Dissertations / Theses on the topic 'Neural prosthesis'

Restoring vision to the blind has been an objective of several research teams for a number of years. It is known that spots of light -- phosphenes -- can be elicited by way of electrical stimulation of surviving retinal neurons. Beyond this, however, our understanding of prosthetic vision remains rudimentary. To advance the realisation of a clinically viable prosthesis for the blind, a versatile integrated circuit neurostimulator was designed, manufactured, and verified. The neurostimulator provides electrical stimuli to surviving neurons in the visual pathway, affording blind patients some form of patterned vision; besides other benefits (independence), this limited vision would let patients distinguish between day and night (resetting their circadian rhythm). This thesis presents the development of the neurostimulator, an interdisciplinary work bridging engineering and medicine. Features of the neurostimulator include: high-voltage CMOS transistors in key circuits, to prevent voltage compliance issues due to an unknown or changing combined tissue and electrode/tissue interface impedance; simultaneous stimulation using current sources and sinks, with return electrodes configured to provide maximum charge containment at each stimulation site; stimuli delivered to a two dimensional mosaic of hexagonally packed electrodes, multiplexing current sources and sinks to allow each electrode in the whole mosaic to become a stimulation site; electrode shorting to remove excess charge accumulated during each stimulation phase. Detailed electrical testing and characterisation verified that the neurostimulator performed as specified, and comparable to, or better than, other vision prostheses neurostimulators. In addition, results from several animal experiments verified that the neurostimulator can elicit electrically evoked visual responses. The features of the neurostimulator enable research into how simultaneous electrical stimulation affects the visual neural pathways; those research results could impact other neural prosthetics research and devices.

In this work, the prototype of an electronic bi-directional interface between the Peripheral Nervous System (PNS) and a neuro-controlled hand prosthesis is presented. The system is composed of two Integrated Circuits (ICs): a standard CMOS device for neural recording and a High Voltage (HV) CMOS device for neural stimulation. The integrated circuits have been realized in two different 0.35μm CMOS processes available fromAustriaMicroSystem(AMS). The recoding IC incorporates 8 channels each including the analog front-end and the A/D conversion based on a sigma delta architecture. It has a total area of 16.8mm2 and exhibits an overall power consumption of 27.2mW. The neural stimulation IC is able to provide biphasic current pulses to stimulate 8 electrodes independently. A voltage booster generates a 17V voltage supply in order to guarantee the programmed stimulation current even in case of high impedances at the electrode-tissue interface in the order of tens of k­. The stimulation patterns, generated by a 5-bit current DAC, are programmable in terms of amplitude, frequency and pulse width. Due to the huge capacitors of the implemented voltage boosters, the stimulation IC has a wider area of 18.6mm2. In addition, a maximum power consumption of 29mW was measured. Successful in-vivo experiments with rats having a TIME electrode implanted in the sciatic nerve were carried out, showing the capability of recording neural signals in the tens of microvolts, with a global noise of 7μVrms , and to selectively elicit the tibial and plantarmuscles using different active sites of the electrode. In order to get a completely implantable interface, a biocompatible and biostable package was designed. It hosts the developed ICs with the minimal electronics required for their proper operation. The package consists of an alumina tube closed at both extremities by two ceramic caps hermetically sealed on it. Moreover, the two caps serve as substrate for the hermetic feedthroughs to enable the device powering and data exchange with the external digital controller implemented on a Field-Programmable Gate Array (FPGA) board. The package has an outer diameter of 7mm and a total length of 26mm. In addition, a humidity and temperature sensor was also included inside the package to allow future hermeticity and life-time estimation tests. Moreover, a wireless, wearable and non-invasive EEG recording system is proposed in order to improve the control over the artificial limb,by integrating the neural signals recorded from the PNS with those directly acquired from the brain. To first investigate the system requirements, a Component-Off-The-Shelf (COTS) device was designed. It includes a low-power 8- channel acquisition module and a Bluetooth (BT) transceiver to transmit the acquired data to a remote platform. It was designed with the aimof creating a cheap and user-friendly system that can be easily interfaced with the nowadays widely spread smartphones or tablets by means of a mobile-based application. The presented system, validated through in-vivo experiments, allows EEG signals recording at different sample rates and with a maximum bandwidth of 524Hz. It was realized on a 19cm2 custom PCB with a maximum power consumption of 270mW.

Background. A visual prosthesis is a conceptual device designed to activate residual functional neurons in the visual pathway of blind individuals to produce artificial vision. Such device, when applied to stimulate the vitreous surface of the retina, has proven feasible in producing patterned light perception in blind individuals suffering from dystrophic diseases of the retina, such as aged-related macular degeneration (AMD). However the practicality of such approach has been challenged by the difficulty of surgical access and the risks of damaging the neuroretina. Positioning a visual implant over the scleral surface of the eye could present a safer alternative but this stimulation modality has not been tested in diseased retinas. Additionally, recent research has shown that the adult neocortex retains substantial plasticity following a disruption to its visual input and the potential deterioration in visual capabilities as a result of such experience modification may undermine the overall bionic rescue strategy. Methods. Two animal models mimicking the principal pathologies found in AMD, namely photoreceptor degeneration and reduced retinal ganglion cell mass, were used to evaluate the efficacy of trans-scleral stimulation of the retina by recording electrical evoked potentials in the visual cortex. The visual performance following the loss of pattern vision induced by bilateral eyelid suturing in adult mice was examined by analysing visual evoked potentials. Findings. Spatially differentiated cortical activations were obtained notwithstanding the underlying retinopathy in the experiment animals. The charge density thresholds were found to be similar to controls and below the bioelectric safety limit. After prolonged visual deprivation (weeks) in the mouse, the visual cortical responses evoked by either electrical or photic stimuli were both significantly reduced. An assessment of different visual capabilities using patterned stimuli demonstrated that whilst visual acuity and motion sensitivity were preserved, significant depression in luminance and contrast sensitivities was detected. Conclusion. Trans-scleral stimulation of the retina is a feasible approach for the development of a visual prosthesis. Following visual loss the adult brain exhibits significant experience-dependent modifications. These new insights may force a revision on the current bionic rescue strategy.

Many people around the world suffer from neurological injuries of various sorts that cause serious difficulties in their lives, due to the loss of important sensory and motor functions. Functional electrical stimulation (FES) provides a possible solution to these difficulties by means of a feedback connection allowing the target organ (or organs) to be controlled by electrical stimulation. The control signals can be provided using recorded data extracted from the nerves (electroneurogram, ENG). The most common and safe approaches for interfacing with nerves is called cuff electrodes which deliver the required feedback path for the implantable system with minimum risk. The amount of recorded information can be improved by increasing the number of electrodes within a single cuff known as multi-electrode cuffs (MECs) configuration. This strategy can increase the signal to noise ratio for the recorded signals which have typically very low amplitude (less than 5μV). Consequently multiple high gain amplifiers are used in order to amplify the signals and supply a multi-channel recorded data stream for signal processing or monitoring applications. The signal processing unit within the implantable system or outside the body is employed for classification and sorting the action potential signals (APs) depending on their conduction velocities. This method is called velocity selective recording (VSR). Basically, the idea of this approach is that the conduction velocity of AP can be determined by timing the appearance of the signal at two or more points along the nerve and then dividing the distance between the points by the delay. The purpose of this thesis to investigate an alternative approach using artificial network for APs detection and extraction in neural recording applications to increase the velocity selectivity based on VSR using MECs. The prototype systems impose four major requirements which are high velocity selectivity, small size, low power consumption and high reliability. The proposed method has been developed for applications which require online AP classification. A novel time delay neural network (TDNN) approach is used to decompose the recorded data into several matched velocity bands to allow for individual velocity selectivity at each band to be increased. Increasing the velocity selectivity leads to more accurate recording from the target fibre (or fibres) within the nerve bundle which can be used for applications that require AP classification such as bladder control and the adjustment of foot drop. The TDNN method was developed to obtain more information from an individual cuff without increasing the number of electrodes or the sampling rate. Moreover, the optimization of the hardware implementation for the proposed signal processing method permits savings in power consumption and silicon area. Finally, a nerve signal synthesiser and noise generator for the evaluation of the VSRmethod is described. This system generates multiple artificial AP signals with a time offset between the channels with additive white Gaussian noise (AWGN) to simulate the MEC and hence reduce the cost and the number of the animals required for experimental tests.

