Sensory nerve recording for closed-loop control to restore motor functions

• Published in: IEEE transactions on biomedical engineering Vol. 40; no. 10; pp. 1024 - 1031
• Main Authors: Popovic, D.B., Stein, R.B., Jovanovic, K.L., Dai, R., Kostov, A., Armstrong, W.W.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.10.1993; Institute of Electrical and Electronics Engineers
• Subjects: Action Potentials; Adaptive control; Adaptive systems; Animals; Ankle Joint - physiology; Biological and medical sciences; Cats; Diseases of the nervous system; Electric Stimulation Therapy; Electrodes; Feedback; Female; Male; Medical sciences; Movement - physiology; Muscles; Neural networks; Neuromuscular stimulation; Neurons, Afferent - physiology; Paralysis - therapy; Peroneal Nerve - physiology; Programmable control; Radiotherapy. Instrumental treatment. Physiotherapy. Reeducation. Rehabilitation, orthophony, crenotherapy. Diet therapy and various other treatments (general aspects); Signal processing; Signal restoration; Tibial Nerve - physiology; Electrical signal; Fissipedia; Closed loop; Peripheral nerve; Peroneal nerve; Carnivora; Tibial nerve; Electrical activity; Electric; Lower limb; Nervous system; Experimental study; Striated muscle; Ankle; Instrumental stimulation; Vertebrata; Mammalia; Animal; Cat; Signal processing; Recording; Monitoring control system; Biomedical engineering
• DOI: 10.1109/10.247801
• ISSN: 0018-9294

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 the authors designed a low-noise amplifier with a blanking circuit to minimize stimulation artifacts. They 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.< >