Real-Time Neural Signals Decoding onto Off-the-Shelf DSP Processors for Neuroprosthetic Applications

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