arrow
返回

xDev: a mixed-signal, software-defined neurotechnology interface platform for accelerated system development

delete2025-04-01
delete0
delete
OA
AI
S
Samuel R Parker
X
X. Lee
J
Jonathan S. Calvert
D
David A. Borton *
DOI:10.1088/1741-2552/adb7bfdelete
delete原文链接
delete分享
delete收藏
查看原文
摘要

摘要

En 中文
Objective. Advances in electronics and materials science have led to the development of sophisticated components for clinical and research neurotechnology systems. However, instrumentation to easily evaluate how these components function in a complete system does not yet exist. In this work, we set out to design and validate a software-defined mixed-signal routing fabric, 'xDev', that enables neurotechnology system designers to rapidly iterate, evaluate, and deploy advanced multi-component systems. Approach. We developed a set of system requirements for xDev, and implemented a design based on a 16 x 16 analog crosspoint multiplexer. We then tested the impedance and switching characteristics of the design, assessed signal gain and crosstalk attenuation across biological and high-speed digital signaling frequencies, and evaluated the ability of xDev to flexibly reroute microvolt-scale amplitude and high-speed signals. Finally, we conducted an intraoperative in vivo deployment of xDev to rapidly conduct neuromodulation experiments using diverse neurotechnology submodules. Main results. The xDev system impedance matching, crosstalk attenuation, and frequency response characteristics accurately transmitted signals over a broad range of frequencies, encapsulating features typical of biosignals and extending into high-speed digital ranges. Microvolt-scale biosignals and 600 Mbps Ethernet connections were accurately routed through the fabric. These performance characteristics culminated in an in vivo demonstration of the flexibility of the system via implanted spinal electrode arrays in an ovine model. Significance. xDev represents a first-of-its-kind, low-cost, software-defined neurotechnology development accelerator platform. Through the public, open-source distribution of our designs, we lower the obstacles facing the development of future neurotechnology systems.
Keyword:
neurotechnology
neural interfaces
system development
system integration
AI总结

AI总结

对已上传原文的论文进行重点信息的提取,主要内容包括:简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。

期刊

Journal of Neural Engineering 封面图
Journal of Neural Engineering
IF:
3.8
论文数:
4.0K
被引数:
1.4W

机构

U
US Department of Veterans Affairs
学者数:
3.8W
论文数: 3.3W
被引数: 47
V
veterans health administration (vha)
学者数:
2.6W
论文数: 2.1W
被引数: 40
引用论文

引用论文

暂无论文信息