Electrical stimulation of the retina affected by photoreceptor loss (e.g., cases of retinitis pigmentosa) elicits the perception of luminous spots (so-called phosphenes) in the visual field. This phenomenon, attributed to the relatively high survival rates of neurons comprising the retina's inner layer, serves as the cornerstone of efforts to provide a microelectronic retinal prosthesis -- a device analogous to the cochlear implant. This thesis concerns phosphenes -- their elicitation and modulation, and, in turn, image analysis for use in a prosthesis. This thesis begins with a comparative review of visual modelling of electrical epiretinal stimulation and analogous acoustic modelling of electrical cochlear stimulation. The latter models involve coloured noise played to normal listeners so as to investigate speech processing and electrode design for use in cochlear implants. Subsequently, four experiments (three psychophysical and one numerical), and two statistical analyses, are presented. Intrinsic signal optical imaging in cerebral cortex is canvassed appendically. The first experiment describes a visual tracking task administered to 20 normal observers afforded simulated prosthetic vision. Fixation, saccade, and smooth pursuit, and the effect of practice, were assessed. Further, an image analysis scheme is demonstrated that, compared to existing approaches, assisted fixation and pursuit (but not saccade) accuracy (35.8% and 6.8%, respectively), and required less phosphene array scanning. Subsequently, (numerical) information-theoretic reasoning is provided for the scheme's superiority. This reasoning was then employed to further optimise the scheme (resulting in a filter comprising overlapping Gaussian kernels), and may be readily extended to arbitrary arrangements of many phosphenes. A face recognition study, wherein stimuli comprised either size- or intensity-modulated phosphenes, is then presented. The study involved unpracticed observers (n=85), and showed no 'size' --versus--'intensity' effect. Overall, a 400-phosphene (100-phosphene) image afforded subjects 89.0% (64.0%) correct recognition (two-interval forced-choice paradigm) when five seconds' scanning was allowed. Performance fell (64.5%) when the 400-phosphene image was stabilised on the retina and presented briefly. Scanning was similar in 400- and 100-phosphene tasks. The final chapter presents the statistical effects of sampling and rendering jitter on the phosphene image. These results may generalise to low-resolution imaging systems involving loosely packed pixels.

Thesis (Ph.D.)--Bioengineering, Georgia Institute of Technology, 2008.
Committee Chair: Stephen P. DeWeerth; Committee Member: Bruce Wheeler; Committee Member: Michelle LaPlaca; Committee Member: Robert Lee; Committee Member: Steve Potter

Spinal cord injury results in complete failure of the central neurons to regenerate and is associated with cyst formation and enlargement of the trauma zone. In contrast to the spinal cord, axons in the injured peripheral nerve have the capacity to undergo some spontaneous regeneration. However, significant post-traumatic loss of nervous tissue causing long nerve gap is one of the main reasons for the poor restoration of function following microsurgical repair of injured nerves. The present thesis investigates the effects of biodegradable conduits prepared from fibrin glue and poly-beta-hydroxybutyrate (PHB) in combination with cultured Schwann cells, mesenchymal stem cells and extracellular matrix molecules on regeneration after spinal cord and peripheral nerve injury in adult rats. At 4-8 weeks after transplantation into the injured spinal cord, the PHB conduit was well integrated into the cavity but regenerating axons were found mainly outside the PHB. When suspension of BrdU-labeled Schwann cells was added to the PHB, regenerating axons filled the conduit and became associated with the implanted cells. Modification of the PHB surface with extracellular matrix molecules significantly increased Schwann cell attachment and proliferation but did not alter axonal regeneration. To improve the labeling technique of the transplanted cells, the efficacy of fluorescent cell tracers Fast Blue, PKH26, Vibrant DiO and Cell Tracker™ Green CMFDA was evaluated. All tested dyes produced very efficient initial labeling of olfactory ensheathing glial cells in culture. The number of Fast Blue-labeled cells remained largely unchanged during the first 4 weeks whereas the number of cells labeled with other tracers was significantly reduced after 2 weeks. After transplantation into the spinal cord, Fast Blue-labeled glial cells survived for 8 weeks but demonstrated very limited migration from the injection sites. Additional immunostaining with glial and neuronal markers demonstrated transfer of the dye from the transplanted cells to the host tissue. In a sciatic nerve injury model, the extent of axonal regeneration through a 10mm gap bridged with tubular PHB conduit was compared with a fibrin glue conduit. At 2 weeks after injury, the fibrin conduit supported similar axonal regeneration and migration of the host Schwann cells compared with the PHB conduit augmented with a diluted fibrin matrix and GFP-labeled Schwann cells or mesenchymal stem cells. The long-term regenerative response was evaluated using retrograde neuronal labeling. The fibrin glue conduit promoted regeneration of 60% of sensory neurons and 52% of motoneurons when compared with the autologous nerve graft. The total number of myelinated axons in the distal nerve stump in the fibrin conduit group reached 86% of the nerve graft control and the weight of gastrocnemius and soleus muscles recovered to 82% and 89%, respectively. When a fibrin conduit was used to bridge a 20mm sciatic nerve gap, the weight of gastrocnemius muscle reached only 43% of the nerve graft control. The morphology of the muscle showed more chaotic appearance and the mean area and diameter of fast type fibers were significantly worse than those of the corresponding 10mm gap group. In contrast, both gap sizes treated with nerve graft showed similar fiber size. In summary, these results show that a PHB conduit promotes attachment, proliferation and survival of adult Schwann cells and supports marked axonal growth after transplantation into the injured spinal cord. The data suggest an advantage of the fibrin conduit for the important initial phase of peripheral nerve regeneration and demonstrate potential of the conduit to promote long-term neuronal regeneration and muscle recovery.

Numerous applications in neuroscience research and neural prosthetics, such as retinal prostheses, spinal-cord surface stimulation for prosthetics, electrocorticogram (ECoG) recording for epilepsy detection, etc., involve electrical interaction with soft excitable tissues using a surface stimulation and/or recording approach. These applications require an interface that is able to set up electrical communications with a high throughput between electronics and the excitable tissue and that can dynamically conform to the shape of the soft tissue. Being a compliant and biocompatible material with mechanical impedance close to that of soft tissues, polydimethylsiloxane (PDMS) offers excellent potential as the substrate material for such neural interfaces. However, fabrication of electrical functionalities on PDMS has long been very challenging. This thesis work has successfully overcome many challenges associated with PDMS-based microfabrication and achieved an integrated technology platform for PDMS-based stretchable microelectrode arrays (sMEAs). This platform features a set of technological advances: (1) we have fabricated uniform current density profile microelectrodes as small as 10 microns in diameter; (2) we have patterned high-resolution (feature as small as 10 microns), high-density (pitch as small as 20 microns) thin-film gold interconnects on PDMS substrate; (3) we have developed a multilayer wiring interconnect technology within the PDMS substrate to further boost the achievable integration density of such sMEA; and (4) we have invented a bonding technology---via-bonding---to facilitate high-resolution, high-density integration of the sMEA with integrated circuits (ICs) to form a compact implant. Taken together, this platform provides a high-resolution, high-density integrated system solution for neural and muscular surface interfacing. sMEAs of example designs are evaluated through in vitro and in vivo experimentations on their biocompatibility, surface conformability, and surface recording/stimulation capabilities, with a focus on epimysial (i.e. on the surface of muscle) applications. Finally, as an example medical application, we investigate a prosthesis for unilateral vocal cord paralysis (UVCP) based on simultaneous multichannel epimysial recording and stimulation.

