Design and characterization of an integrate-and-fire neural recording system

A neuronal recording system for brain-machine interfaces (BMI) based on asynchronous biphasic pulse coding is described. A recording experiment comparing, in parallel, a commercial recording system (Tucker-Davis Technology) 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. Based on different front-end hardware settings, recording bandwidth and corresponding reconstruction accuracy can be varied. Taking advantage of neural firing features, the pulse-based approach requires less than 3 K pulses/second to record a 25 KHz bandwidth signal from a hardware neural simulator. Recording performance has been characterized in the back-end signal processing with the spike sorting method. Two different spike sorting methods are proposed depending on different recording bandwidth constraints.