Model-based ankle joint angle tracing by cuff electrode recordings of peroneal and tibial nerves

Abstract

The main goal of the present study was to estimate the ankle joint angle from the peroneal and tibial electroneurography (ENG) recordings. Two single-channel cuff electrodes for recording ENG were placed on the proximal part of rabbit peroneal and tibial nerves respectively and static positioning and ramp-and-hold stretches were performed to characterize the static and dynamic ENG responses. An ENG model, consisting of static and dynamic parts, was constructed to relate ENG to ankle angle trajectory and an inverse ENG model was derived to predict ankle angle. The results showed that the new model could accurately estimate large-range ankle angles during and after ramp-and-hold movements. Our study provides a basis for implementing joint angle tracing without using artificial angle sensors.

An Implantable Wireless Neural Interface System for Simultaneous Recording and Stimulation of Peripheral Nerve with a Single Cuff Electrode

“…The ENG signals were properly amplified and filtered with a high signal-to-noise ratio of 5.46 dB and were then wirelessly transmitted at 10 kHz sampling rate from the implantable device to the external device. The recorded ENG signals from both peroneal and tibial nerves corresponded to the results obtained in the previous studies [ 44 , 45 , 46 ]. During neural signal recording with the passive movement of the rabbit’s ankle, the stimulation path is completely disconnected by the cuff-electrode path controller.…”

“…An anesthetic mixed with Zoletil 50 (Virbac, Carros, France) and Rompun (Bayer, Seoul, Korea) was used for anesthesia. After removing the hair on the thigh of the animal, approximately an 3 cm incision on the rear side of the left hind limb was made to expose the sciatic nerve through the avascular fascicular junction [ 44 ]. Through the incision, the tibial and peroneal nerves were gently exposed; then, the two nerves were carefully separated to ensure space for the installation of the two cuff electrodes (see Figure 8 ).…”

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

“…Therefore, in most cases, the neural activity recorded in this way has been used for onset detection, for example for the control of a 1-DoF hand prosthesis (Stein et al, 1980) or of FES-systems in hemiplegics Hoffer et al (2005). Even if these limits can be partly overcome by using multi-site cuff electrodes (Yoo and Durand, 2005) and advanced processing techniques (Micera et al, 2001;Cavallaro et al, 2003;Lin et al, 2007;Tesfayesus and Durand, 2007), more selective PNS interfaces may be necessary to access more specific information. In fact, higher selectivity interfaces make possible the identification of single spikes from single axons (or a small group of axons) and to access the natural frequency coded information (Micera et al, 2006) in ENG signals.…”

Ultra-low noise miniaturized neural amplifier with hardware averaging

“…This approach improves the noise performance of existing acquisition devices and an alternative of implementing a customized ASIC design flow for similar applications. The noise levels achieved with this design are comparable to those obtained using either an input transformer [11,16,18,29,31], or a low, voltage-noise solid state amplifier [24,28] for single-channel whole nerve recording and contact impedances less than 1 KΩ.…”

“…Considering the two types of contacts used in the FINE, the thermal noise generated by the contacts equal to e k T Z 4 T = and denoted by e T(we) and e T(re) . For noise analysis of multiple parallel input devices connected to a FINE (figure 2(B)), the total input referred noise is given by (equation(24) in[21]):…”