BACKGROUND: Based on mainly vascular diseases and traumatic injuries, around 40,000 upper limb amputations are performed annually worldwide. The affected persons are strongly impaired in their physical abilities by such an intervention. Through myoelectric prostheses, affected persons are able to recover some of their abilities. METHODS: In order to control such prostheses, a system is to be developed by which electromyographic (EMG) measurements on the upper extremities can be carried out. The data obtained in this way should then be processed to recognize different gestures. These EMG measurements are to be performed by means of a suitable microcontroller and afterwards processed and classified by adequate software. Finally, a model or prototype of a hand is to be created, which is controlled by means of the acquired data. RESULTS: The signals from the upper extremities were picked up by four MyoWare sensors and transmitted to a computer via an Arduino Uno microcontroller. The Signals were processed in quantized time windows using Matlab. By means of a neural network, the gestures were recognized and displayed both graphically and by a prosthesis. The achieved recognition rate was up to 87% across all gestures. CONCLUSION: With an increasing number of gestures to be detected, the functionality of a neural network exceeds that of any fuzzy logic concerning classification accuracy. The recognition rates fluctuated between the individual gestures. This indicates that further fine tuning is needed to better train the classification software. However, it demonstrated that relatively cheap hardware can be used to create a control system for upper extremity prostheses.

Um dos desafios propostos por pesquisadores em neurociência aos engenheiros biomédicos é a interação cérebro-máquina. O sistema nervoso comunica-se interpretando sinais eletroquímicos, e circuitos implantáveis podem tomar decisões de modo a interagir com o meio biológico. Sabe-se também que a doença de Parkinson está relacionada a um déficit do neurotransmissor dopamina. Para controlar a concentração de dopamina diferentes técnicas tem sido empregadas como estimuladores elétricos, magnéticos e drogas. Neste trabalho obteve-se o controle da concentração do neurotransmissor de maneira automática uma vez que atualmente isto não é realizado. Para tanto, projetou-se e desenvolveu-se quatro sistemas: a estimulação cerebral profunda ou deep brain stimulation (DBS), a estimulação transmagnética ou transmagnetic stimulation (TMS), um controle de bomba de infusão ou infusion pump control (IPC) para a entrega de drogas e um sistema de voltametria cíclica de varredura rápida ou fast scan ciclic voltammetry (FSCV) (circuito que detecta variações de concentração de neurotransmissores como a dopamina - DA). Também foi necessário o desenvolvimento de softwares para a visualização de dados e análises em sincronia com acontecimentos ou experimentos correntes, facilitando a utilização destes dispositivos quando emprega-se bombas de infusão e a sua flexibilidade é tal que a DBS ou a TMS podem ser utilizadas de maneira manual ou automática além de outras técnicas de estimulação como luzes, sons, etc. O sistema desenvolvido permite controlar de forma automática a concentração da DA. A resolução do sistema é de 0.4 µmol/L podendo-se ajustar o tempo para correção da concentração entre 1 e 90 segundos. O sistema permite controlar concentrações entre 1 e 10 µmol/L, com um erro de cerca de +/- 0,8 µmol/L. Embora desenhado para o controle da concentração de dopamina o sistema pode ser utilizado para controlar outros neurotransmissores. Propõe-se continuar o desenvolvimento em malha fechada empregando FSCV e DBS (ou TMS, ou infusão), utilizando modelos animais parkinsonianos.
One of the challenges to biomedical engineers proposed by researchers in neuroscience is brain machine interaction. The nervous system communicates by interpreting electrochemical signals, and implantable circuits make decisions in order to interact with the biological environment. It is well known that Parkinson’s disease is related to a deficit of dopamine (DA). Different methods has been employed to control dopamine concentration like magnetic or electrical stimulators or drugs. In this work was automatically controlled the neurotransmitter concentration since this is not currently employed. To do that, four systems were designed and developed: deep brain stimulation (DBS), transmagnetic stimulation (TMS), Infusion Pump Control (IPC) for drug delivery, and fast scan cyclic voltammetry (FSCV) (sensing circuits which detect varying concentrations of neurotransmitters like dopamine caused by these stimulations). Some softwares also were developed for data display and analysis in synchronously with current events in the experiments. This allowed the use of infusion pumps and their flexibility is such that DBS or TMS can be used in single mode and other stimulation techniques and combinations like lights, sounds, etc. The developed system allows to control automatically the concentration of DA. The resolution of the system is around 0.4 µmol/L with time correction of concentration adjustable between 1 and 90 seconds. The system allows controlling DA concentrations between 1 and 10 µmol/L, with an error about +/- 0.8 µmol/L. Although designed to control DA concentration, the system can be used to control, the concentration of other substances. It is proposed to continue the closed loop development with FSCV and DBS (or TMS, or infusion) using parkinsonian animals models.

Les travaux menés au cours de cette thèse portent sur la microfabrication d'interfaces neuro-électroniques pour des applications en neurosciences. Nous avons choisi de nos focaliser sur la réhabilitation fonctionnelle motrice et sensorielle en développant différentes matrices de micro-électrodes (MEA) respectivement, des sondes neuronales rigides et des implants rétiniens souples. Selon les applications visées, deux types de substrats ont été utilisés pour concevoir ces MEA. Pour des analyses ou expériences in-vitro, les MEA (sondes neuronales) ont plutôt été réalisées sur des substrats rigides tels que le silicium ou le verre, tandis que pour les expériences in-vivo, les MEA (implants rétiniens) ont été réalisées sur des substrats souples tels que des polymères biocompatibles (polyimide ou parylène). Ces MEA ont été fabriquées avec différents matériaux d'électrodes (diamant dopé, platine, platine noir et or) qui ont également été testés afin de déterminer leur capacité en enregistrement et/ou stimulation. De plus, à l'aide de travaux de modélisation numérique, nous avons validé le concept d'une géométrie tridimensionnelle avec grille de masse permettant une stimulation plus focale des cellules. Cette thèse a ainsi contribué à stabiliser différents procédés de fabrication pour obtenir des MEA plus reproductibles ainsi que pour améliorer leur rendement. Elle a également permis d'établir un suivi et un protocole expérimental pour assurer une traçabilité des MEA et contrôler leur performances à toutes les étapes : depuis leur fabrication au moyen de techniques électrochimiques (CV, EIS) jusqu'aux expériences biologiques in-vitro et in-vivo
The work lead during this thesis deals with microfabrication of neuro-electronic interfaces for neuroscience applications. We have chosen to focus on motor and sensory function rehabilitations by developing Micro-Electrode Arrays (MEA) respectively, rigid neural probes and flexible retinal implants. According to the targeted applications, two types of substrates have been used to achieve these MEA. For analysis or in vitro experiments, neural probes MEA have been realized on rigid substrates such as silicon or glass whereas for in-vivo experiments, retinal implants MEA have been realized on flexible substrates such as biocompatible polymers (polyimide or parylene). These MEA were made with different electrode materials (boron doped diamond, platinum, black platinum and gold) which have been tested to determine their capability in recording and/or stimulation. Moreover, with numerical modelling work, we have validated a tridimensional geometry concept with a ground grid which permits a more local stimulation of cells. This thesis has contributed to stabilize different fabrication processes to obtain more repeatable MEA and also to improve their yield. It also allowed the set-up of a follow-up and an experimental protocol to insure MEA traceability and to monitor their performances at each step since their fabrication through means of electrochemical techniques (CV, EIS) to in vitro and in-vivo biological experiments

Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2007.
Includes bibliographical references (leaves 213-230).
Neural prosthetic devices offer a means of restoring function that have been lost due to neural damage. The first part of this thesis investigates the design of a 15-channel, low-power, fully implantable stimulator chip. The chip is powered wirelessly and receives wireless commands. The chip features a CMOS only ASK detector, a single-differential converter based on a novel feedback loop, a low-power adaptive bandwidth DLL and 15 programmable current sources that can be controlled via four commands. Though it is feasible to build an implantable stimulator chip, the amount of power required to stimulate more than 16 channels is prohibitively large. Clearly, there is a need for a fundamentally different approach. The ultimate challenge is to design a self-sufficient neural interface. The ideal device will lend itself to seamless integration with the existing neural architecture. This necessitates that communication with the neural tissue should be performed via chemical rather than electrical messages. However, catastrophic destruction of neural tissue due to the release of large quantities of a neuroactive species, like neurotransmitters, precludes the storage of quantities large enough to suffice for the lifetime of the device. The ideal device then should actively sequester the chemical species from the body and release it upon receiving appropriate triggers in a power efficient manner. This thesis proposes the use of ionic gradients, specifically K+ ions as an alternative chemical stimulation method. The required ions can readily be sequestered from the background extracellular fluid. The parameters of using such a stimulation technique are first established by performing in-vitro experiments on rabbit retinas. The results show that modest increases (~~10mM) of K+ ions are sufficient to elicit a neural response.
(cont.) The first building block of making such a stimulation technique possible is the development of a potassium selective membrane. To achieve low-power the membranes must be ultrathin to allow for efficient operation in the diffusive transport limited regime. One method of achieving this is to use lyotropic self-assembly; unfortunately, conventional lipid bilayers cannot be used since they are not robust enough. Furthermore, the membrane cannot be made potassium selective by simply incorporating ion carriers since they would eventually leach away from the membrane. A single solution that solves all the above issues was then investigated in this thesis. A novel facile synthesis of self-assembling receptor functionalized polymers was achieved. By combining the properties of hydrophobic and hydrophilic interactions of two polymers a triblock co-polymer was synthesized. The middle hydrophobic block was composed of biocompatible polysiloxanes and further derivatized to posses ion recognition capabilities via pendant crown ether chains. The hydrophilic blocks were composed of biocompatible polyoxazolines. The self-assembling properties of the membrane were then studied by electroforming them into vesicular structures. The ion responsive properties of these polymers were then examined. These polymers show emergent behavior such as, spontaneous fusion and shape transformation to ionic stimuli due to the synergy between form and function. The results from the thesis show that it is feasible to build a renewable chemically based neural prosthesis based on supramolecular architectures. However, there remains a lot of fundamental work that needs to be pursued in the future to bring the idea to complete fruition.
by Luke Satish Kumar Theogarajan.
Ph.D.

A finalidade desse trabalho é desenvolver um protótipo de uma prótese antropomórfica multifuncional para membros superiores para pacientes amputados. Seu objetivo é substituir a mão natural perdida, de forma a auxiliar a realização de algumas tarefas diárias do usuário. A prótese possuirá características antropomórficas, tais como aparência e movimentação semelhantes às da mão humana, e características naturais inerentes à mesma, por exemplo, o arco reflexo. Além disso, contará também com meios de realimentação táteis das informações de forças aplicadas pela prótese em objetos, bem como sua temperatura para o paciente, suprindo assim uma das carências das próteses convencionais. Esse dispositivo terá incorporado na sua construção sensores diversos para realizar as funções propostas e contará com um algoritmo baseado em redes neurais artificiais, capaz de identificar padrões dos sinais mioelétricos do paciente, que serão utilizados como sinais de controle, possibilitando ao paciente um comando natural. Todas essas implementações visam contribuir para a redução da taxa de rejeição de próteses para membros superiores e possibilitar uma maior reabilitação e reintegração do paciente à sociedade.
The purpose of this assignment is to develop a multifunctional and anthropomorphic upper limb prosthesis prototype for amputated patients. Its objective is to substitute the natural lost hand, in a way to improve the performance of regular activities. This prosthesis will have anthropomorphic characteristics, like appearance and movement, similar to the ones of the human hand, and natural characteristics inherent to it, for example the reflected arc. Another characteristic will be the tactile feedback ways of obtaining the information of the forces applied by the prosthesis in objects, as well as their temperature for the patient, overcoming therefore one of the traditional prosthesis\' deficiency. This device will have incorporated in its construction many sensors in order to do the proposed functions and it will use an algorithm based on the artificial neural network that is able to recognize patterns of myoelectric signals of the patient, which will be used as control signals, making possible to the patient a natural command. All of these implementations objective to contribute for the reduction of the rejection rate of prostheses for upper limb members and make possible a better rehabilitation and reintegration of the patient in the society.

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Physics, 2007.
Includes bibliographical references (leaves 119-122).
This thesis develops a system for adaptively and automatically learning to interpret patterns of electrical activity in neuronal populations in a real-time, on-line fashion. The system is primarily intended to enable the long-term implantation of low-power, microchip-based recording and decoding hardware in the brains of human patients in order to treat neurologic disorders. The decoding system developed in the present work interprets neural signals from the parietal cortex encoding arm movement intention, suggesting that the system could function as the decoder in a neural prosthetic limb, potentially enabling a paralyzed person to control an artificial limb just as the natural one was controlled, through thought alone. The same decoder is also used to interpret the activity of a population of thalami neurons encoding head orientation in absolute space. The success of the decoder in that context motivates the development of a model of generalized place cells to explain how networks of neurons adapt the configurations of their receptive fields in response to new stimuli, learn to encode the structure of new parameter spaces, and ultimately retrace trajectories through such spaces in the absence of the original stimuli.
(cont.) Qualitative results of this model are shown to agree with experimental observations. This combination of results suggests that the neural signal decoder is applicable to a broad scope of neural systems, and that a microchip-based implementation of the decoder based on the designs presented in this thesis could function as a useful investigational tool for experimental neuroscience and potentially as an implantable interpreter of simple thoughts and dreams.
by Benjamin Isaac Rapoport.
S.M.

Upper-limb myoelectric prostheses are controlled by muscle activity information recorded on the skin surface using electromyography (EMG). Intuitive prosthetic control can be achieved by deploying statistical and machine learning (ML) tools to decipher the user's movement intent from EMG signals. This thesis proposes various means of advancing the capabilities of non-invasive, ML-based control of myoelectric hand prostheses. Two main directions are explored, namely classification-based hand grip selection and proportional finger position control using regression methods. Several practical aspects are considered with the aim of maximising the clinical impact of the proposed methodologies, which are evaluated with offline analyses as well as real-time experiments involving both able-bodied and transradial amputee participants. It has been generally accepted that the EMG signal may not always be a reliable source of control information for prostheses, mainly due to its stochastic and non-stationary properties. One particular issue associated with the use of surface EMG signals for upper-extremity myoelectric control is the limb position effect, which is related to the lack of decoding generalisation under novel arm postures. To address this challenge, it is proposed to make concurrent use of EMG sensors and inertial measurement units (IMUs). It is demonstrated this can lead to a significant improvement in both classification accuracy (CA) and real-time prosthetic control performance. Additionally, the relationship between surface EMG and inertial measurements is investigated and it is found that these modalities are partially related due to reflecting different manifestations of the same underlying phenomenon, that is, the muscular activity. In the field of upper-limb myoelectric control, the linear discriminant analysis (LDA) classifier has arguably been the most popular choice for movement intent decoding. This is mainly attributable to its ease of implementation, low computational requirements, and acceptable decoding performance. Nevertheless, this particular method makes a strong fundamental assumption, that is, data observations from different classes share a common covariance structure. Although this assumption may often be violated in practice, it has been found that the performance of the method is comparable to that of more sophisticated algorithms. In this thesis, it is proposed to remove this assumption by making use of general class-conditional Gaussian models and appropriate regularisation to avoid overfitting issues. By performing an exhaustive analysis on benchmark datasets, it is demonstrated that the proposed approach based on regularised discriminant analysis (RDA) can offer an impressive increase in decoding accuracy. By combining the use of RDA classification with a novel confidence-based rejection policy that intends to minimise the rate of unintended hand motions, it is shown that it is feasible to attain robust myoelectric grip control of a prosthetic hand by making use of a single pair of surface EMG-IMU sensors. Most present-day commercial prosthetic hands offer the mechanical abilities to support individual digit control; however, classification-based methods can only produce pre-defined grip patterns, a feature which results in prosthesis under-actuation. Although classification-based grip control can provide a great advantage over conventional strategies, it is far from being intuitive and natural to the user. A potential way of approaching the level of dexterity enjoyed by the human hand is via continuous and individual control of multiple joints. To this end, an exhaustive analysis is performed on the feasibility of reconstructing multidimensional hand joint angles from surface EMG signals. A supervised method based on the eigenvalue formulation of multiple linear regression (MLR) is then proposed to simultaneously reduce the dimensionality of input and output variables and its performance is compared to that of typically used unsupervised methods, which may produce suboptimal results in this context. An experimental paradigm is finally designed to evaluate the efficacy of the proposed finger position control scheme during real-time prosthesis use. This thesis provides insight into the capacity of deploying a range of computational methods for non-invasive myoelectric control. It contributes towards developing intuitive interfaces for dexterous control of multi-articulated prosthetic hands by transradial amputees.

