Successful use of microelectrode arrays to record neural activity in the cortex has opened new opportunities for scientists to decode the intricate functionality of the human brain and the behavior of neurons that enable its complex operation. The resulting brain-machine interface devices play a critical role in enabling patients with neural disorders to achieve a better lifestyle. Such interfaces provide a direct interface to the brain and show great promise in many biomedical applications.This thesis explores some of the major obstacles impeding the advance of wireless neural implants and addresses them through development of highly efficient algorithms and implantable hardware. An overwhelming amount of data is generated by the microelectrode arrays, resulting in a data bandwidth bottleneck. To overcome this problem, an implantable system has been devised to enable control over the amount of data that must be transmitted without compromising the information contained in the array of neural signals. Furthermore, the nature of the wireless communication channel across the skin tissue is not well characterized. In this thesis, solutions have been developed to maximize that data throughput and enable unfailing yet low-power communication of bidirectional data between the implanted device and the external world. Finally, a unified energy-efficient, implantable CMOS integrated circuit was developed to address these two critical problems. The resulting integrated solution ensures seamless multi-modal operation, and thus establishes a pathway to the design of next-generation neuroprosthetics devices. Although the motivation for this thesis comes from the field of neuroprosthetics, the solutions devised are pertinent to a wide range of implantable applications.
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