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abstract: In the last 15 years, there has been a significant increase in the number of motor neural prostheses used for restoring limb function lost due to neurological disorders or accidents. The aim of this technology is to enable patients to control a motor prosthesis using their residual neural pathways (central or peripheral). Recent studies in non-human primates and humans have shown the possibility of controlling a prosthesis for accomplishing varied tasks such as self-feeding, typing, reaching, grasping, and performing fine dexterous movements. A neural decoding system comprises mainly of three components: (i) sensors to record neural signals, (ii) an algorithm to map neural recordings to upper limb kinematics and (iii) a prosthetic arm actuated by control signals generated by the algorithm. Machine learning algorithms that map input neural activity to the output kinematics (like finger trajectory) form the core of the neural decoding system. The choice of the algorithm is thus, mainly imposed by the neural signal of interest and the output parameter being decoded. The various parts of a neural decoding system are neural data, feature extraction, feature selection, and machine learning algorithm. There have been significant advances in the field of neural prosthetic applications. But there are challenges for translating a neural prosthesis from a laboratory setting to a clinical environment. To achieve a fully functional prosthetic device with maximum user compliance and acceptance, these factors need to be addressed and taken into consideration. Three challenges in developing robust neural decoding systems were addressed by exploring neural variability in the peripheral nervous system for dexterous finger movements, feature selection methods based on clinically relevant metrics and a novel method for decoding dexterous finger movements based on ensemble methods. / Dissertation/Thesis / Doctoral Dissertation Bioengineering 2017

In response to high and rising amputation rates, significant advances have been made in the field of prosthetic limb design. Unfortunately, there exists a lag in the neural interfacing technology required to provide an adequate link between the nervous system and this emerging generation of advanced prosthetic devices. Novel approaches to peripheral nerve interfacing are required to establish the stable, high channel count connections necessary to provide natural, thought driven control of an external prosthesis. Here, a tissue engineering-based approach has been used to create a device capable of interfacing with a regenerated portion of amputated nerve. As part of this work, a nerve guidance channel design, in which small amounts of interior scaffolding material could be precisely positioned, was evaluated. Guidance channels containing a single thin-film sheet of aligned scaffolding were shown to support robust functional nerve regeneration across extended injury gaps by minimally supplementing natural repair mechanisms. Significantly, these "thin-film enhanced nerve guidance channels" also provided the capability to guide the course of axons regenerating from a cut nerve. This capability to control axonal growth was next leveraged to create "regenerative scaffold electrodes (RSEs)" able to interface with axons regenerating from an amputated nerve. In the RSE design, low-profile arrays of interfacing electrodes were embedded within layers of aligned scaffolding material, such that regenerating axons were topographically guided by the scaffolding through the device and directly across the embedded electrodes. Chronically implanted RSEs were successfully used to record evoked neural activity from amputated nerves in an animal model. These results demonstrate that the use of topographic cues within a nerve guidance channel might offer the potential to influence the course of nerve regeneration to the advantage of a peripheral nerve interface suitable for limb amputees.