Thesis (Ph.D.) -- University of Maryland, College Park, 2009.
Thesis research directed by: Dept. of Electrical and Computer Engineering . Title from t.p. of PDF. Includes bibliographical references. Published by UMI Dissertation Services, Ann Arbor, Mich. Also available in paper.

Neural prosthetics are used to replace or substantially augment remaining motor and sensory functions of neural pathways that were lost or damaged due to physical trauma, disease, or genetics. However, due to poor spatial signal resolution, neural prostheses fail to recapitulate the intimate, precise interactions inherent to neural networks. Designing materials and interfaces that direct de novo nerve growth to spatially specific stimulating elements is, therefore, a promising method to enhance signal specificity and performance of prostheses such as the successful cochlear implant (CI) and the developing retinal implant. In this work, the spatial and temporal reaction control inherent to photopolymerization was used to develop methods to generate micro and nanopatterned materials that direct neurite growth from prosthesis relevant neurons. In particular, neurite growth and directionality has been investigated in response to physical, mechanical, and chemical cues on photopolymerized surfaces. Spiral ganglion neurons (SGNs) serve as the primary neuronal model as they are the principal target for CI stimulation. The objective of the research is to rationally design materials that spatially direct neurite growth and to translate fundamental understanding of nerve cell-material interactions into methods of nerve regeneration that improve neural prosthetic performance. A rapid, single-step photopolymerization method was developed to fabricate micro and nanopatterned physical cues on methacrylate surfaces by selectively blocking light with photomasks. Feature height is readily tuned by modulating parameters of the photopolymerizaiton including initiator concentration and species, light intensity, separation distance from the photomask, and radiation exposure time. Alignment of neural elements increases significantly with increasing feature amplitude and constant periodicity, as well as with decreasing periodicity and constant amplitude. SGN neurite alignment strongly correlates with the maximum feature slope. Neurite alignment is compared on unpatterned, unidirectional, and multidirectional photopolymerized micropatterns. The effect of substrate rigidity on neurite alignment to physical cues was determined by maintaining equivalent pattern microfeatures, afforded by the reaction control of photopolymerization, while concomitantly altering the composition of several copolymer platforms to tune matrix stiffness. For each platform, neurite alignment to unidirectional patterns increases with increasing substrate rigidity. Interestingly, SGN neurites respond to material stiffness cues that are orders of magnitude higher (GPa) than what is typically ascribed to neural environments (kPa). Finally, neurite behavior at bioactive borders of various adhesion modulating molecules was evaluated on micropatterned materials to determine which cues took precedence in establishing neurite directionality. At low microfeatures aspect ratios, neurites align to the pattern direction but are then caused to turn and repel from or turn and align to bioactive borders. Conversely, physical cues dominate neurite path-finding as pattern feature slope increases, i.e. aspect ratio of sloping photopolymerized features increases, causing neurites to readily cross bioactive borders. The photopolymerization method developed in this work to generate micro and nanopatterned materials serves as an additional surface engineering tool that enables investigation of cell-material interactions including directed de novo neurite growth. The results of this interdisciplinary effort contribute substantially to polymer neural regeneration technology and will lead to development of advanced biomaterials that improve neural prosthetic tissue integration and performance by spatially directing nerve growth.

The research presented in this thesis concerns the field of bioelectronics, in particular the work has been focused on the development of special electronic devices for neural signal acquisition and Peripheral Nervous System (PNS) stimulation. The final aim of the project in which this work is involved is in fact the realization of a prosthetic hand controlled using neural signals. The commercially available prosthesis are based on Electromyographic (EMG) signals, their use implies unnatural movements for the patient that needs a special training to develop the control capabilities over the mechanical limb. The proposed approach offers a number of advantages compared to the traditional prosthesis, first because the signals used are the same used to control the biologic limb, allowing a more comfortable solution for the patient that gets closer to feel the robotic hand as a natural extension of his/her body. Secondly, placing temperature and pressure sensors on the limb surface, it is possible to trasduce such information in an electrical current that, injected into the PNS, can restore the sensory feedback in amputees. The final goal of this research is the development of a fully implantable device able to perform a bidirectional communication between the robotic hand and the patient. Due to small area, low noise and low power constraints, the only possible way to reach this aim is the design of a full custom Integrated Circuit (IC). However a preliminary evaluation of the key design features, such as neural signal amplitudes and frequencies as well as stimulation shape parameters, is necessary in order to define clearly and precisely the design specifications. A low-cost and short implementation time device is then needed for this aim, the Components Off The Shelf (COTS) approach seems to be the best solution for this purpose. A Printed Circuit Board (PCB) with discrete components has been designed, developed and tested, the information extracted by the test results have been used to guide the IC design. The generation of electrical signals in biological cells, such as neural spikes, is possible thanks to ions that move across the cell membrane. In many applications it is important, not only to record the spikes, but also to measure these small currents in order to understand which electro-chemical processes are involved in the signal generation and to have a direct measurement of the ion channels involved in the reaction. Ion currents, in fact, play a key role in several physiological processes, in neural signal generation, but also in the maintenance of heartbeat and in muscle contraction. For this purpose, a system level implementation of a Read out circuit for ion channel current detection has been developed.

