On the viability of implantable electrodes for the natural control of artificial limbs: Review and discussion

The control of robotic prostheses based on pattern recognition algorithms is a widely studied subject that has shown promising results in acute experiments. The long-term implementation of this technology, however, has not yet been achieved due to practical issues that can be mainly attributed to the use of surface electrodes and their highly environmental dependency. This paper describes several implantable electrodes and discusses them as a solution for the natural control of artificial limbs. In this context "natural" is defined as producing control over limb movement analogous to that of an intact physiological system. This includes coordinated and simultaneous movements of different degrees of freedom. It also implies that the input signals must come from nerves or muscles that were originally meant to produce the intended movement and that feedback is perceived as originating in the missing limb without requiring burdensome levels of concentration. After scrutinizing different electrode designs and their clinical implementation, we concluded that the epimysial and cuff electrodes are currently promising candidates to achieving a long-term stable and natural control of robotic prosthetics, provided that communication from the electrodes to the outside of the body is guaranteed

Restoring Upper Extremity Mobility through Functional Neuromuscular Stimulation using Macro Sieve Electrodes

The last decade has seen the advent of brain computer interfaces able to extract precise motor intentions from cortical activity of human subjects. It is possible to convert captured motor intentions into movement through coordinated, artificially induced, neuromuscular stimulation using peripheral nerve interfaces. Our lab has developed and tested a new type of peripheral nerve electrode called the Macro-Sieve electrode which exhibits excellent chronic stability and recruitment selectivity. Work presented in this thesis uses computational modeling to study the interaction between Macro-Sieve electrodes and regenerated peripheral nerves. It provides a detailed understanding of how regenerated fibers, both on an individual level and on a population level respond differently to functional electrical stimulation compared to non-disrupted axons. Despite significant efforts devoted to developing novel regenerative peripheral interfaces, the degree of spatial clustering between functionally related fibers in regenerated nerves is poorly understood. In this thesis, bioelectrical modeling is also used to predict the degree of topographical organization in regenerated nerve trunks. In addition, theoretical limits of the recruitment selectivity of the device is explored and a set of optimal stimulation paradigms used to selectively activate fibers in different regions of the nerve are determined. Finally, the bioelectrical model of the interface/nerve is integrated with a biomechanical model of the macaque upper limb to study the feasibility of using macro-sieve electrodes to achieve upper limb mobilization

Retina implant project : chronic implantation of active epiretinal implants

b

Skin-Integrated wearable systems and implantable biosensors: a comprehensive review

Biosensors devices have attracted the attention of many researchers across the world. They have the capability to solve a large number of analytical problems and challenges. They are future ubiquitous devices for disease diagnosis, monitoring, treatment and health management. This review presents an overview of the biosensors field, highlighting the current research and development of bio-integrated and implanted biosensors. These devices are micro- and nano-fabricated, according to numerous techniques that are adapted in order to offer a suitable mechanical match of the biosensor to the surrounding tissue, and therefore decrease the body’s biological response. For this, most of the skin-integrated and implanted biosensors use a polymer layer as a versatile and flexible structural support, combined with a functional/active material, to generate, transmit and process the obtained signal. A few challenging issues of implantable biosensor devices, as well as strategies to overcome them, are also discussed in this review, including biological response, power supply, and data communication.This research was funded by FCT- FUNDAÇÃO PARA A CIÊNCIA E TECNOLOGIA, grant numbers: PTDC/EMD-EMD/31590/2017 and PTDC/BTM-ORG/28168/2017

Zapping the Retina - Understanding electrical responsiveness and electrical desensitization in mouse retinal ganglion cells

