Tutorial: A guide to techniques for analysing recordings from the peripheral nervous system

The nervous system, through a combination of conscious and automatic processes, enables the regulation of the body and its interactions with the environment. The peripheral nervous system is an excellent target for technologies that seek to modulate, restore or enhance these abilities as it carries sensory and motor information that most directly relates to a target organ or function. However, many applications require a combination of both an effective peripheral nerve interface and effective signal processing techniques to provide selective and stable recordings. While there are many reviews on the design of peripheral nerve interfaces, reviews of data analysis techniques and translational considerations are limited. Thus, this tutorial aims to support new and existing researchers in the understanding of the general guiding principles, and introduces a taxonomy for electrode configurations, techniques and translational models to consider

Are You "Tilting at Windmills" or Undertaking a Valid Clinical Trial?

In this review, several aspects surrounding the choice of a therapeutic intervention and the conduct of clinical trials are discussed. Some of the background for why human studies have evolved to their current state is also included. Specifically, the following questions have been addressed: 1) What criteria should be used to determine whether a scientific discovery or invention is worthy of translation to human application? 2) What recent scientific advance warrants a deeper understanding of clinical trials by everyone? 3) What are the different types and phases of a clinical trial? 4) What characteristics of a human disorder should be noted, tracked, or stratified for a clinical trial and what inclusion /exclusion criteria are important to enrolling appropriate trial subjects? 5) What are the different study designs that can be used in a clinical trial program? 6) What confounding factors can alter the accurate interpretation of clinical trial outcomes? 7) What are the success rates of clinical trials and what can we learn from previous clinical trials? 8) What are the essential principles for the conduct of valid clinical trials

Bioelectric Source Localization in Peripheral Nerves

Currently there does not exist a type of peripheral nerve interface that adequately combines spatial selectivity, spatial coverage and low invasiveness. In order to address this lack, we investigated the application of bioelectric source localization algorithms, adapted from electroencephalography/magnetoencephalography, to recordings from a 56-contact “matrix” nerve cuff electrode. If successful, this strategy would enable us to improve current neuroprostheses and conduct more detailed investigations of neural control systems. Using forward field similarities, we first developed a method to reduce the number of unnecessary variables in the inverse problem, and in doing so obtained an upper bound on the spatial resolution. Next, a simulation study of the peripheral nerve source localization problem revealed that the method is unlikely to work unless noise is very low and a very accurate model of the nerve is available. Under more realistic conditions, the method had localization errors in the 140 μm-180 μm range, high numbers of spurious pathways, and low resolution. On the other hand, the simulations also showed that imposing physiologically meaningful constraints on the solution can reduce the number of spurious pathways. Both the influence of the constraints and the importance of the model accuracy were validated experimentally using recordings from rat sciatic nerves. Unfortunately, neither idealized models nor models based on nerve sample cross-sections were sufficiently accurate to allow reliable identification of the branches stimulated during the experiments. To overcome this problem, an experimental leadfield was constructed using training data, thereby eliminating the dependence on anatomical models. This new strategy was successful in identifying single-branch cases, but not multi-branches ones. Lastly, an examination of the information contained in the matrix cuff recordings was performed in comparison to a single-ring configuration of contacts. The matrix cuff was able to achieve better fascicle discrimination due to its ability to select among the most informative locations around the nerve. These findings suggest that nerve cuff-based neuroprosthetic applications would benefit from implanting devices with a large number of contacts, then performing a contact selection procedure. Conditions that must be met before source localization approaches can be applied in practice to peripheral nerves were also discussed.Ph

Interaction Detection in Egocentric Video: Toward a Novel Outcome Measure for Upper Extremity Function