During the years, neuroprosthetic applications have obtained a great deal of attention by the international research, especially in the bioengineering field, thanks to the huge investments on several proposed projects funded by the political institutions which consider the treatment of this particular disease of fundamental importance for the global community. The aim of these projects is to find a possible solution to restore the functionalities lost by a patient subjected to an upper limb amputation trying to develop, according to physiological considerations, a communication link between the brain in which the significant signals are generated and a motor prosthesis device able to perform the desired action. Moreover, the designed system must be able to give back to the brain a sensory feedback about the surrounding world in terms of pressure or temperature acquired by tactile biosensors placed at the surface of the cybernetic hand. It in fact allows to execute involuntarymovements when for example the armcomes in contact with hot objects. The development of such a closed-loop architecture involves the need to address some critical issues which depend on the chosen approach. Several solutions have been proposed by the researches of the field, each one differing with respect to where the neural signals are acquired, either at the central nervous systemor at the peripheral one,most of themfollowing the former even that the latter is always considered by the amputees amore natural way to handle the artificial limb. This research work is based on the use of intrafascicular electrodes directly implanted in the residual peripheral nerves of the stump which represents a good compromise choice in terms of invasiveness and selectivity extracting electroneurographic (ENG) signals from which it is possible to identify the significant activity of a quite limited number of neuronal cells. In the perspective of the hardware implementation of the resulting solution which can work autonomously without any intervention by the amputee in an adaptive way according to the current characteristics of the processed signal and by using batteries as power source allowing portability, it is necessary to fulfill the tight constraints imposed by the application under consideration involved in each of the various phases which compose the considered closed-loop system. Regarding to the recording phase, the implementation must be able to remove the unwanted interferences mainly due to the electro-stimulations of themuscles placed near the electrodes featured by an order of magnitude much greater in comparison to that of the signals of interest amplifying the frequency components belonging to the significant bandwidth, and to convert them with a high resolution in order to obtain good performance at the next processing phases. To this aim, a recording module for peripheral neural signals will be presented, based on the use of a sigma-delta architecture which is composed by two main parts: an analog front-end stage for neural signal acquisition, pre-filtering and sigma-delta modulation and a digital unit for sigma-delta decimation and system configuration. Hardware/software cosimulations exploiting the Xilinx System Generator tool in Matlab Simulink environment and then transistor-level simulations confirmed that the system is capable of recording neural signals in the order of magnitude of tens of μV rejecting the huge low-frequency noise due to electromyographic interferences. The same architecture has been then exploited to implement a prototype of an 8-channel implantable electronic bi-directional interface between the peripheral nervous system and the neuro-controlled hand prosthesis. The solution includes a custom designed Integrated Circuit (0.35μm CMOS technology), responsible of the signal pre-filtering and sigma-delta modulation for each channel and the neural stimuli generation (in the opposite path) based on the directives sent by a digital control systemmapped on a low-cost Xilinx FPGA Spartan-3E 1600 development board which also involves the multi-channel sigma-delta decimation with a high-order band-pass filter as first stage in order to totally remove the unwanted interferences. In this way, the analog chip can be implanted near the electrodes thanks to its limited size avoiding to add a huge noise to theweak neural signals due to longwires connections and to cause heat-related infections, shifting the complexity to the digital part which can be hosted on a separated device in the stump of the amputeewithout using complex laboratory instrumentations. The system has been successfully tested from the electrical point of view and with in-vivo experiments exposing good results in terms of output resolution and noise rejection even in case of critical conditions. The various output channels at the Nyquist sampling frequency coming from the acquisition system must be processed in order to decode the intentions of movements of the amputee, applying the correspondent electro-mechanical stimulation in input to the cybernetic hand in order to perform the desired motor action. Different decoding approaches have been presented in the past, the majority of them were conceived starting from the relative implementation and performance evaluation of their off-line version. At the end of the research, it is necessary to develop these solutions on embedded systems performing an online processing of the peripheral neural signals. However, it is often possible only by using complex hardware platforms clocked at very high operating frequencies which are not be compliant with the low-power requirements needed to allow portability for the prosthetic device. At present, in fact, the important aspect of the real-time implementation of sophisticated signal processing algorithms on embedded systems has been often overlooked, notwithstanding the impact that limited resources of the former may have on the efficiency/effectiveness of any given algorithm. In this research work it has been addressed the optimization of a state-of-the-art algorithmfor PNS signals decoding that is a step forward for its real-time, full implementation onto a floating-point Digital Signal Processor (DSP). Beyond low-level optimizations, different solutions have been proposed at an high level in order to find the best trade-off in terms of effectiveness/efficiency. A latency model, obtained through cycle accurate profiling of the different code sections, has been drawn in order to perform a fair performance assessment. The proposed optimized real-time algorithmachieves up to 96% of correct classification on real PNS signals acquired through tf-LIFE electrodes on animals, and performs as the best off-line algorithmfor spike clustering on a synthetic cortical dataset characterized by a reasonable dissimilarity between the spikemorphologies of different neurons. When the real-time requirements are joined to the fulfilment of area and power minimization for implantable/portable applications, such as for the target neuroprosthetic devices, only custom VLSI implementations can be adopted. In this case, every part of the algorithmshould be carefully tuned. To this aim, the first preprocessing stage of the decoding algorithmbased on the use of aWavelet Denoising solution able to remove also the in-band noise sources has been deeply analysed in order to obtain an optimal hardware implementation. In particular, the usually overlooked part related to threshold estimation has been evaluated in terms of required hardware resources and functionality, exploiting the commercial Xilinx System Generator tool for the design of the architecture and the co-simulation. The analysis has revealed how the widely used Median Absolute Deviation (MAD) could lead o hardware implementations highly inefficient compared to other dispersion estimators demonstrating better scalability, relatively to the specific application. Finally, two different hardware implementations of the reference decoding algorithm have been presented highlighting pros and cons of each one of them. Firstly, a novel approach based on high-level dataflow description and automatic hardware generation is presented and evaluated on the on-line template-matching spike sorting algorithmwhich represents the most complex processing stage. It starts from the identification of the single kernels with the greater computational complexity and using their dataflow description to generate the HDL implementation of a coarse-grained reconfigurable global kernel characterized by theminimumresources in order to reduce the area and the energy dissipation for the fulfilment of the low-power requirements imposed by the application. Results in the best case have revealed a 71%of area saving compared tomore traditional solutions,without any accuracy penalty. With respect to single kernels execution, better latency performance are achievable stillminimizing the number of adopted resources. The performance in terms of latency can also be improved by tuning the implemented parallelismin the light of a defined number of channels and real-time constraints, by using more than one reconfigurable global kernel in order that they can be exploited to perform the same or different kernels at the same time in a parallel way, due to the fact that each one can execute the relative processing only in a sequential way. For this reason, a second FPGA-based prototype has been proposed based on the use of aMulti-Processor System-on-Chip (MPSoC) embedded architecture. This prototype is capable of respecting the real-time constraints posed by the application when clocked at less than 50 MHz, in comparison to 300 MHz of the previous DSP implementation. Considering that the application workload is extremely data dependent and unpredictable due to the sparsity of the neural signals, the architecture has to be dimensioned taking into account critical worst-case operating conditions in order to always ensure the correct functionality. To compensate the resulting overprovisioning of the system architecture, a software-controllable power management based on the use of clock gating techniques has been integrated in order tominimize the dynamic power consumption of the resulting solution. Summarizing, this research work can be considered a sort of proof-of-concept for the proposed techniques considering all the design issues which characterize each stage of the closed-loop system in the perspective of a portable low-power real-time hardware implementation of the neuro-controlled prosthetic device.

As the technology moves towards more natural human-machine interfaces (e.g. bionic limbs, teleoperation, virtual reality), it is necessary to develop a sensory feedback system in order to foster embodiment and achieve better immersion in the control system. Contemporary feedback interfaces presented in research use few sensors and stimulation units to feedback at most two discrete feedback variables (e.g. grasping force and aperture), whereas the human sense of touch relies on a distributed network of mechanoreceptors providing a wide bandwidth of information. To provide this type of feedback, it is necessary to develop a distributed sensing system that could extract a wide range of information during the interaction between the robot and the environment. In addition, a distributed feedback interface is needed to deliver such information to the user. This thesis proposes the development of a distributed sensing system (e-skin) to acquire tactile sensation, a first integration of distributed sensing system on a robotic hand, the development of a sensory feedback system that compromises the distributed sensing system and a distributed stimulation system, and finally the implementation of deep learning methods for the classification of tactile data. It’s core focus addresses the development and testing of a sensory feedback system, based on the latest distributed sensing and stimulation techniques. To this end, the thesis is comprised of two introductory chapters that describe the state of art in the field, the objectives, and the used methodology and contributions; as well as six studies that tackled the development of human-machine interfaces.

Thesis: Ph. D., Harvard-MIT Program in Health Sciences and Technology, 2018.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 86-94).
Humans have the ability to precisely sense the position, speed, and torque of their body parts. This sense is known as proprioception, and is essential to human motor control. In the many attempts to create human-mechatronic interactions, there is still no robust, repeatable methodology to reflect proprioceptive information from a synthetic device onto the nervous system. As a solution to this shortcoming, I present the agonist-antagonist myoneural interface (AMI). The AMI is comprised of 1) a surgical construct made up of two muscle-tendons - an agonist and an antagonist - surgically connected in series so that contraction of one muscle stretches the other, and 2) a bi-directional efferent-afferent neural control architecture. The AMI preserves dynamic muscle relationships that exist within native anatomy, thereby allowing proprioceptive signals from biological sensors within both muscles to be communicated to the central nervous system. Each AMI is designed to send control signals to one joint of a prosthesis, and to provide proprioceptive feedback pertaining to the movement of that joint. The doctoral work presented in this thesis constitutes the pre-clinical and early clinical validation of the AMI. The AMI concept is first described and validated in small (murine) and large (caprine) pre-clinical models. A detailed surgical methodology for implementation of the AMI during primary below-knee amputation is then described and evaluated in three human patients. Characterization of independent neural control of prosthetic joint position and impedance is presented for one AMI patient, as compared to a group of four persons with traditional amputation. Data are shown evidencing improved volitional control over the prosthesis in the AMI patient, as well as an emergence of natural reflexive behaviors during stair ambulation that do not exist in the traditional amputation cohort. These results provide a framework for reconsidering the integration of bionic systems with human physiology.
by Tyler R. Clites.
Ph. D.

