Skin contact force information in sensory nerve signals recorded by implanted cuff electrodes

Haugland, Morten Kristian; Hoffer, J. A.; Sinkjær, Thomas

doi:10.1109/86.296346

Cited by 110 publications

(61 citation statements)

References 31 publications

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“…Indeed, recording the responses of human or monkey afferents is technically challenging and only yields responses from a single fiber at a time. Even multielectrode arrays only yield responses from a sparse sample of afferents (53) or the aggregate activity of a large number of fibers (54,55). The model allows us to simulate the responses of entire populations of afferents to arbitrarily complex stimuli.…”

Section: Discussionmentioning

confidence: 99%

Simulating tactile signals from the whole hand with millisecond precision

Saal

Delhaye

Rayhaun

et al. 2017

Proc. Natl. Acad. Sci. U.S.A.

212

251

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When we grasp and manipulate an object, populations of tactile nerve fibers become activated and convey information about the shape, size, and texture of the object and its motion across the skin. The response properties of tactile fibers have been extensively characterized in single-unit recordings, yielding important insights into how individual fibers encode tactile information. A recurring finding in this extensive body of work is that stimulus information is distributed over many fibers. However, our understanding of population-level representations remains primitive. To fill this gap, we have developed a model to simulate the responses of all tactile fibers innervating the glabrous skin of the hand to any spatiotemporal stimulus applied to the skin. The model first reconstructs the stresses experienced by mechanoreceptors when the skin is deformed and then simulates the spiking response that would be produced in the nerve fiber innervating that receptor. By simulating skin deformations across the palmar surface of the hand and tiling it with receptors at their known densities, we reconstruct the responses of entire populations of nerve fibers. We show that the simulated responses closely match their measured counterparts, down to the precise timing of the evoked spikes, across a wide variety of experimental conditions sampled from the literature. We then conduct three virtual experiments to illustrate how the simulation can provide powerful insights into population coding in touch. Finally, we discuss how the model provides a means to establish naturalistic artificial touch in bionic hands.mechanoreceptor | tactile afferent | somatosensory periphery | skin mechanics | computational model T he human hand is endowed with thousands of mechanoreceptors of different types distributed across the skin, each innervated by one or more large myelinated nerve fibers (1). These fibers convey detailed information about contact events and provide us with an exquisite sensitivity to the form and surface properties of grasped objects (2, 3). During object manipulation and tactile exploration, the glabrous skin of the hand undergoes complex spatiotemporal mechanical deformations, which in turn, drive very precise spiking responses in individual afferents. Coarse object features, such as edges and corners, are reflected in spatial patterns of activation in slowly adapting type I (SA1) and rapidly adapting (RA) fibers, which are densely packed in the fingertip (3, 4). At the same time, interactions with objects and surfaces elicit high-frequency, low-amplitude surface waves that propagate across the skin of the finger and palm and excite vibration-sensitive Pacinian (PC) afferents all over the hand (5-8).Recording the activity of tactile nerve fibers in monkeys or humans is technically difficult, is slow, and generally yields responses from a single unit at a time (9, 10). Although such recordings have yielded powerful insights into the neural basis of touch, they provide a limited window into the information that the hand c...

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Section: Discussionmentioning

confidence: 99%

Simulating tactile signals from the whole hand with millisecond precision

Saal

Delhaye

Rayhaun

et al. 2017

Proc. Natl. Acad. Sci. U.S.A.

212

251

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“…The use of naturally-occurring (afferent) neural signals (ENG) to provide sensory feedback to artificial devices is a major current challenge in neuroprosthetics research [1,2]. There are many applications for this technology and a primary requirement is stable responses from chronically implanted electrodes.…”

Section: Introductionmentioning

confidence: 99%

A low-noise front-end for multiplexed ENG recording using CMOS technology

Taylor

Rieger

2011

Analog Integr Circ Sig Process

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Multi-electrode cuffs have been proposed as a means for the parallel recording of naturally-occurring neural signals. Compared to a conventional tripole cuff providing a single channel signal, additional information about the velocity and direction of nerve signals can be extracted from the multi-electrode recordings. Moreover, interference suppression is improved and overall power consumption reduced, as the analysis shows. In this paper a new recording system is proposed, each channel of which consists of a low-noise preamplifier employing lateral bipolar transistors to provide an impedance match to the tissue and a path to ground for the switching currents of a single high-gain amplifier, which is multiplexed between the channels. The proposed system provides low-noise, low-power operation and practically identical channel gains and is suitable for integration in a larger CMOSbased system.

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“…The disorder can be corrected by electrically stimulating nerves that innervate the dorsiflexor muscles of the foot joint to produce toe lift. Sensing natural neural signals to control assistive devices was suggested a number of years ago by Stein [1], and proved useful to supply reproducible information for feedback purposes in FES systems [2], [3]. Our particular interest lies in the correction of foot drop based on cuff whole nerve recordings as investigated previously e.g.…”

Section: Introductionmentioning

confidence: 99%

Chronic cuff electrode recordings from walking Göttingen mini-pigs

Andersen

Munch

Jensen

et al. 2011

2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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We present data from cuff electrode recordings from a mixed sensory-/motor nerve as expressed during walking in chronically implanted Göttingen mini-pigs. Our results show that it is possible to filter out residual electromyographic interference and that the energy content of the resulting electroneurographic (ENG) signals modulate clearly with gait. The approach may be used to detect heel strike from cuff electrode measurements to control the timing of stimulation in implantable foot drop correction systems.

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Skin contact force information in sensory nerve signals recorded by implanted cuff electrodes

Cited by 110 publications

References 31 publications

Simulating tactile signals from the whole hand with millisecond precision

Simulating tactile signals from the whole hand with millisecond precision

A low-noise front-end for multiplexed ENG recording using CMOS technology

Chronic cuff electrode recordings from walking Göttingen mini-pigs

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