The development of advanced neuroprosthetic systems and brain-machine interfaces for high-capacity, real-time, 2-way communication with the nervous system is a major challenge to the emerging neural engineering discipline. Recent advances in the fabrication of high-density microelectrode arrays for recording and stimulation of multiple neuronal cell populations have triggered numerous neurophysiological discoveries. Nevertheless, the success of these devices is mitigated in part by their current communication and signal processing capabilities. Data transmission in real-time from a high-density neural implant would require an ultra-high bandwidth telemetry link. Pre-processing neural signals is accordingly sought to be implemented as close as possible to where the signal is acquired to infer the """"""""useful"""""""" information early in the data stream and to reduce the computational and communication costs. The proposed project is aimed at developing a highly scalable modular microsystem capable of processing neural signals in real-time and achieving large compression ratios while maintaining highest signal fidelity. The project has 3 aims.
The first aim i s to design adequate array signal processing algorithms to infer the useful information in the neural signals prior to extra-cutaneous transmission. Once optimized, the second aim is to embed these algorithms onto a custom designed hardware platform for the purpose of preconditioning the neural signals. The module will be designed for intra-cranial implantation, thus will be optimized for minimum power dissipation and form factor. This module will serve as the front-end stage of a distributed microsystem aimed at interfacing multiple neuronal populations in cortical structures of interest. Adaptation of the algorithms will be assessed in aim 3 with rigorous testing using conditioned offline recordings to mimic adverse conditions in long term chronic experiments. ? ?