Thesis: S.M., Massachusetts Institute of Technology, Department of Mechanical Engineering, 2019
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 87-88).
This thesis presents the design and evaluation of a neurally-controlled ankle-foot prosthesis optimized to enhance rock climbing ability in persons with transtibial amputation. The bionic rock climbing prosthesis restores biologic performance of the ankle-foot complex. The user volitionally controls the positions of both the prosthetic ankle and subtalar joints via input from electromyography surface electrodes worn on the residual limb. We hypothesize that a climbing specific robotic ankle-foot prosthesis will result in more biological emulation than a passive prosthesis. Specifically, we hypothesize that joint angles of the hip, knee, ankle, and subtalar of a person with transtibial amputation while rock climbing are are more similar to the joint angles of a height-, weight-, and ability-matched control subject with intact limbs, compared to climbing with a passive prosthesis. To test the hypothesis, a powered, 2-degree-of-freedom, neurally controlled prosthesis is built that comprises a pair of non-backdrivable linear actuators providing 16 degrees of dorsiflexion, 18 degrees of plantar flexion, and 20 degrees each of inversion and eversion. The prosthesis operates at a bandwidth and range of motion matching biological free-space motion of the ankle and subtalar joint. Climbing performance is evaluated by measuring joint angles and muscle activity during rock climbing with the robotic prosthesis and a traditional passive prosthesis, and comparing the kinematic data to that of a subject with intact biological limbs. We find that the bionic prosthesis brings the ankle and subtalar joint angles of the subject to more similar angles than the control subjects with intact biological limbs, compared to a standard passive prosthesis. These results indicate that a lightweight, actuated, 2-degree-of-freedom neurally-controlled robotic ankle-foot prosthesis restores biological function to the user during an extremely technical sport.
by Emily Rogers.
S.M.
S.M. Massachusetts Institute of Technology, Department of Mechanical Engineering

In this work we focus on pattern recognition methods related to EMG upper-limb prosthetic control. After giving a detailed review of the most widely used classification methods, we propose a new classification approach. It comes as a result of comparison in the Fourier analysis between able-bodied and trans-radial amputee subjects. We thus suggest a different classification method which considers each surface electrodes contribute separately, together with five time domain features, obtaining an average classification accuracy equals to 75% on a sample of trans-radial amputees. We propose an automatic feature selection procedure as a minimization problem in order to improve the method and its robustness.

Amputation is a life-changing event that results in a drastic reduction in quality of life including extreme loss of function and severe mental, emotional and physical pain. In order to mitigate these negative outcomes, there is great interest in the design of ‘advanced/robotic’ prosthetics that cosmetically and functionally mimic the lost limb. While the robotics side of advanced prosthetics has seen many advances recently, they still provide only a fraction of the natural limbs’ functionality. At the heart of the issue is the interface between the robotic limb and the individual that needs significant development. Amputees retain significant function in their nerves post-amputation, which offers a unique opportunity to interface with the peripheral nerve. Here we evaluate a relatively new approach to peripheral nerve interfacing by using microchannels, which hold the intrinsic ability to record larger neural signals from nerves than previously developed peripheral nerve interfaces. We first demonstrate that microchannel scaffolds are well suited for chronic integration with amputated nerves and promote highly organized nerve regeneration. We then demonstrate the ability to record neural signals, specifically action potentials, using microchannels permanently integrated with electrodes after chronic implantation in a terminal study. Together these studies suggest that microchannels are well suited for chronic implantation and stable peripheral nerve interfacing. As a next step toward clinical translation, we developed fully-integrated high electrode count microchannel interfacing technology capable of functioning while implanted in awake and freely moving animal models as needed for pre-clinical evaluation. Importantly, fabrication techniques were developed that apply to a broad range of flexible devices/sensors benefiting from flexible interconnects, surface mount device (SMD) integration, and/or operation in aqueous environments. Examples include diabetic glucose sensors, flexible skin based health monitors, and the burgeoning flexible wearable technology industry. Finally, we successfully utilized the fully integrated microchannel interfaces to record action potentials in the challenging awake and freely moving animal model validating the microchannel approach for peripheral nerve interfacing. In the end, the findings of these studies help direct and give significant credence to future technology development enabling eventual clinical application of microchannels for peripheral nerve interfacing.

Beginning with the introduction of electrically powered prostheses more than 65 years ago surface electromyographic (sEMG) signals recorded from residual muscles in amputated limbs have served as the primary source of upper-limb myoelectric prosthetic control. The majority of these devices use one or more neural interfaces to translate the sEMG signal amplitude into voltage control signals that drive the mechanical components of a prosthesis. In so doing, users are able to directly control the speed and direction of prosthetic actuation by varying the level of muscle activation and the associated sEMG signal amplitude. Consequently, in spite of decades of development, myoelectric prostheses are prone to highly variable functional control, leading to a relatively high-incidence of prosthetic abandonment among 23-35% of upper-limb amputees. Efforts to improve prosthetic control in recent years have led to the development and commercialization of neural interfaces that employ pattern recognition of sEMG signals recorded from multiple locations on a residual limb to map different intended movements. But while these advanced algorithms have made strident gains, there still exists substantial need for further improvement to increase the reliability of pattern recognition control solutions amongst the variability of muscle co-activation intensities. In efforts to enrich the control signals that form the basis for myoelectric control, I have been developing advanced algorithms as part of a next generation neural interface research and development, referred to as Motor Unit Drive (MU Drive), that is able to non-invasively extract the firings of individual motor units (MUs) from sEMG signals in real-time and translate the firings into smooth biomechanically informed control signals. These measurements of motor unit firing rates and recruitment naturally provide high-levels of motor control information from the peripheral nervous system for intact limbs and therefore hold the greater promise for restoring function for amputees. The goal for my doctoral work was to develop advanced algorithms for the MU Drive neural interface system, that leverage MU features to provide intuitive control of multiple degrees-of-freedom. To achieve this goal, I targeted 3 research aims: 1) Derive real-time MU-based control signals from motor unit firings, 2) Evaluate feasibility of motor unit action potential (MUAP) based discrimination of muscle intent 3) Design and evaluate MUAP-based motion Classification of motions of the arm and hand.

Cochlear implants (CIs) help to restore basic auditory function in patients who are deaf or have profound hearing loss. However, CI patients suffer from limited voice and tonal perception due to spatial separation between the stimulating CI electrode and the receptor spiral ganglion neurons (SGNs). Directed regeneration of proximate SGN axons may improve tonal performance and implant fidelity by decreasing the spatial separation between the CI electrode and the neural receptor. Additionally, fibrous scar tissue formation on the surface of implanted electrodes further decreases tonal perception through current attenuation and spreading resulting in late-term hearing loss. Thus, designing surfaces that induce favorable responses from neural tissues will be necessary in overcoming signal resolution barriers. In this work, the inherent spatial and temporal control of photopolymerization was used to functionalize surfaces with topographical and biochemical micropatterns that control the outgrowth of neural and other cell types. First, laminin, a cell adhesion protein was patterned using a photodeactivation process onto methacrylate polymer surfaces and was shown to direct the growth of spiral ganglion neurons (SGN), the primary auditory neural receptors. These protein patterns could even overcome low amplitude/high periodicity competing topographical cues. Additionally, glass substrates were patterned with linear zwitterionic polymers and fibroblasts, astrocytes, and Schwann cells all showed dramatically decreased cell adhesion on 100 µm precocity patterns. Further, SGN neurites showed excellent alignment to these same patterns. Next, poly(dimethyl siloxane) (PDMS) was coated with a crosslinked zwitterionic thin film using a single step photografting/photopolymerization process to covalently bind the hydrogel to PDMS. These coated surfaces showed dramatically lower levels of protein, cell, and bacterial adhesion. Finally, zwitterionic hydrogels were strengthened by changing the concentration of poly(ethylene glycol) diacrylate (PEGDA) and 2-hydroxyethyl methacrylate (HEMA) in the formulation. The direct relationship between changing zwitterionic hydrogel formulation to strengthen the hydrogel and the anti-fouling properties were established. The fundamental understanding and design of cochlear implant materials described herein serves as a foundation for the development of next generation neural prosthetics.

