A neuronal recording system for brain-machine interfaces (BMI) based on asynchronous biphasic pulse coding is described. It demonstrates the first step in the development of a completely implanted wireless solution with a fully integrated circuit architecture. A recording experiment comparing, in parallel, a commercial recording system (Tucker-Davis Technology (TDT)) and the UF's custom solution (FWIRE) is set up to compare performance. The novel aspect of the UF system is that the analog signal is represented by an asynchronous pulse train, which provides a low-power, low-bandwidth, noise-resistant means for coding and transmission. Taking advantage of neural firing features, the pulse-based approach uses only 3K pulses/second to record a 25 kHz bandwidth signal from a hardware neural simulator.
['Sheng-Feng Steve Yen', 'Jie Xu', 'Manu Rastogi', 'John G. Harris', 'Jose C. Principe', 'Justin C. Sanchez']
A biphasic integrate-and-fire system