The field of science and technology has come a long way since the famous 70’s science fiction series “The Six Million Dollar Man,” where a disabled pilot was transformed into a bionic superhero after receiving artificial implants. What was indeed once a science fiction has now turned into a science fact with the development of various electronic devices interfacing the human neurons in the brain, retina, and limbs. One such advancement was the development of retinal implants. Over the past two decades, the field of retinal prosthetics has made significant advancement in restoring functional vision in patients blinded by diseases such as Retinitis pigmentosa (RP) and Age-related macular degeneration (AMD). RP and AMD are the two leading cause of degenerative blindness. While there is still no definitive cure for either of these diseases, various treatment strategies are currently being explored. Of the various options, the most successful one has been the retinal implants. Retinal implants are small microelectrode or photodiode arrays, which are implanted in the eye of a patient, to stimulate the degenerating retina electrically. They are broadly classified into three types depending on the placement ̶ epiretinal (close proximity to retinal ganglion cells, RGCs) , subretinal (close proximity to bipolar cells, BP) and suprachoroidal (close proximity to choroid). While the ongoing human trials have shown promising results, there remains a considerable variability among patients concerning the quality of visual percepts which limits the working potential of these implants. One such limitation often reported by the implanted patients is “ fading” of visual percepts. Fading refers to the limited ability to elicit temporally stable visual percepts. While, this is not a primary concern for epiretinal implants , it is often observed in subretinal and suprachoroidal implants which use the remaining retinal network to control the temporal spiking pattern of the ganglion cells. The neural correlate of fading is often referred to as “electrical desensitization”, which is the reduction of ganglion cell responses to repetitive electrical stimulation . While much is known about the temporal component of desensitization ( time constant, τ), the spatial aspects (space constant, λ) has not been well characterized. Further, how both these aspects interact to generate spiking responses, remains poorly understood. These crucial questions formed the critical components of my thesis. To address these questions, we stimulated the retinal network electrically, with voltage and current pulses and recorded the corresponding spiking activity using the microelectrode arrays (MEAs). While addressing the primary question of my thesis, we were able to address few idiosyncrasies which has currently stymied the field of retinal prosthetics. At a conceptual level, we have developed an experimental and analysis framework by which one can identify the single stimulus that will activate the most ganglion cells (Chapter 2, Part 1). This stimulus is optimal for ‘blind’ experiments where the specific response properties of each cell are unknown. Furthermore, we attempted to understand the correspondence between the electrical response patterns and visual response types (Chapter 2, Part2). In Chapter 3, we sought to understand better how the visual responses parameters change during ongoing electrical stimulation. We demonstrated that apart from the adaptation occurring due to visual stimulation and invitro experimental conditions, the electrical stimulation alters the RGC visual responses, suggesting the requirement for stimulation-induced changes to be incorporated in the designing of stimulation paradigms for the implant. Finally addressing the primary question (Chapter 4) of my thesis with which we started, we were able to demonstrate, that the electrical desensitization requires the interaction of both time and distance and is not a global phenomenon of the retina. In the final chapter (Chapter 5) we summarize the results of the thesis, discuss the key outcomes and its relevance to the prosthetic field and other vision restoration strategies and the potential future directions of this research. Therefore, in future, to improve the efficacy of retinal prostheses and patient outcomes, it is crucial to have an in-depth understanding of the responsiveness of the retinal circuitry to electrical stimulation

Low-dimensional representations of neural time-series data with applications to peripheral nerve decoding

Bioelectronic medicines, implanted devices that influence physiological states by peripheral neuromodulation, have promise as a new way of treating diverse conditions from rheumatism to diabetes. We here explore ways of creating nerve-based feedback for the implanted systems to act in a dynamically adapting closed loop. In a first empirical component, we carried out decoding studies on in vivo recordings of cat and rat bladder afferents. In a low-resolution data-set, we selected informative frequency bands of the neural activity using information theory to then relate to bladder pressure. In a second high-resolution dataset, we analysed the population code for bladder pressure, again using information theory, and proposed an informed decoding approach that promises enhanced robustness and automatic re-calibration by creating a low-dimensional population vector. Coming from a different direction of more general time-series analysis, we embedded a set of peripheral nerve recordings in a space of main firing characteristics by dimensionality reduction in a high-dimensional feature-space and automatically proposed single efficiently implementable estimators for each identified characteristic. For bioelectronic medicines, this feature-based pre-processing method enables an online signal characterisation of low-resolution data where spike sorting is impossible but simple power-measures discard informative structure. Analyses were based on surrogate data from a self-developed and flexibly adaptable computer model that we made publicly available. The wider utility of two feature-based analysis methods developed in this work was demonstrated on a variety of datasets from across science and industry. (1) Our feature-based generation of interpretable low-dimensional embeddings for unknown time-series datasets answers a need for simplifying and harvesting the growing body of sequential data that characterises modern science. (2) We propose an additional, supervised pipeline to tailor feature subsets to collections of classification problems. On a literature standard library of time-series classification tasks, we distilled 22 generically useful estimators and made them easily accessible.Open Acces