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

Abstract

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.

On the use of wavelet denoising and spike sorting techniques to process electroneurographic signals recorded using intraneural electrodes

“…Conversely, information from the nervous system could be retrieved by recording the electrical activity of the nerve. Given a chronically stable device acting on an appropriate set of nerve fibers, such an interface could be used as part of a FES (functional electrical stimulation) system to restore function to paralyzed limbs (Popović et al, 1993) or in a brain-controlled robotic limb application (Micera et al, 2006).…”

Intrafascicular stimulation of monkey arm nerves evokes coordinated grasp and sensory responses

“…FES-based prostheses can enable paralyzed individuals to grasp objects with a few simple grips, or even enable paraplegic individuals to walk a short distance in conjunction with external support. However, FES systems can be fatiguing and relatively difficult to use because they typically activate near-maximal contractions, preferentially activate fatigable motor units, and provide no somatosensory or proprioceptive sensory feedback (Popovic et al 1993;Spadone et al 2003).…”

Double-Differential Recording and AGC Using Microcontrolled Variable Gain ASIC

“…We enumerate the gain steps by and let and be the initial balancing gains established using (3). Then the required settings are (6) denotes a relative gain variation per step. The rate of change per step described by (6) depends on and and is therefore generally different for and .…”

“…The recording of the electrocardiogram (ECG) [2] or electromyogram (EMG) [3]- [5], the detection and localization of brain activity [6], [7] and recording of the electroneurogram (ENG) [8]- [10] as part of a monitoring system [5], [6], [10] are typical examples. The bandwidth of the signals is low, varying from tens of hertz for the ECG to a few kilo-hertz for EMG and ENG, while the voltage amplitudes at the recording site are small, on the order of millivolts or less.…”