The following thesis project aims to study and realize a wearable manipulation system composed by an AR10 robotic hand, controlled via myoelectric signals and tactile sensors for prosthetic studies. The project starts with the kinematic study of the hand via MATLAB and Simulink, in order to obtain a complete insight on the robotic grasping device. Thereafter, a wearable support has been designed and printed to fix the robotic hand around the user forearm. Surface electromyography is acquired using a gForce gesture armband. A Simulink system has been developed to acquire and filter the signals, then the myoelectric data are elaborated to derive the command for the robotic hand. Tactile sensors are added by means of custom 3D-printed support on the fingertips in order to get a force feedback to allow the user to perform the grasp of different objects. Finally, in order to test the whole solution, a subject wearing the whole manipulation system carried out a series of tasks to evaluate the system’s usability during dynamic grasps of different objects. The results of the tests report the accuracy of the manipulation system. The main goal of the project is to test a wearable manipulation system made to be worn by intact subjects, in order to study prosthetic grasping scenarios that can provide results useful for future developments involving amputees.

This thesis studies the state-of-the-art in myoelectric control of active hand prostheses for people with trans-radial amputation using pattern recognition and machine learning techniques. Our work is supported by Centro Protesi INAIL in Vigorso di Budrio (BO). We studied the control system developed by INAIL consisting in acquiring EMG signals from amputee subjects and using pattern recognition methods for the classifcation of acquired signals, associating them with specifc gestures and consequently commanding the prosthesis. Our work consisted in improving classifcation methods used in the learning phase. In particular, we proposed a classifer based on a neural network as a valid alternative to the INAIL one-versus-all approach to multiclass classifcation.

Damage to the central nervous system (CNS) leads to the generation of an immune response which culminates with the encapsulation of the damaged area. The encapsulation, known as a glial scar, essentially breaks neural signal pathways and blocks signal transmissions to and from the CNS. The effect is the loss of motor and sensory control for the damaged individual. One method that has been used successfully to treat this problem is the use of a brain-machine interface (BMI) which can intercept signals from the brain and use these signals to control a machine. Although there are many types of BMI devices, implantable devices show the greatest promise with the ability to target specific areas of the CNS, with reduced noise levels and faster signal interception, and the fact that they can also be used to send signals to neurons. The largest problem that has plagued this type of BMI device is that the materials that have been used for their construction are not chemically resilient, elicit a negative biological response, or have difficulty functioning for extended periods of time in the harsh body environment. Many of these implantable devices experience catastrophic failure within weeks to months because of these negative factors. New materials must be examined to advance the future utilization of BMI devices to assist people with CNS damage or disease. We have proposed that two semiconductor materials, cubic silicon carbide (3C-SiC) and nanocrystalline diamond (NCD), which should provide solutions to the material biocompatibility problems experienced by implantable BMI devices. We have shown in this study that these two materials show chemical resilience to neuronal cellular processes, and we show evidence which indicates that these materials possess good biocompatibility with neural cell lines that, in the worst case, is comparable to celltreated polystyrene and, in most cases, even surpasses polystyrene. We have utilized 3C-SiC within an electrode device and activated the action potential of differentiated PC12 cells. This work details our initial efforts to modify the surfaces of these materials in order to improve cellular interaction and biocompatibility, and we examine our current and future work on improving our implantable BMI devices.

Providing suitable prosthetic sockets for the restoration of function following lower-limb amputation remains a significant issue in medical device prescription. Poorly designed sockets are associated with discomfort, poor quality function and injury, with quality linked to the capability of the socket to adequately distribute the forces from ambulation. Despite this link, systems of measuring stump-socket interface pressure have not seen use in clinical practice, in part due to limitations in functional performance. A technique using neural networks to relate external socket deformation to the internal pressure distribution was recently developed: this method has several advantages over contemporary systems but had not been evaluated in detail in dynamic situations. A wireless system estimating transtibial socket pressure distribution was produced. When supplied with simulated socket loads, an estimate produced from a group of networks (an ensemble) demonstrated improved accuracy and reduced variance. Work was undertaken to identify optimal design in terms of input data conditioning and post-estimate correction. This demonstrated that these can provide significant accuracy and reliability improvements. Measurements were taken from two transtibial amputees during standing, walking, walking on slopes, walking with coronal plane misalignment and walking with an alternative socket liner. An evaluation of the contributions to variance confirmed the applicability of ensembles in this application. The system proved capable recording significant differences in socket load distribution between different prosthesis configurations. For future investigation, this demonstrates that the technique is sensitive enough to examine the changes in the application of force which are present during daily use, device set-up and common fault conditions. The results of this study support further development of the practical aspects of the system, future work in producing a realistic load training system and extrapolation of results to other sockets, structures and engineering problems.

Le développement de prothèses du membre supérieur est couramment divisé en deux parties : la conception mécatronique de la prothèse et l'interface homme-machine dédiée à la commande. L'objectif de cette thèse est de rapprocher ces deux domaines afin de mieux les assortir. Une première étape concerne l'acquisition et le traitement des signaux de commande. Ainsi, une base de données comprenant des signaux électromyographiques et des coordonnées articulaires mesurées par vision par ordinateur a été réalisée. Un réseau de neurones artificiels réalise ensuite la reconstruction de la position de la main par exploitation des séquences électromyographiques. Une architecture de main sous-actionnée bio-inspirée est alors proposée afin de reproduire la cinématique de la main en garantissant une répartition de l'effort de préhension. Cette nouvelle approche consiste à optimiser l'imitation des synergies de la main liées à la saisie, permettant ainsi une commande plus naturelle pour les utilisateurs de prothèses actives
Research on upper-body prosthetic device is commonly divided in two categories: The prosthesis mechatronic conception and the human-machine interface dedicated to the control. This PhD thesis aims to bring together these two fields of research. The first step deals with control signals. Thus, a database containing electromyographic sequences and vision based joint coordinate measurements was created. Then, an artificial neural network achieves the motion estimation from electromyographic sequences. Accordingly, an under-actuated bio-inspired hand architecture is proposed to copy an organic hand motion while ensuring a grasping force distribution. This innovative approach allows to optimize the synergies imitation and proposes a control more intuitive for active prosthesis users

Neuroprosthetics enhance a variety of artificial devices that can beused to improve the quality of motor, sensory defects that emerge frombrain-injuries. Brain-Computer interface, as the name suggests,en compasses the interaction between brain and environment. The purpose of this thesis is to study the electrophysiological signals for controlling a prosthetic arm using fat intra-body communication(fat-IBC), in which the fat tissue inside the body is used as atransmission medium for low power microwaves. With the help of a standardized data-set of the recordings of UtahMicro-electrode Array, from BlackRock Microsystems, it was possible to study the different techniques of Spike Sorting which helped in mapping the recorded action potential to a neuron. In order to perform the published spike sorting process, signal analysis and machinelearning techniques were used to best quantize the neural signals. One objective of this thesis to determine the quality of isolation of neurons for which the spikes detection algorithm was implemented at acertain threshold level which was further classified using theclustering techniques to identify the putative neurons. The results of the classification of neurons indicated a drop in the isolationquality because of background noise or electrode drift. However, the framework used in this thesis could be used for future works with a different threshold value.