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

Taylor, J.; Metcalfe, Benjamin; Clarke, Chris; Chew, Daniel; Nielsen, Thomas Nørgaard; Donaldson, Nick

doi:10.1109/isvlsi.2015.34

Cited by 6 publications

(3 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Thus, the ability to discriminate between neural signals from different groups of fibres is limited. To improve the performance of recording using cuffs, Taylor et al [ 15 ] proposed the velocity selective recording (VSR) technique, which combines spatial and temporal information to convert the time-domain signal into the velocity domain [ 16 ]. The VSR technique allows the discrimination of the signal based on the properties of its fibre-type populations (i.e., conduction velocity) and the direction of propagation (i.e., afferent or efferent).…”

Section: Introductionmentioning

confidence: 99%

The Use of the Velocity Selective Recording Technique to Reveal the Excitation Properties of the Ulnar Nerve in Pigs

Andreis

Metcalfe

Janjua

et al. 2021

Sensors

Self Cite

View full text Add to dashboard Cite

Decoding information from the peripheral nervous system via implantable neural interfaces remains a significant challenge, considerably limiting the advancement of neuromodulation and neuroprosthetic devices. The velocity selective recording (VSR) technique has been proposed to improve the classification of neural traffic by combining temporal and spatial information through a multi-electrode cuff (MEC). Therefore, this study investigates the feasibility of using the VSR technique to characterise fibre type based on the electrically evoked compound action potentials (eCAP) propagating along the ulnar nerve of pigs in vivo. A range of electrical stimulation parameters (amplitudes of 50 μA–10 mA and pulse durations of 100 μs, 500 μs, 1000 μs, and 5000 μs) was applied on a cutaneous and a motor branch of the ulnar nerve in nine Danish landrace pigs. Recordings were made with a 14 ring MEC and a delay-and-add algorithm was used to convert the eCAPs into the velocity domain. The results revealed two fibre populations propagating along the cutaneous branch of the ulnar nerve, with mean velocities of 55 m/s and 21 m/s, while only one dominant fibre population was found for the motor branch, with a mean velocity of 63 m/s. Because of its simplicity to provide information on the fibre selectivity and direction of propagation of nerve fibres, VSR can be implemented to advance the performance of the bidirectional control of neural prostheses and bioelectronic medicine applications.

show abstract

Section: Introductionmentioning

confidence: 99%

The Use of the Velocity Selective Recording Technique to Reveal the Excitation Properties of the Ulnar Nerve in Pigs

Andreis

Metcalfe

Janjua

et al. 2021

Sensors

Self Cite

View full text Add to dashboard Cite

show abstract

“…The Spatio-Temporal Warping (STW) approach, introduced by Silveira et al [9] before the LDA classifier, allowed to discriminate six proprioceptive stimuli with accuracy between 93.4 and 98%. On the other hand, other studies focused on the Velocity Selective Recording (VSR) to exploits the temporal information within ENG signals [19,20], like the conduction velocity and the direction of propagation of the electrical activity inside the nerve. Moreover, it is possible to infer the firing rate of active neural fibers within the nerve by detecting the compound action potentials (CAPs), i.e.…”

Section: Related Workmentioning

confidence: 99%

Classification of Sensory Neural Signals through Deep Learning Methods

Vasta,

Coviello,

Spagnolini

et al. 2023

IEEE EUROCON 2023 - 20th International Conference on Smart Technologies

View full text Add to dashboard Cite

The recording and analysis of peripheral neural signals can be beneficial to provide feedback to prosthetic limbs and recover the sensory functionality in people with nerve injuries. Nevertheless, the interpretation of sensory recordings extracted from the nerve is not trivial, and only few studies have applied classifiers on sequences of neural signals without previous feature extraction. This paper evaluates the classification performance of two deep learning (DL) models (CNN and ConvLSTM) applied to the electroneurographic (ENG) activity recorded from the sciatic nerve of rats. The ENG signals, available from two public datasets, were recorded using multi-channel cuff electrodes in response to four sensory inputs (plantarflexion, dorsiflexion, nociception, and touch) elicited in response to mechanical stimulation applied to the hind paw of the rats. Different temporal lengths of the signals were considered (2.5 s, 1 s, 500 ms, 200 ms, and 100 ms). Both the two DL models proved to correctly discriminate sensory stimuli without the need of hand-engineering feature extraction. Moreover, ConvLSTM outperformed state-of-the-art results in classifying sensory ENG activity (more than 90% F1-score for sequences greater than 500 ms), and it showed promising results for real-time application scenarios.

show abstract

“…The output signal is the intrinsic velocity spectrum (IVS). For a complete description of the delay-and-add process, see [14].…”

Section: ) Delay-and-addmentioning

confidence: 99%

A Comparison of Delay-and-Add and Maximum Likelihood Estimation for Velocity-Selective Recording Using Multi-Electrode Cuffs

Andreis

Metcalfe

Janjua

et al. 2022

2022 44th Annual International Conference of the IEEE Engineering in Medicine &Amp; Biology Society (EMBC)

Self Cite

View full text Add to dashboard Cite

Extracting information from the peripheral nervous system with implantable devices remains a significant challenge that limits the advancement of closed-loop neural prostheses. Linear electrode arrays can record neural signals with both temporal and spatial selectivity, and velocity selective recording using the delay-and-add algorithm can enable classification based on fibre type. The maximum likelihood estimation method also measures velocity and is frequently used in electromyography but has never been applied to electroneurography. Therefore, this study compares the two algorithms using in-vivo recordings of electrically evoked compound action potentials from the ulnar nerve of a pig. The performance of these algorithms was assessed using the velocity quality factor (Q-factor), computational time and the influence of the number of channels. The results show that the performance of both algorithms is significantly influenced by the number of channels in the recording array, with accuracies ranging from 77% with only two channels to 98% for 11 channels. Both algorithms were comparable in accuracy and Qfactor for all channels, with the delay-and-add having a slight advantage in the Q-factor.

show abstract

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

Cited by 6 publications

References 10 publications

The Use of the Velocity Selective Recording Technique to Reveal the Excitation Properties of the Ulnar Nerve in Pigs

The Use of the Velocity Selective Recording Technique to Reveal the Excitation Properties of the Ulnar Nerve in Pigs

Classification of Sensory Neural Signals through Deep Learning Methods

A Comparison of Delay-and-Add and Maximum Likelihood Estimation for Velocity-Selective Recording Using Multi-Electrode Cuffs

Contact Info

Product

Resources

About