Hierarchical beamformer and cross-talk reduction in electroneurography

Abstract

Electroneurography (ENG) is a method of recording neural activity within nerves. Using nerve electrodes with multiple contacts the activation patterns of individual neuronal fascicles can be estimated by measuring the surface voltages induced by the intraneural activity. The information about neuronal activation can be used for functional electric stimulation (FES) of patients suffering of spinal chord injury, or to control a robotic prosthetic limb of an amputee. However, the ENG signal estimation is a severely ill-posed inverse problem due to uncertainties in the model, low resolution due to limitations of the data, geometric constraints, and the diffculty to separate the signal from biological and exogenous noise. In this article, a reduced computational model for the forward problem is proposed, and the ENG problem is addressed by using beamformer techniques. It is shown that the beamformer algorithm can be interpreted as a version of the classical Backus-Gilbert algorithm. Furthermore, we show that using a hierarchical statistical model, it is possible to develop an adaptive beamformer algorithm that estimates directly the source variances rather than the voltage source itself. The advantage of this new algorithm, e.g., over a traditional adaptive beamformer algorithms is that it allows a very stable noise reduction by averaging over a time window. In addition, a new projection technique for separating sources and reducing cross-talk between different fascicle signals is proposed. The algorithms are tested on a computer model of realistic nerve geometry and time series signals.

Effect on signal-to-noise ratio of splitting the continuous contacts of cuff electrodes into smaller recording areas

“…A discrete (or “dot”) configuration, however, would potentially yield more information than continuous rings by decomposing the average of the field in a number of subsectors, as ideally illustrated in Figure 1. This idea has been exploited with different multi-contact cuff electrode designs, together with algorithms for extracting the fascicular source signals [21-26]. These have employed configurations with only dots [21-23], or a mixed of dot and ring contacts [24-26], where the impact on the contact type has not been addressed.…”

“…Spatial filtering or beamforming algorithms have been used to extract neural information with considerable crosstalk from this multi-contact electrode [25,26]. The contacts selection, however, has not been motivated.…”

The future of upper extremity rehabilitation robotics: research and practice

“…Second, ENG signal recording faces the same problems of crosstalk as surface EMG: recording neural activity from a population of fascicles instead of just a single fascicle. To address this, algorithms have been proposed to localize and separate ENG activity . More recently, these methods have been tested in long‐term animal studies and show promise in separating and recovering nerve fascicle signal sources reliably .…”

An inversion method based on random sampling for real-time MEG neuroimaging

“…The inverse problem was solved both by the least squares method and by the beamforming method introduced in [16]. The numerical solution takes few hundredths of seconds on a laptop.…”