School of Computing, University of Utah, Salt Lake City, UT 84112, USA.
IEEE Trans Neural Syst Rehabil Eng. 2009 Oct;17(5):504-11. doi: 10.1109/TNSRE.2009.2029494. Epub 2009 Aug 7.
Over the past decade, research in the field of functional electrical stimulation (FES) has led to a new generation of high-electrode-count (HEC) devices that offer increasingly selective access to neural populations. Incorporation of these devices into research and clinical applications, however, has been hampered by the lack of hardware and software platforms capable of taking full advantage of them. In this paper, we present the first generation of a closed-loop FES platform built specifically for HEC neural interface devices. The platform was designed to support a wide range of stimulus-response mapping and feedback-based control routines. It includes a central control module, a 1100-channel stimulator, an array of biometric devices, and a 160-channel data recording module. To demonstrate the unique capabilities of this platform, two automated software routines for mapping stimulus-response properties of implanted HEC devices were implemented and tested. The first routine determines stimulation levels that produce perithreshold muscle activity, and the second generates recruitment curves (as measured by peak impulse response). Both routines were tested on 100-electrode Utah Slanted Electrode Arrays (USEAs) implanted in cat hindlimb nerves using joint torque or emg as muscle output metric. Mean time to map perithreshold stimulus level was 16.4 s for electrodes that evoked responses (n = 3200), and 3.6 s for electrodes that did not evoke responses (n = 1800). Mean time to locate recruitment curve asymptote for an electrode (n = 155) was 9.6 s , and each point in the recruitment curve required 0.87 s. These results demonstrate the utility of our FES platform by showing that it can be used to completely automate a typically time- and effort-intensive procedure associated with using HEC devices.
在过去的十年中，功能电刺激 (FES) 领域的研究导致了新一代高电极计数 (HEC) 设备的出现，这些设备为神经群体提供了越来越有选择性的访问。然而，将这些设备纳入研究和临床应用受到缺乏能够充分利用它们的硬件和软件平台的阻碍。在本文中，我们提出了专为 HEC 神经接口设备构建的第一代闭环 FES 平台。该平台旨在支持广泛的刺激-反应映射和基于反馈的控制例程。它包括一个中央控制模块、一个 1100 通道刺激器、一组生物识别设备和一个 160 通道数据记录模块。为了展示该平台的独特功能，实现并测试了两个用于映射植入的 HEC 设备的刺激-反应特性的自动化软件例程。第一个例程确定产生阈下肌肉活动的刺激水平，第二个例程生成募集曲线（如通过峰值脉冲响应测量）。这两个例程都在使用联合扭矩或肌电图作为肌肉输出指标的猫后肢神经中植入的 100 电极犹他斜电极阵列 (USEA) 上进行了测试。对于引发反应的电极 (n = 3200)，映射阈下刺激水平的平均时间为 16.4 秒，对于没有引发反应的电极 (n = 1800)，平均时间为 3.6 秒。对于一个电极 (n = 155) 找到募集曲线渐近线的平均时间为 9.6 秒，募集曲线上的每个点需要 0.87 秒。这些结果通过展示它可以用于完全自动化与使用 HEC 设备相关的通常时间和精力密集型过程，证明了我们的 FES 平台的实用性。
