SENSORY NERVE RECORDING FOR CLOSED-LOOP CONTROL TO RESTORE MOTOR FUNCTIONS
SENSORY NERVE RECORDING FOR CLOSED-LOOP CONTROL TO RESTORE MOTOR FUNCTIONS
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DOI：
10.1109/10.247801
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发表时间：
1993-10-01
影响因子：
4.6
通讯作者：
ARMSTRONG, WW
中科院分区：
文献类型：
--
作者：
POPOVIC, DB;STEIN, RB;ARMSTRONG, WW
开发了一种用于使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。在慢性猫中进行实验，目标是设计基于规则的控制器以在跑步机运动期间产生踝关节的有节奏运动。用袖带电极记录来自胫神经和腓浅神经的神经信号，并与来自猫后肢中的踝屈肌和伸肌的肌肉信号同时处理。袖带电极是一种有效的方法，用于在周围神经中进行长期慢性记录，而不会对神经造成不适或损伤。对于实时操作，我们设计了一个具有消隐电路的低噪声放大器，以最大限度地减少刺激伪影。我们使用阈值检测来设计一个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鲁棒，以适应神经记录的变化。用于本研究的自适应逻辑网络是有效的映射传递函数，因此适用于确定步态不变量用于闭环控制的FES系统。简单的规则基础可能会被选择用于人类患者的初始应用。然而，更复杂的FES应用程序需要更复杂的规则库和更好的连续神经记录和肌肉活动的映射。自适应神经网络有望用于这些更复杂的应用。
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.