Adaptive neural matching online spike sorting VLSI chip design for wireless BCI implants
Zaghloul Saad Zaghloul, Magdy A. Bayoumi
Abstract
Controlling the surrounding world by just the power of our thoughts has always seemed to be just a fictional dream. With recent advancements in technology and research, this dream has become a reality for some through the use of a Brain Computer/Machine Interface (BCI/BMI). One of the most important goals of BCI is to enable handicap people to control artificial limbs. Some research proposed wireless implants that do not require chronic wound in the skull. However, the communications consume a high bandwidth and power that exceeds the allowed limits, 8–10mW. This study proposes and implements a modified version of real-time spike sorting for wireless BCI [4] that simplifies and uses less computation via an adaptive neural-structure; which makes it simpler, faster and power and area efficient. The system was implemented, and simulated using Modalism and Cadence, with ideal case and worst case accuracy of 100% and 91.7%, respectively. Also, the chip layout of 0.704mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> , with power consumption of 4.7mW and was synthesized on 45nm technology using Synopsys.
BibTeX
@inproceedings{icassp2015_adaptiveneuralma,
title = {Adaptive neural matching online spike sorting VLSI chip design for wireless BCI implants},
author = {Zaghloul Saad Zaghloul and Magdy A. Bayoumi},
booktitle = {ICASSP 2015},
year = {2015}
}