An ultra low power implantable neural recording system for brain-machine interfaces

In the past few decades, direct recordings from different areas of the brain have enabled scientists to gradually understand and unlock the secrets of neural coding. This scientific advancement has shown great promise for successful development of practical brain-machine interfaces (BMIs) to restore lost body functions to patients with disorders in the central nervous system. Practical BMIs require the uses of implantable wireless neural recording systems to record and process neural signals, before transmitting neural information wirelessly to an external device, while avoiding the risk of infection due to through-skin connections. The implantability requirement poses major constraints on the size and total power consumption of the neural recording system. This thesis presents the design of an ultra-low-power implantable wireless neural recording system for use in brain-machine interfaces. The system is capable of amplifying and digitizing neural signals from 32 recording electrodes, and processing the digitized neural data before transmitting the neural information wirelessly to a receiver at a data rate of 2.5 Mbps. By combining state-of-the-art custom ASICs, a commercially-available FPGA, and discrete components, the system achieves excellent energy efficiency, while still offering design flexibility during the system development phase. The system's power consumption of 6.4 mW from a 3.6-V supply at a wireless output data rate of 2.5 Mbps makes it the most energy-efficient implantable wireless neural recording system reported to date. The system is integrated on a flexible PCB platform with dimensions of 1.8 cm x 5.6 cm and is designed to be powered by an implantable Li-ion battery. As part of this thesis, I describe the design of low-power integrated circuits (ICs) for amplification and digitization of the neural signals, including a neural amplifier and a 32-channel neural recording IC. Low-power low-noise design techniques are utilized in the design of the neural amplifier such that it achieves a noise efficiency factor (NEF) of 2.67, which is close to the theoretical limit determined by physics. The neural recording IC consists of neural amplifiers, analog multiplexers, ADCs, serial programming interfaces, and a digital processing unit. It can amplify and digitize neural signals from 32 recording electrodes, with a sampling rate of 31.25 kS/s per channel, and send the digitized data off-chip for further processing. The IC was successfully tested in an in-vivo wireless recording experiment from a behaving primate with an average power dissipation per channel of 10.1 pW. Such a system is also widely useful in implantable brain-machine interfaces for the blind and paralyzed, and in cochlea implants for the deaf. Thesis Supervisor: Rahul Sarpeshkar, Ph.D. Title: Associate Professor of Electrical Engineering