Popović D B, Stein R B, Jovanović K L, Dai R, Kostov A, Armstrong W W
Division of Neuroscience, University of Alberta, Edmonton, AB, Canada.
IEEE Trans Biomed Eng. 1993 Oct;40(10):1024-31. doi: 10.1109/10.247801.
A method is developed for using neural recordings to control functional electrical stimulation (FES) to nerves and muscles. Experiments were done in chronic cats with a goal of designing a rule-based controller to generate rhythmic movements of the ankle joint during treadmill locomotion. Neural signals from the tibial and superficial peroneal nerves were recorded with cuff electrodes and processed simultaneously with muscular signals from ankle flexors and extensors in the cat's hind limb. Cuff electrodes are an effective method for long-term chronic recording in peripheral nerves without causing discomfort or damage to the nerve. For real-time operation we designed a low-noise amplifier with a blanking circuit to minimize stimulation artifacts. We used threshold detection to design a simple rule-based control and compared its output to the pattern determined using adaptive neural networks. Both the threshold detection and adaptive networks are robust enough to accommodate the variability in neural recordings. The adaptive logic network used for this study is effective in mapping transfer functions and therefore applicable for determination of gait invariants to be used for closed-loop control in an FES system. Simple rule-bases will probably be chosen for initial applications to human patients. However, more complex FES applications require more complex rule-bases and better mapping of continuous neural recordings and muscular activity. Adaptive neural networks have promise for these more complex applications.
开发了一种利用神经记录来控制对神经和肌肉的功能性电刺激（FES）的方法。在慢性猫身上进行了实验，目的是设计一种基于规则的控制器，以在跑步机运动期间产生踝关节的节律性运动。用袖带电极记录来自胫神经和腓浅神经的神经信号，并与猫后肢踝关节屈肌和伸肌的肌肉信号同时进行处理。袖带电极是在外周神经中进行长期慢性记录的有效方法，不会对神经造成不适或损伤。为了进行实时操作，我们设计了一种带有消隐电路的低噪声放大器，以尽量减少刺激伪迹。我们使用阈值检测来设计一种简单的基于规则的控制，并将其输出与使用自适应神经网络确定的模式进行比较。阈值检测和自适应网络都足够强大，能够适应神经记录中的变异性。本研究中使用的自适应逻辑网络在映射传递函数方面是有效的，因此适用于确定用于FES系统闭环控制的步态不变量。对于最初应用于人类患者的情况，可能会选择简单的规则库。然而，更复杂的FES应用需要更复杂的规则库以及对连续神经记录和肌肉活动的更好映射。自适应神经网络在这些更复杂的应用中有前景。
