Velocity Selective Neural Signal Recording Using a Space-Time Electrode Array

Abstract

Extracting the activity of a particular neural fiber from the extracellular recording of a peripheral nerve is quite important from different clinical perspectives. While traditional neural recording methods are unable to provide such granularity, new signal recording and processing techniques have offered promising solutions recently. A multi-electrode cuff in conjunction with a delay and sum beamforming structure has been used to detect the activity of different fibers in a nerve based on the propagation velocity of action potentials. However, as it is shown in this paper, simple delay and sum beamforming method encounters severe selectivity problem and signal distortion especially at higher velocities. In addition to scrutinizing the performance of the delay and sum beamformer and its inherent problems, here we propose a new beamforming method, based on broadband sensor array signal processing techniques, which exhibits much better selectivity with uniform frequency-velocity response. Our simulation results show how the new method can faithfully extract individual neural fiber activities and explore potential applications which could emerge from using such technique.

A Summary of Current and New Methods in Velocity Selective Recording (VSR) of Electroneurogram (ENG)

“…The placement of a bandpass filter (BPF) in each channel fixes f to the centre frequency of the BPF reducing Y to a function of velocity only (this issue was also examined in [7]). Finally, in order to quantify the velocity selectivity, we define a velocity quality factor, Qv, by analogy with linear systems in the frequency domain:…”

A model of electrical impedance tomography implemented in nerve-cuff for neural-prosthetics control

“…Researchers have used nerve cuffs to identify touch force and slip , estimate joint angle (Chan, Lin et al 2012), and as a sensor in closed loop functional electrical stimulation (FES) systems (Haugland and Sinkjaer 1995, Haugland, Lickel et al 1999, Inmann and Haugland 2004. While many researchers have shown it is possible to classify multiple concurrent signals based on the type of sensory information (Raspopovic, Carpaneto et al 2010, Rieger and Taylor 2013, Al-Shueli, Clarke et al 2014, Karimi and Seydnejad 2015, it has not been possible to differentiate more than two concurrent signals of the same type (Raspopovic, Carpaneto et al 2010, Zariffa 2015, which is a major barrier to their use in prosthetic limbs.…”

An implantable ENG detector with in-system velocity selective recording (VSR) capability

“…A very powerful method for implementing VSR using multi-electrode cuffs (MECs) has been described recently [3], [4]. In addition refs [5] - [8] and describe, respectively, current VSR methods and their limitations theoretically and experimentally. The current paper describes very significant improvements to current practices in neural recording allowing the recording of natural (physiological) ENG (see e.g.…”

“…The current paper describes very significant improvements to current practices in neural recording allowing the recording of natural (physiological) ENG (see e.g. [8], [9]). These improvements include an interleaved analogue to digital conversion system providing increased sampling rate and improved digital processing creating the opportunity to incorporate the signal processing required for VSR into the implantable system.…”