Department of Rehabilitation Medicine, University of Kansas Medical Center, 3901 Rainbow Boulevard, Kansas City, 66160 KS, United States of America. Bioengineering Graduate Program, University of Kansas, Lawrence, KS, United States of America.
J Neural Eng. 2019 Oct 29;16(6):066022. doi: 10.1088/1741-2552/ab3319.
Activity-dependent stimulation (ADS) is designed to strengthen the connections between neuronal circuits and therefore may be a promising tool for promoting neurophysiological reorganization following a brain injury. To successfully perform this technique, two criteria must be met: (1) spikes in the extracellular electrical field potential must be detected accurately at one site of interest, and (2) stimulation pulses generated at fixed (<1 ms jitter), low-latency (<10 ms) intervals relative to each detected spike must be delivered reliably to a second site of interest. Here, we aimed to improve noise rejection in a low-cost commercial system to reliably perform ADS in awake, behaving rats, while maintaining latency requirements.
We implemented a spike detection state machine on a field-programmable gate array (FPGA). Because the accuracy of spike detection can be heavily reduced in awake and behaving animals due to biological artifacts such as movement and chewing, the state machine tracks candidate spike waveforms, checking them against multiple programmable thresholds and rejecting any spikes that fail to meet a programmed threshold criterion.
A series of offline analyses showed that our implementation was able to appropriately trigger stimulation during epochs of biological artifacts with an overall accuracy between 72% and 97%, fixed computational latency of 167 µs, and an algorithmic latency of 300 µs to 800 µs.
Our improvements have been made open-source and are freely available to all scientists working on closed-loop neuroprosthetic devices. Importantly, the improvements are easily incorporated into existing workflows that utilize the Intan Stimulation and Recording Controller.
活动依赖性刺激（ADS）旨在增强神经元回路之间的连接，因此可能是促进脑损伤后神经生理重组的一种有前途的工具。要成功执行此技术，必须满足两个条件：（1）必须准确地在一个感兴趣的部位检测到细胞外电场电位中的尖峰，（2）必须以固定的（<1 ms 抖动）、低延迟（<10 ms）间隔相对于每个检测到的尖峰可靠地将刺激脉冲输送到第二个感兴趣的部位。在这里，我们旨在提高低成本商业系统中的噪声抑制能力，以便在保持潜伏期要求的情况下，在清醒、行为活跃的大鼠中可靠地执行 ADS。
我们在现场可编程门阵列（FPGA）上实现了一个尖峰检测状态机。由于由于运动和咀嚼等生物伪影，在清醒和行为活跃的动物中，尖峰检测的准确性可能会大大降低，状态机跟踪候选尖峰波形，对照多个可编程阈值进行检查，并拒绝任何未达到编程阈值标准的尖峰。
一系列离线分析表明，我们的实现能够在生物伪影的时段内适当触发刺激，总体准确性在 72%到 97%之间，固定计算延迟为 167 µs，算法延迟为 300 µs 到 800 µs。
我们的改进已经开源，并免费提供给所有从事闭环神经假肢设备研究的科学家。重要的是，改进可以轻松地纳入现有的工作流程中，这些工作流程利用 Intan 刺激和记录控制器。
