By restoring the ability to move and communicate with the world, brain machine interfaces (BMIs) offer the potential to improve quality of life for people suffering from spinal cord injury, stroke, or neurodegenerative diseases, such as amyotrophic lateral sclerosis (ALS). BMIs attempt to translate measured neural signals into the user's intentions and, subsequently, control a computer or actuator. Recently, compelling examples of intra-cortical BMIs have been demonstrated in tetraplegic patients. Although these studies provide a powerful proof-of-concept, clinical viability is impeded by limited performance and robustness over short (hours) and long (days) timescales. We address performance and robustness over short time periods by approaching BMIs as a systems level design problem. We identify key components of the system and design a novel BMI from a feedback control perspective. In this perspective, the brain is the controller of a new plant, defined by the BMI, and the actions of this BMI are witnessed by the user. This simple perspective leads to design advances that result in significant qualitative and quantitative performance improvements. Through online closed loop experiments, we show that this BMI is capable of producing continuous endpoint movements that approach native limb performance and can operate continuously for hours. We also demonstrate how this system can be operated across days by a bootstrap procedure with the potential to eliminate an explicit recalibration step. To examine the use of BMIs over longer timescales, we develop new electrophysiology tools that allow for continuous multi-day neural recording. Through application of this technology, we measure the signal acquisition stability (and instability) of the electrode array technology used in current BMI clinical trials. We also demonstrate how these systems can be used to study BMI decoding over longer time periods. In this demonstration, we present a simple methodology for switching BMI systems on and off at appropriate times. The algorithms and methods demonstrated can be run with existing low power application specific integrated circuits (ASICs), with a defined path towards the development of a fully implantable neural interface system. We believe that these advances are a step towards clinical viability and, with careful user interface design, neural prosthetic systems can be translated into real world solutions.