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 complete implanted wireless solution with fully integrated circuit architecture. A recording experiment comparing in parallel a commercial recording system (Tucker-Davis Technology (TDT)) and the UFs 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.
[1]
Reid R. Harrison,et al.
A low-power, low-noise CMOS amplifier for neural recording applications
,
2002,
2002 IEEE International Symposium on Circuits and Systems. Proceedings (Cat. No.02CH37353).
[2]
Yuan Li,et al.
Asynchronous biphasic pulse signal coding and its CMOS realization
,
2006,
2006 IEEE International Symposium on Circuits and Systems.
[3]
David M. Santucci,et al.
Learning to Control a Brain–Machine Interface for Reaching and Grasping by Primates
,
2003,
PLoS biology.
[4]
M S Lewicki,et al.
A review of methods for spike sorting: the detection and classification of neural action potentials.
,
1998,
Network.