SENSORY NERVE RECORDING FOR CLOSED-LOOP CONTROL TO RESTORE MOTOR FUNCTIONS

A method is developed for using neural recordings to control functiona l 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 lo comotion. Neural signals from the tibial and superficial peroneal nerv es were recorded with cuff electrodes and processed simultaneously wit h muscular signals from ankle flexors and extensors in the cat's hind limb. Cuff electrodes are an effective method for long-term chronic re cording in peripheral nerves without causing discomfort or damage to t he nerve. For real-time operation we designed a low-noise amplifier wi th a blanking circuit to minimize stimulation artifacts. We used thres hold 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 acc ommodate the variability in neural recordings. The adaptive logic netw ork 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 proba bly be chosen for initial applications to human patients. However, mor e complex FES applications require more complex rule-bases and better mapping of continuous neural recordings and muscular activity. Adaptiv e neural networks have promise for these more complex applications.