In order to develop effective interventions for restoring upper extremity function after cervical spinal cord injury, tools are needed to accurately measure hand function throughout the rehabilitation process. However, there is currently no suitable method to collect information about hand function in the community, when patients are not under direct observation of a clinician. We propose a wearable system that can monitor functional hand use using computer vision techniques applied to egocentric camera videos. To this end, in this study we demonstrate the feasibility of detecting interactions of the hand with objects in the environment from egocentric video. The system consists of a preprocessing step where the hand is segmented out from the background. The algorithm then extracts features associated with hand-object interactions. This includes comparing motion cues in the region near the hand (i.e., where the object is most likely to be located) to the motion of the hand itself, as well as to the motion of the background. Features representing hand shape are also extracted. The features serve as inputs to a random forest classifier, which was tested with a dataset of 14 activities of daily living as well as noninteractive tasks in five environment (total video duration of 44.16 min). The average F-score for the classifier was 0.85 for leave-one-activity out in our dataset set and 0.91 for a publicly available set (1.72 min) when filtered with a moving average. These results suggest that using egocentric video to monitor functional hand use at home is feasible.This work was supported in part by the Natural Sciences and Engineering Research Council of Canada (RGPIN-2014-05498) and the Rick Hansen Institute (G2015-30)

Analysis of the hands in egocentric vision: A survey

: Egocentric vision (a.k.a. first-person vision - FPV) applications have thrived over the past few years, thanks to the availability of affordable wearable cameras and large annotated datasets. The position of the wearable camera (usually mounted on the head) allows recording exactly what the camera wearers have in front of them, in particular hands and manipulated objects. This intrinsic advantage enables the study of the hands from multiple perspectives: localizing hands and their parts within the images; understanding what actions and activities the hands are involved in; and developing human-computer interfaces that rely on hand gestures. In this survey, we review the literature that focuses on the hands using egocentric vision, categorizing the existing approaches into: localization (where are the hands or parts of them?); interpretation (what are the hands doing?); and application (e.g., systems that used egocentric hand cues for solving a specific problem). Moreover, a list of the most prominent datasets with hand-based annotations is provided

A wearable vision-based system for detecting hand-object interactions in individuals with cervical spinal cord injury: First results in the home environment

: Cervical spinal cord injury (cSCI) causes the paralysis of upper and lower limbs and trunk, significantly reducing quality of life and community participation of the affected individuals. The functional use of the upper limbs is the top recovery priority of people with cSCI and wearable vision-based systems have recently been proposed to extract objective outcome measures that reflect hand function in a natural context. However, previous studies were conducted in a controlled environment and may not be indicative of the actual hand use of people with cSCI living in the community. Thus, we propose a deep learning algorithm for automatically detecting hand-object interactions in egocentric videos recorded by participants with cSCI during their daily activities at home. The proposed approach is able to detect hand-object interactions with good accuracy (F1-score up to 0.82), demonstrating the feasibility of this system in uncontrolled situations (e.g., unscripted activities and variable illumination). This result paves the way for the development of an automated tool for measuring hand function in people with cSCI living in the community

Influence of Anatomical Detail and Tissue Conductivity Variations in Simulations of Multi-Contact Nerve Cuff Recordings

Accurate simulations of peripheral nerve recordings are needed to develop improved neuroprostheses. Previous models of peripheral nerves contained simplifications whose effects have not been investigated. We created a novel detailed finite element (FE) model of a peripheral nerve, and used it to carry out a sensitivity analysis of several model parameters. To construct the model, in vivo recordings were obtained in a rat sciatic nerve using an 8-channel nerve cuff electrode, after which the nerve was imaged using magnetic resonance imaging (MRI). The FE model was constructed based on the MRI data, and included progressive branching of the fascicles. Neural pathways were defined in the model for the tibial, peroneal and sural fascicles. The locations of these pathways were selected so as to maximize the correlations between the simulated and in vivo recordings. The sensitivity analysis showed that varying the conductivities of neural tissues had little influence on the ability of the model to reproduce the recording patterns obtained experimentally. On the other hand, the increased anatomical detail did substantially alter the recording patterns observed, demonstrating that incorporating fascicular branching is an important consideration in models of nerve cuff recordings. The model used in this study constitutes an improved simulation tool and can be used in the design of neural interfaces.This work was supported in part by the Natural Sciences and Engineering Research Council of Canada (RGPIN-2014-05498)