Selective Recording of Electroneurograms from the Sciatic Nerve of a Dog with Multi-Electrode Spiral Cuffs.

Rozman, Janez; Zorko, B.; Bunc, M.

doi:10.2170/jjphysiol.50.509

Cited by 34 publications

(30 citation statements)

References 20 publications

(19 reference statements)

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“…Although large sensory fibers exist, they may not fire to the degree of synchrony as these motor fibers. A study specifically measuring proprioception in canines [35], which consists of large sensory fibers which intuitively should fire synchronously, reported modest ENG (<10uV PP) despite utilizing large ankle excursions (~100 degrees). The recorded activity in this study was larger (~20–30uV PP) with smaller ankle excursions, supporting the hypothesis that the recorded activity is predominantly motor.…”

Section: Discussionmentioning

confidence: 99%

Model-based Bayesian signal extraction algorithm for peripheral nerves

et al. 2017

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Objective Multi-channel cuff electrodes have recently been investigated for extracting fascicular-level motor commands from mixed neural recordings. Such signals could provide volitional, intuitive control over a robotic prosthesis for amputee patients. Recent work has demonstrated success in extracting these signals in acute and chronic preparations using spatial filtering techniques. These extracted signals, however, had low signal-to-noise ratios and thus limited their utility to binary classification. In this work a new algorithm is proposed which combines previous source localization approaches to create a model based method which operates in real time. Approach To validate this algorithm, a saline benchtop setup was created to allow the precise placement of artificial sources within a cuff and interference sources outside the cuff. The artificial source was taken from five seconds of chronic neural activity to replicate realistic recordings. The proposed algorithm, Hybrid Bayesian Signal Extraction (HBSE), is then compared to previous algorithms, beamforming and a Bayesian spatial filtering method, on this test data. An example chronic neural recording is also analyzed with all three algorithms. Main Results The proposed algorithm improved the signal to noise and signal to interference ratio of extracted test signals two to three fold, as well as increased the correlation coefficient between the original and recovered signals by 10–20%. These improvements translated to the chronic recording example and increased the calculated bit rate between the recovered signals and the recorded motor activity. Significance HBSE significantly outperforms previous algorithms in extracting realistic neural signals, even in the presence of external noise sources. These results demonstrate the feasibility of extracting dynamic motor signals from a multi-fascicled intact nerve trunk, which in turn could extract motor command signals from an amputee for the end goal of controlling a prosthetic limb.

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Section: Discussionmentioning

confidence: 99%

Model-based Bayesian signal extraction algorithm for peripheral nerves

et al. 2017

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“…Indeed, it has been reported that invasive methods to read the nerve signal by intruding the protective perineurium may incur damage to the nerve and increase the risk of infections ([11]), and therefore non-penetrating methods have an appeal. On the other hand, identifying the neuronal activity of different fascicles within a nerve from measurements on the surface of the nerve poses a remarkable challenge from the point of view of both data collection and interpretation ([9, 10, 14, 19, 20]).…”

Section: Introductionmentioning

confidence: 99%

Hierarchical beamformer and cross-talk reduction in electroneurography

Calvetti¹,

Wodlinger²,

Durand³

et al. 2011

J. Neural Eng.

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Electroneurography (ENG) is a method of recording neural activity within nerves. Using nerve electrodes with multiple contacts the activation patterns of individual neuronal fascicles can be estimated by measuring the surface voltages induced by the intraneural activity. The information about neuronal activation can be used for functional electric stimulation (FES) of patients suffering of spinal chord injury, or to control a robotic prosthetic limb of an amputee. However, the ENG signal estimation is a severely ill-posed inverse problem due to uncertainties in the model, low resolution due to limitations of the data, geometric constraints, and the diffculty to separate the signal from biological and exogenous noise. In this article, a reduced computational model for the forward problem is proposed, and the ENG problem is addressed by using beamformer techniques. It is shown that the beamformer algorithm can be interpreted as a version of the classical Backus-Gilbert algorithm. Furthermore, we show that using a hierarchical statistical model, it is possible to develop an adaptive beamformer algorithm that estimates directly the source variances rather than the voltage source itself. The advantage of this new algorithm, e.g., over a traditional adaptive beamformer algorithms is that it allows a very stable noise reduction by averaging over a time window. In addition, a new projection technique for separating sources and reducing cross-talk between different fascicle signals is proposed. The algorithms are tested on a computer model of realistic nerve geometry and time series signals.

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“…The electrodes on the nerve branches can then be removed so that only the electrodes on the nerve trunk are chronically implanted. However, this has been challenging to perform as most peripheral neural interfaces, such as penetrating electrodes,36, 37, 38, 39 regenerative electrodes,40, 41, 42 and extra‐neural electrodes43, 44 were designed to interface with relatively large nerves (with diameters larger than 500 μm). For example, penetrating electrodes such as intrafascicular electrodes typically require the use of a needle to penetrate the nerve, but these needles are too bulky to pass through the very small nerve branches.…”

mentioning

confidence: 99%

Mapping of Small Nerve Trunks and Branches Using Adaptive Flexible Electrodes

et al. 2016

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Selective stimulation is delivered to the sciatic nerve using different paris of contacts on a split‐ring electrode, while simulatneous recordings are acquired by the neural ribbon electrodes on three different branches. Two hook electrodes are also implanted in the muscle to monitor the activated muscle responses. It shows that the high precision implantation of electrodes, increases the efficacy and reduces the incidence of side effects.

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Selective Recording of Electroneurograms from the Sciatic Nerve of a Dog with Multi-Electrode Spiral Cuffs.

Cited by 34 publications

References 20 publications

Model-based Bayesian signal extraction algorithm for peripheral nerves

Model-based Bayesian signal extraction algorithm for peripheral nerves

Hierarchical beamformer and cross-talk reduction in electroneurography

Mapping of Small Nerve Trunks and Branches Using Adaptive Flexible Electrodes

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